├── Detector ├── CITATION.cff ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── best.pt ├── custom.yaml ├── docker │ ├── Dockerfile │ ├── Dockerfile-arm64 │ ├── Dockerfile-cpu │ ├── Dockerfile-jetson │ └── Dockerfile-python ├── docs │ ├── CNAME │ ├── README.md │ ├── SECURITY.md │ ├── assets │ │ └── favicon.ico │ ├── build_reference.py │ ├── datasets │ │ ├── classify │ │ │ ├── caltech101.md │ │ │ ├── caltech256.md │ │ │ ├── cifar10.md │ │ │ ├── cifar100.md │ │ │ ├── fashion-mnist.md │ │ │ ├── imagenet.md │ │ │ ├── imagenet10.md │ │ │ ├── imagenette.md │ │ │ ├── imagewoof.md │ │ │ ├── index.md │ │ │ └── mnist.md │ │ ├── detect │ │ │ ├── argoverse.md │ │ │ ├── coco.md │ │ │ ├── coco8.md │ │ │ ├── globalwheat2020.md │ │ │ ├── index.md │ │ │ ├── objects365.md │ │ │ ├── open-images-v7.md │ │ │ ├── sku-110k.md │ │ │ ├── visdrone.md │ │ │ ├── voc.md │ │ │ └── xview.md │ │ ├── index.md │ │ ├── pose │ │ │ ├── coco.md │ │ │ ├── coco8-pose.md │ │ │ └── index.md │ │ ├── segment │ │ │ ├── coco.md │ │ │ ├── coco8-seg.md │ │ │ └── index.md │ │ └── track │ │ │ └── index.md │ ├── guides │ │ ├── index.md │ │ └── kfold-cross-validation.md │ ├── help │ │ ├── CI.md │ │ ├── CLA.md │ │ ├── FAQ.md │ │ ├── code_of_conduct.md │ │ ├── contributing.md │ │ ├── environmental-health-safety.md │ │ ├── index.md │ │ └── minimum_reproducible_example.md │ ├── hub │ │ ├── app │ │ │ ├── android.md │ │ │ ├── index.md │ │ │ └── ios.md │ │ ├── datasets.md │ │ ├── index.md │ │ ├── inference_api.md │ │ ├── integrations.md │ │ ├── models.md │ │ ├── projects.md │ │ └── quickstart.md │ ├── index.md │ ├── integrations │ │ ├── index.md │ │ ├── openvino.md │ │ └── ray-tune.md │ ├── models │ │ ├── fast-sam.md │ │ ├── index.md │ │ ├── mobile-sam.md │ │ ├── rtdetr.md │ │ ├── sam.md │ │ ├── yolo-nas.md │ │ ├── yolov3.md │ │ ├── yolov4.md │ │ ├── yolov5.md │ │ ├── yolov6.md │ │ ├── yolov7.md │ │ └── yolov8.md │ ├── modes │ │ ├── benchmark.md │ │ ├── export.md │ │ ├── index.md │ │ ├── predict.md │ │ ├── track.md │ │ ├── train.md │ │ └── val.md │ ├── overrides │ │ └── partials │ │ │ ├── comments.html │ │ │ └── source-file.html │ ├── quickstart.md │ ├── reference │ │ ├── cfg │ │ │ └── __init__.md │ │ ├── data │ │ │ ├── annotator.md │ │ │ ├── augment.md │ │ │ ├── base.md │ │ │ ├── build.md │ │ │ ├── converter.md │ │ │ ├── dataset.md │ │ │ ├── loaders.md │ │ │ └── utils.md │ │ ├── engine │ │ │ ├── exporter.md │ │ │ ├── model.md │ │ │ ├── predictor.md │ │ │ ├── results.md │ │ │ ├── trainer.md │ │ │ └── validator.md │ │ ├── hub │ │ │ ├── __init__.md │ │ │ ├── auth.md │ │ │ ├── session.md │ │ │ └── utils.md │ │ ├── models │ │ │ ├── fastsam │ │ │ │ ├── model.md │ │ │ │ ├── predict.md │ │ │ │ ├── prompt.md │ │ │ │ ├── utils.md │ │ │ │ └── val.md │ │ │ ├── nas │ │ │ │ ├── model.md │ │ │ │ ├── predict.md │ │ │ │ └── val.md │ │ │ ├── rtdetr │ │ │ │ ├── model.md │ │ │ │ ├── predict.md │ │ │ │ ├── train.md │ │ │ │ └── val.md │ │ │ ├── sam │ │ │ │ ├── amg.md │ │ │ │ ├── build.md │ │ │ │ ├── model.md │ │ │ │ ├── modules │ │ │ │ │ ├── decoders.md │ │ │ │ │ ├── encoders.md │ │ │ │ │ ├── sam.md │ │ │ │ │ ├── tiny_encoder.md │ │ │ │ │ └── transformer.md │ │ │ │ └── predict.md │ │ │ ├── utils │ │ │ │ ├── loss.md │ │ │ │ └── ops.md │ │ │ └── yolo │ │ │ │ ├── classify │ │ │ │ ├── predict.md │ │ │ │ ├── train.md │ │ │ │ └── val.md │ │ │ │ ├── detect │ │ │ │ ├── predict.md │ │ │ │ ├── train.md │ │ │ │ └── val.md │ │ │ │ ├── model.md │ │ │ │ ├── pose │ │ │ │ ├── predict.md │ │ │ │ ├── train.md │ │ │ │ └── val.md │ │ │ │ └── segment │ │ │ │ ├── predict.md │ │ │ │ ├── train.md │ │ │ │ └── val.md │ │ ├── nn │ │ │ ├── autobackend.md │ │ │ ├── modules │ │ │ │ ├── block.md │ │ │ │ ├── conv.md │ │ │ │ ├── head.md │ │ │ │ ├── transformer.md │ │ │ │ └── utils.md │ │ │ └── tasks.md │ │ ├── trackers │ │ │ ├── basetrack.md │ │ │ ├── bot_sort.md │ │ │ ├── byte_tracker.md │ │ │ ├── track.md │ │ │ └── utils │ │ │ │ ├── gmc.md │ │ │ │ ├── kalman_filter.md │ │ │ │ └── matching.md │ │ └── utils │ │ │ ├── __init__.md │ │ │ ├── autobatch.md │ │ │ ├── benchmarks.md │ │ │ ├── callbacks │ │ │ ├── base.md │ │ │ ├── clearml.md │ │ │ ├── comet.md │ │ │ ├── dvc.md │ │ │ ├── hub.md │ │ │ ├── mlflow.md │ │ │ ├── neptune.md │ │ │ ├── raytune.md │ │ │ ├── tensorboard.md │ │ │ └── wb.md │ │ │ ├── checks.md │ │ │ ├── dist.md │ │ │ ├── downloads.md │ │ │ ├── errors.md │ │ │ ├── files.md │ │ │ ├── instance.md │ │ │ ├── loss.md │ │ │ ├── metrics.md │ │ │ ├── ops.md │ │ │ ├── patches.md │ │ │ ├── plotting.md │ │ │ ├── tal.md │ │ │ ├── torch_utils.md │ │ │ └── tuner.md │ ├── robots.txt │ ├── stylesheets │ │ └── style.css │ ├── tasks │ │ ├── classify.md │ │ ├── detect.md │ │ ├── index.md │ │ ├── pose.md │ │ └── segment.md │ ├── usage │ │ ├── callbacks.md │ │ ├── cfg.md │ │ ├── cli.md │ │ ├── engine.md │ │ └── python.md │ └── yolov5 │ │ ├── environments │ │ ├── aws_quickstart_tutorial.md │ │ ├── docker_image_quickstart_tutorial.md │ │ └── google_cloud_quickstart_tutorial.md │ │ ├── index.md │ │ ├── quickstart_tutorial.md │ │ └── tutorials │ │ ├── architecture_description.md │ │ ├── clearml_logging_integration.md │ │ ├── comet_logging_integration.md │ │ ├── hyperparameter_evolution.md │ │ ├── model_ensembling.md │ │ ├── model_export.md │ │ ├── model_pruning_and_sparsity.md │ │ ├── multi_gpu_training.md │ │ ├── neural_magic_pruning_quantization.md │ │ ├── pytorch_hub_model_loading.md │ │ ├── roboflow_datasets_integration.md │ │ ├── running_on_jetson_nano.md │ │ ├── test_time_augmentation.md │ │ ├── tips_for_best_training_results.md │ │ ├── train_custom_data.md │ │ └── transfer_learning_with_frozen_layers.md ├── examples │ ├── README.md │ ├── YOLOv8-CPP-Inference │ │ ├── CMakeLists.txt │ │ ├── README.md │ │ ├── inference.cpp │ │ ├── inference.h │ │ └── main.cpp │ ├── YOLOv8-ONNXRuntime-CPP │ │ ├── README.md │ │ ├── inference.cpp │ │ ├── inference.h │ │ └── main.cpp │ ├── YOLOv8-ONNXRuntime │ │ ├── README.md │ │ └── main.py │ ├── YOLOv8-OpenCV-ONNX-Python │ │ ├── README.md │ │ └── main.py │ ├── hub.ipynb │ └── tutorial.ipynb ├── mkdocs.yml ├── requirements.txt ├── setup.cfg ├── setup.py ├── tests │ ├── conftest.py │ ├── test_cli.py │ ├── test_engine.py │ └── test_python.py ├── ultralytics │ ├── __init__.py │ ├── assets │ │ ├── bus.jpg │ │ └── zidane.jpg │ ├── cfg │ │ ├── __init__.py │ │ ├── datasets │ │ │ ├── Argoverse.yaml │ │ │ ├── GlobalWheat2020.yaml │ │ │ ├── ImageNet.yaml │ │ │ ├── Objects365.yaml │ │ │ ├── SKU-110K.yaml │ │ │ ├── VOC.yaml │ │ │ ├── VisDrone.yaml │ │ │ ├── coco-pose.yaml │ │ │ ├── coco.yaml │ │ │ ├── coco128-seg.yaml │ │ │ ├── coco128.yaml │ │ │ ├── coco8-pose.yaml │ │ │ ├── coco8-seg.yaml │ │ │ ├── coco8.yaml │ │ │ ├── open-images-v7.yaml │ │ │ └── xView.yaml │ │ ├── default.yaml │ │ ├── models │ │ │ ├── README.md │ │ │ ├── rt-detr │ │ │ │ ├── rtdetr-l.yaml │ │ │ │ └── rtdetr-x.yaml │ │ │ ├── v3 │ │ │ │ ├── yolov3-spp.yaml │ │ │ │ ├── yolov3-tiny.yaml │ │ │ │ └── yolov3.yaml │ │ │ ├── v5 │ │ │ │ ├── yolov5-p6.yaml │ │ │ │ └── yolov5.yaml │ │ │ ├── v6 │ │ │ │ └── yolov6.yaml │ │ │ └── v8 │ │ │ │ ├── yolov8-cls.yaml │ │ │ │ ├── yolov8-p2.yaml │ │ │ │ ├── yolov8-p6.yaml │ │ │ │ ├── yolov8-pose-p6.yaml │ │ │ │ ├── yolov8-pose.yaml │ │ │ │ ├── yolov8-rtdetr.yaml │ │ │ │ ├── yolov8-seg.yaml │ │ │ │ └── yolov8.yaml │ │ └── trackers │ │ │ ├── botsort.yaml │ │ │ └── bytetrack.yaml │ ├── data │ │ ├── __init__.py │ │ ├── annotator.py │ │ ├── augment.py │ │ ├── base.py │ │ ├── build.py │ │ ├── converter.py │ │ ├── dataloaders │ │ │ └── __init__.py │ │ ├── dataset.py │ │ ├── loaders.py │ │ ├── scripts │ │ │ ├── download_weights.sh │ │ │ ├── get_coco.sh │ │ │ ├── get_coco128.sh │ │ │ └── get_imagenet.sh │ │ └── utils.py │ ├── engine │ │ ├── __init__.py │ │ ├── exporter.py │ │ ├── model.py │ │ ├── predictor.py │ │ ├── results.py │ │ ├── trainer.py │ │ └── validator.py │ ├── hub │ │ ├── __init__.py │ │ ├── auth.py │ │ ├── session.py │ │ └── utils.py │ ├── models │ │ ├── __init__.py │ │ ├── fastsam │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ ├── predict.py │ │ │ ├── prompt.py │ │ │ ├── utils.py │ │ │ └── val.py │ │ ├── nas │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ ├── predict.py │ │ │ └── val.py │ │ ├── rtdetr │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ ├── predict.py │ │ │ ├── train.py │ │ │ └── val.py │ │ ├── sam │ │ │ ├── __init__.py │ │ │ ├── amg.py │ │ │ ├── build.py │ │ │ ├── model.py │ │ │ ├── modules │ │ │ │ ├── __init__.py │ │ │ │ ├── decoders.py │ │ │ │ ├── encoders.py │ │ │ │ ├── sam.py │ │ │ │ ├── tiny_encoder.py │ │ │ │ └── transformer.py │ │ │ └── predict.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── loss.py │ │ │ └── ops.py │ │ └── yolo │ │ │ ├── __init__.py │ │ │ ├── classify │ │ │ ├── __init__.py │ │ │ ├── predict.py │ │ │ ├── train.py │ │ │ └── val.py │ │ │ ├── detect │ │ │ ├── __init__.py │ │ │ ├── predict.py │ │ │ ├── train.py │ │ │ └── val.py │ │ │ ├── model.py │ │ │ ├── pose │ │ │ ├── __init__.py │ │ │ ├── predict.py │ │ │ ├── train.py │ │ │ └── val.py │ │ │ └── segment │ │ │ ├── __init__.py │ │ │ ├── predict.py │ │ │ ├── train.py │ │ │ └── val.py │ ├── nn │ │ ├── __init__.py │ │ ├── autobackend.py │ │ ├── modules │ │ │ ├── __init__.py │ │ │ ├── block.py │ │ │ ├── conv.py │ │ │ ├── head.py │ │ │ ├── transformer.py │ │ │ └── utils.py │ │ └── tasks.py │ ├── trackers │ │ ├── README.md │ │ ├── __init__.py │ │ ├── basetrack.py │ │ ├── bot_sort.py │ │ ├── byte_tracker.py │ │ ├── track.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── gmc.py │ │ │ ├── kalman_filter.py │ │ │ └── matching.py │ ├── utils │ │ ├── __init__.py │ │ ├── autobatch.py │ │ ├── benchmarks.py │ │ ├── callbacks │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── clearml.py │ │ │ ├── comet.py │ │ │ ├── dvc.py │ │ │ ├── hub.py │ │ │ ├── mlflow.py │ │ │ ├── neptune.py │ │ │ ├── raytune.py │ │ │ ├── tensorboard.py │ │ │ └── wb.py │ │ ├── checks.py │ │ ├── dist.py │ │ ├── downloads.py │ │ ├── errors.py │ │ ├── files.py │ │ ├── instance.py │ │ ├── loss.py │ │ ├── metrics.py │ │ ├── ops.py │ │ ├── patches.py │ │ ├── plotting.py │ │ ├── tal.py │ │ ├── torch_utils.py │ │ └── tuner.py │ └── yolo │ │ ├── __init__.py │ │ ├── cfg │ │ └── __init__.py │ │ ├── data │ │ └── __init__.py │ │ ├── engine │ │ └── __init__.py │ │ ├── utils │ │ └── __init__.py │ │ └── v8 │ │ └── __init__.py └── yolov8n.pt ├── README.md ├── cldm ├── cldm.py ├── ddim_hacked.py ├── hack.py ├── logger.py └── model.py ├── create_mask_overlapped_images.py ├── dataset_test_load.py ├── dataset_train_load.py ├── figures ├── .gitkeep ├── controlnet_diag1.svg ├── intro.png └── intro2_controlnet.svg ├── ldm ├── data │ ├── __init__.py │ └── util.py ├── models │ ├── autoencoder.py │ └── diffusion │ │ ├── __init__.py │ │ ├── ddim.py │ │ ├── ddpm.py │ │ ├── dpm_solver │ │ ├── __init__.py │ │ ├── dpm_solver.py │ │ └── sampler.py │ │ ├── plms.py │ │ └── sampling_util.py ├── modules │ ├── attention.py │ ├── diffusionmodules │ │ ├── __init__.py │ │ ├── model.py │ │ ├── openaimodel.py │ │ ├── upscaling.py │ │ └── util.py │ ├── distributions │ │ ├── __init__.py │ │ └── distributions.py │ ├── ema.py │ ├── encoders │ │ ├── __init__.py │ │ └── modules.py │ └── image_degradation │ │ ├── __init__.py │ │ ├── bsrgan.py │ │ ├── bsrgan_light.py │ │ └── utils_image.py └── util.py ├── mask_to_bb.py ├── models └── cldm_v15.yaml ├── requirements.txt ├── sample_test.json ├── sample_train.json ├── tool_add_control.py └── train.py /Detector/CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 1.2.0 2 | preferred-citation: 3 | type: software 4 | message: If you use this software, please cite it as below. 5 | authors: 6 | - family-names: Jocher 7 | given-names: Glenn 8 | orcid: "https://orcid.org/0000-0001-5950-6979" 9 | - family-names: Chaurasia 10 | given-names: Ayush 11 | orcid: "https://orcid.org/0000-0002-7603-6750" 12 | - family-names: Qiu 13 | given-names: Jing 14 | orcid: "https://orcid.org/0000-0003-3783-7069" 15 | title: "YOLO by Ultralytics" 16 | version: 8.0.0 17 | # doi: 10.5281/zenodo.3908559 # TODO 18 | date-released: 2023-1-10 19 | license: AGPL-3.0 20 | url: "https://github.com/ultralytics/ultralytics" 21 | -------------------------------------------------------------------------------- /Detector/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include *.md 2 | include requirements.txt 3 | include LICENSE 4 | include setup.py 5 | include ultralytics/assets/bus.jpg 6 | include ultralytics/assets/zidane.jpg 7 | recursive-include ultralytics *.yaml 8 | recursive-exclude __pycache__ * 9 | -------------------------------------------------------------------------------- /Detector/best.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/best.pt -------------------------------------------------------------------------------- /Detector/custom.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics 3 | # Example usage: python train.py --data coco128.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco128 ← downloads here (7 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: /Detector/train_data/ # dataset root dir 12 | train: /Detector/train_data/train # train images (relative to 'path') 13 | val: /Detector/train_data/val # val images (relative to 'path') 14 | test: /Detector/train_data/test # test images (optional) 15 | 16 | 17 | # Classes 18 | nc: 1 # number of classes 19 | names: ['polyp'] # class names 20 | -------------------------------------------------------------------------------- /Detector/docker/Dockerfile-arm64: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics 3 | # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi 4 | 5 | # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu 6 | FROM arm64v8/ubuntu:22.10 7 | 8 | # Downloads to user config dir 9 | ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ 10 | 11 | # Install linux packages 12 | # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package 13 | RUN apt update \ 14 | && apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 15 | # RUN alias python=python3 16 | 17 | # Create working directory 18 | WORKDIR /usr/src/ultralytics 19 | 20 | # Copy contents 21 | # COPY . /usr/src/app (issues as not a .git directory) 22 | RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics 23 | ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/ 24 | 25 | # Install pip packages 26 | RUN python3 -m pip install --upgrade pip wheel 27 | RUN pip install --no-cache -e . thop 28 | 29 | 30 | # Usage Examples ------------------------------------------------------------------------------------------------------- 31 | 32 | # Build and Push 33 | # t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t 34 | 35 | # Run 36 | # t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t 37 | 38 | # Pull and Run with local volume mounted 39 | # t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t 40 | -------------------------------------------------------------------------------- /Detector/docs/CNAME: -------------------------------------------------------------------------------- 1 | docs.ultralytics.com 2 | -------------------------------------------------------------------------------- /Detector/docs/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Detector/docs/SECURITY.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover how Ultralytics ensures the safety of user data and systems. Check out the measures we have implemented, including Snyk and GitHub CodeQL Scanning. 3 | keywords: Ultralytics, Security Policy, data security, open-source projects, Snyk scanning, CodeQL scanning, vulnerability detection, threat prevention 4 | --- 5 | 6 | # Security Policy 7 | 8 | At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities. 9 | 10 | ## Snyk Scanning 11 | 12 | We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan all Ultralytics repositories for vulnerabilities and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any risks to our users. 13 | 14 | [![ultralytics](https://snyk.io/advisor/python/ultralytics/badge.svg)](https://snyk.io/advisor/python/ultralytics) 15 | 16 | ## GitHub CodeQL Scanning 17 | 18 | In addition to our Snyk scans, we also use GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scans to proactively identify and address security vulnerabilities across all Ultralytics repositories. 19 | 20 | [![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml) 21 | 22 | ## Reporting Security Issues 23 | 24 | If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible. 25 | 26 | We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone. 27 | -------------------------------------------------------------------------------- /Detector/docs/assets/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/docs/assets/favicon.ico -------------------------------------------------------------------------------- /Detector/docs/datasets/track/index.md: -------------------------------------------------------------------------------- 1 | --- 2 | comments: true 3 | description: Understand multi-object tracking datasets, upcoming features and how to use them with YOLO in Python and CLI. Dive in now!. 4 | keywords: Ultralytics, YOLO, multi-object tracking, datasets, detection, segmentation, pose models, Python, CLI 5 | --- 6 | 7 | # Multi-object Tracking Datasets Overview 8 | 9 | ## Dataset Format (Coming Soon) 10 | 11 | Multi-Object Detector doesn't need standalone training and directly supports pre-trained detection, segmentation or Pose models. 12 | Support for training trackers alone is coming soon 13 | 14 | ## Usage 15 | 16 | !!! example "" 17 | 18 | === "Python" 19 | 20 | ```python 21 | from ultralytics import YOLO 22 | 23 | model = YOLO('yolov8n.pt') 24 | results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) 25 | ``` 26 | === "CLI" 27 | 28 | ```bash 29 | yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show 30 | ``` 31 | -------------------------------------------------------------------------------- /Detector/docs/guides/index.md: -------------------------------------------------------------------------------- 1 | --- 2 | comments: true 3 | description: In-depth exploration of Ultralytics' YOLO. Learn about the YOLO object detection model, how to train it on custom data, multi-GPU training, exporting, predicting, deploying, and more. 4 | keywords: Ultralytics, YOLO, Deep Learning, Object detection, PyTorch, Tutorial, Multi-GPU training, Custom data training 5 | --- 6 | 7 | # Comprehensive Tutorials to Ultralytics YOLO 8 | 9 | Welcome to the Ultralytics' YOLO 🚀 Guides! Our comprehensive tutorials cover various aspects of the YOLO object detection model, ranging from training and prediction to deployment. Built on PyTorch, YOLO stands out for its exceptional speed and accuracy in real-time object detection tasks. 10 | 11 | Whether you're a beginner or an expert in deep learning, our tutorials offer valuable insights into the implementation and optimization of YOLO for your computer vision projects. Let's dive in! 12 | 13 | ## Guides 14 | 15 | Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO. 16 | 17 | * [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique. 18 | 19 | Note: More guides about training, exporting, predicting, and deploying with Ultralytics YOLO are coming soon. Stay tuned! 20 | -------------------------------------------------------------------------------- /Detector/docs/hub/integrations.md: -------------------------------------------------------------------------------- 1 | --- 2 | comments: true 3 | --- 4 | 5 | # 🚧 Page Under Construction ⚒ 6 | 7 | This page is currently under construction!️ 👷Please check back later for updates. 😃🔜 8 | -------------------------------------------------------------------------------- /Detector/docs/hub/quickstart.md: -------------------------------------------------------------------------------- 1 | --- 2 | comments: true 3 | --- 4 | 5 | # 🚧 Page Under Construction ⚒ 6 | 7 | This page is currently under construction!️ 👷Please check back later for updates. 😃🔜 8 | -------------------------------------------------------------------------------- /Detector/docs/overrides/partials/comments.html: -------------------------------------------------------------------------------- 1 | {% if page.meta.comments %} 2 |

{{ lang.t("meta.comments") }}

3 | 4 | 5 | 20 | 21 | 22 | 50 | {% endif %} 51 | -------------------------------------------------------------------------------- /Detector/docs/overrides/partials/source-file.html: -------------------------------------------------------------------------------- 1 | {% import "partials/language.html" as lang with context %} 2 | 3 | 5 | 6 |
7 |
8 | 9 | 10 | 11 | {% if page.meta.git_revision_date_localized %} 12 | 📅 {{ lang.t("source.file.date.updated") }}: 13 | {{ page.meta.git_revision_date_localized }} 14 | {% if page.meta.git_creation_date_localized %} 15 |
16 | 🎂 {{ lang.t("source.file.date.created") }}: 17 | {{ page.meta.git_creation_date_localized }} 18 | {% endif %} 19 | 20 | 21 | {% elif page.meta.revision_date %} 22 | 📅 {{ lang.t("source.file.date.updated") }}: 23 | {{ page.meta.revision_date }} 24 | {% endif %} 25 | 26 |
27 | -------------------------------------------------------------------------------- /Detector/docs/reference/cfg/__init__.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics cfg functions like cfg2dict, handle_deprecation, merge_equal_args & more to handle YOLO settings and configurations efficiently. 3 | keywords: Ultralytics, YOLO, Configuration, cfg2dict, handle_deprecation, merge_equals_args, handle_yolo_settings, copy_default_cfg, Image Detection 4 | --- 5 | 6 | # Reference for `ultralytics/cfg/__init__.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.cfg.cfg2dict 14 |

15 | 16 | --- 17 | ## ::: ultralytics.cfg.get_cfg 18 |

19 | 20 | --- 21 | ## ::: ultralytics.cfg._handle_deprecation 22 |

23 | 24 | --- 25 | ## ::: ultralytics.cfg.check_dict_alignment 26 |

27 | 28 | --- 29 | ## ::: ultralytics.cfg.merge_equals_args 30 |

31 | 32 | --- 33 | ## ::: ultralytics.cfg.handle_yolo_hub 34 |

35 | 36 | --- 37 | ## ::: ultralytics.cfg.handle_yolo_settings 38 |

39 | 40 | --- 41 | ## ::: ultralytics.cfg.parse_key_value_pair 42 |

43 | 44 | --- 45 | ## ::: ultralytics.cfg.smart_value 46 |

47 | 48 | --- 49 | ## ::: ultralytics.cfg.entrypoint 50 |

51 | 52 | --- 53 | ## ::: ultralytics.cfg.copy_default_cfg 54 |

55 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/annotator.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Enhance your machine learning model with Ultralytics’ auto_annotate function. Simplify data annotation for improved model training. 3 | keywords: Ultralytics, Auto-Annotate, Machine Learning, AI, Annotation, Data Processing, Model Training 4 | --- 5 | 6 | # Reference for `ultralytics/data/annotator.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.annotator.auto_annotate 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/base.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore BaseDataset in Ultralytics docs. Learn how this implementation simplifies dataset creation and manipulation. 3 | keywords: Ultralytics, docs, BaseDataset, data manipulation, dataset creation 4 | --- 5 | 6 | # Reference for `ultralytics/data/base.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.base.BaseDataset 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/build.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the Ultralytics YOLO v3 data build procedures, including the InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source. 3 | keywords: Ultralytics, YOLO v3, Data build, DataLoader, InfiniteDataLoader, seed_worker, build_dataloader, load_inference_source 4 | --- 5 | 6 | # Reference for `ultralytics/data/build.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.build.InfiniteDataLoader 14 |

15 | 16 | --- 17 | ## ::: ultralytics.data.build._RepeatSampler 18 |

19 | 20 | --- 21 | ## ::: ultralytics.data.build.seed_worker 22 |

23 | 24 | --- 25 | ## ::: ultralytics.data.build.build_yolo_dataset 26 |

27 | 28 | --- 29 | ## ::: ultralytics.data.build.build_dataloader 30 |

31 | 32 | --- 33 | ## ::: ultralytics.data.build.check_source 34 |

35 | 36 | --- 37 | ## ::: ultralytics.data.build.load_inference_source 38 |

39 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/converter.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics data converter functions like coco91_to_coco80_class, merge_multi_segment, rle2polygon for efficient data handling. 3 | keywords: Ultralytics, Data Converter, coco91_to_coco80_class, merge_multi_segment, rle2polygon 4 | --- 5 | 6 | # Reference for `ultralytics/data/converter.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.converter.coco91_to_coco80_class 14 |

15 | 16 | --- 17 | ## ::: ultralytics.data.converter.convert_coco 18 |

19 | 20 | --- 21 | ## ::: ultralytics.data.converter.rle2polygon 22 |

23 | 24 | --- 25 | ## ::: ultralytics.data.converter.min_index 26 |

27 | 28 | --- 29 | ## ::: ultralytics.data.converter.merge_multi_segment 30 |

31 | 32 | --- 33 | ## ::: ultralytics.data.converter.delete_dsstore 34 |

35 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/dataset.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the YOLODataset and SemanticDataset classes in YOLO data. Learn how to efficiently handle and manipulate your data with Ultralytics. 3 | keywords: Ultralytics, YOLO, YOLODataset, SemanticDataset, data handling, data manipulation 4 | --- 5 | 6 | # Reference for `ultralytics/data/dataset.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.dataset.YOLODataset 14 |

15 | 16 | --- 17 | ## ::: ultralytics.data.dataset.ClassificationDataset 18 |

19 | 20 | --- 21 | ## ::: ultralytics.data.dataset.SemanticDataset 22 |

23 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/loaders.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Find detailed guides on Ultralytics YOLO data loaders, including LoadStreams, LoadImages and LoadTensor. Learn how to get the best YouTube URLs. 3 | keywords: Ultralytics, data loaders, LoadStreams, LoadImages, LoadTensor, YOLO, YouTube URLs 4 | --- 5 | 6 | # Reference for `ultralytics/data/loaders.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/loaders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/loaders.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.loaders.SourceTypes 14 |

15 | 16 | --- 17 | ## ::: ultralytics.data.loaders.LoadStreams 18 |

19 | 20 | --- 21 | ## ::: ultralytics.data.loaders.LoadScreenshots 22 |

23 | 24 | --- 25 | ## ::: ultralytics.data.loaders.LoadImages 26 |

27 | 28 | --- 29 | ## ::: ultralytics.data.loaders.LoadPilAndNumpy 30 |

31 | 32 | --- 33 | ## ::: ultralytics.data.loaders.LoadTensor 34 |

35 | 36 | --- 37 | ## ::: ultralytics.data.loaders.autocast_list 38 |

39 | 40 | --- 41 | ## ::: ultralytics.data.loaders.get_best_youtube_url 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/data/utils.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Uncover a detailed guide to Ultralytics data utilities. Learn functions from img2label_paths to autosplit, all boosting your YOLO model’s efficiency. 3 | keywords: Ultralytics, data utils, YOLO, img2label_paths, exif_size, polygon2mask, polygons2masks_overlap, check_cls_dataset, delete_dsstore, autosplit 4 | --- 5 | 6 | # Reference for `ultralytics/data/utils.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.data.utils.HUBDatasetStats 14 |

15 | 16 | --- 17 | ## ::: ultralytics.data.utils.img2label_paths 18 |

19 | 20 | --- 21 | ## ::: ultralytics.data.utils.get_hash 22 |

23 | 24 | --- 25 | ## ::: ultralytics.data.utils.exif_size 26 |

27 | 28 | --- 29 | ## ::: ultralytics.data.utils.verify_image_label 30 |

31 | 32 | --- 33 | ## ::: ultralytics.data.utils.polygon2mask 34 |

35 | 36 | --- 37 | ## ::: ultralytics.data.utils.polygons2masks 38 |

39 | 40 | --- 41 | ## ::: ultralytics.data.utils.polygons2masks_overlap 42 |

43 | 44 | --- 45 | ## ::: ultralytics.data.utils.check_det_dataset 46 |

47 | 48 | --- 49 | ## ::: ultralytics.data.utils.check_cls_dataset 50 |

51 | 52 | --- 53 | ## ::: ultralytics.data.utils.compress_one_image 54 |

55 | 56 | --- 57 | ## ::: ultralytics.data.utils.delete_dsstore 58 |

59 | 60 | --- 61 | ## ::: ultralytics.data.utils.zip_directory 62 |

63 | 64 | --- 65 | ## ::: ultralytics.data.utils.autosplit 66 |

67 | -------------------------------------------------------------------------------- /Detector/docs/reference/engine/exporter.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, iOSDetectModel, and try exporting with examples. 3 | keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export 4 | --- 5 | 6 | # Reference for `ultralytics/engine/exporter.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/exporter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/exporter.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.engine.exporter.Exporter 14 |

15 | 16 | --- 17 | ## ::: ultralytics.engine.exporter.iOSDetectModel 18 |

19 | 20 | --- 21 | ## ::: ultralytics.engine.exporter.export_formats 22 |

23 | 24 | --- 25 | ## ::: ultralytics.engine.exporter.gd_outputs 26 |

27 | 28 | --- 29 | ## ::: ultralytics.engine.exporter.try_export 30 |

31 | 32 | --- 33 | ## ::: ultralytics.engine.exporter.export 34 |

35 | -------------------------------------------------------------------------------- /Detector/docs/reference/engine/model.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the detailed guide on using the Ultralytics YOLO Engine Model. Learn better ways to implement, train and evaluate YOLO models. 3 | keywords: Ultralytics, YOLO, engine model, documentation, guide, implementation, training, evaluation 4 | --- 5 | 6 | # Reference for `ultralytics/engine/model.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.engine.model.Model 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/engine/predictor.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about Ultralytics BasePredictor, an essential component of our engine that serves as the foundation for all prediction operations. 3 | keywords: Ultralytics, BasePredictor, YOLO, prediction, engine 4 | --- 5 | 6 | # Reference for `ultralytics/engine/predictor.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/predictor.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/predictor.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.engine.predictor.BasePredictor 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/engine/results.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Master Ultralytics engine results including base tensors, boxes, and keypoints with our thorough documentation. 3 | keywords: Ultralytics, engine, results, base tensor, boxes, keypoints 4 | --- 5 | 6 | # Reference for `ultralytics/engine/results.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/results.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/results.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.engine.results.BaseTensor 14 |

15 | 16 | --- 17 | ## ::: ultralytics.engine.results.Results 18 |

19 | 20 | --- 21 | ## ::: ultralytics.engine.results.Boxes 22 |

23 | 24 | --- 25 | ## ::: ultralytics.engine.results.Masks 26 |

27 | 28 | --- 29 | ## ::: ultralytics.engine.results.Keypoints 30 |

31 | 32 | --- 33 | ## ::: ultralytics.engine.results.Probs 34 |

35 | -------------------------------------------------------------------------------- /Detector/docs/reference/engine/trainer.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about the BaseTrainer class in the Ultralytics library. From training control, customization to advanced usage. 3 | keywords: Ultralytics, BaseTrainer, Machine Learning, Training Control, Python library 4 | --- 5 | 6 | # Reference for `ultralytics/engine/trainer.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/trainer.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/trainer.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.engine.trainer.BaseTrainer 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/engine/validator.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about the Ultralytics BaseValidator module. Understand its principles, uses, and how it interacts with other components. 3 | keywords: Ultralytics, BaseValidator, Ultralytics engine, module, components 4 | --- 5 | 6 | # Reference for `ultralytics/engine/validator.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/validator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/validator.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.engine.validator.BaseValidator 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/hub/__init__.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics hub functions for model resetting, checking datasets, model exporting and more. Easy-to-follow instructions provided. 3 | keywords: Ultralytics, hub functions, model export, dataset check, reset model, YOLO Docs 4 | --- 5 | 6 | # Reference for `ultralytics/hub/__init__.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/__init__.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/__init__.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.hub.login 14 |

15 | 16 | --- 17 | ## ::: ultralytics.hub.logout 18 |

19 | 20 | --- 21 | ## ::: ultralytics.hub.start 22 |

23 | 24 | --- 25 | ## ::: ultralytics.hub.reset_model 26 |

27 | 28 | --- 29 | ## ::: ultralytics.hub.export_fmts_hub 30 |

31 | 32 | --- 33 | ## ::: ultralytics.hub.export_model 34 |

35 | 36 | --- 37 | ## ::: ultralytics.hub.get_export 38 |

39 | 40 | --- 41 | ## ::: ultralytics.hub.check_dataset 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/hub/auth.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Dive into the Ultralytics Auth API documentation & learn how to manage authentication in your AI & ML projects easily and effectively. 3 | keywords: Ultralytics, Auth, API documentation, User Authentication, AI, Machine Learning 4 | --- 5 | 6 | # Reference for `ultralytics/hub/auth.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/auth.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/auth.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.hub.auth.Auth 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/hub/session.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore details about the HUBTrainingSession in Ultralytics framework. Learn to utilize this functionality for effective model training. 3 | keywords: Ultralytics, HUBTrainingSession, Documentation, Model Training, AI, Machine Learning, YOLO 4 | --- 5 | 6 | # Reference for `ultralytics/hub/session.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/session.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/session.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.hub.session.HUBTrainingSession 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/hub/utils.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics docs for various Events, including "request_with_credentials" and "requests_with_progress". Also, understand the use of the "smart_request". 3 | keywords: Ultralytics, Events, request_with_credentials, smart_request, Ultralytics hub utils, requests_with_progress 4 | --- 5 | 6 | # Reference for `ultralytics/hub/utils.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.hub.utils.Events 14 |

15 | 16 | --- 17 | ## ::: ultralytics.hub.utils.request_with_credentials 18 |

19 | 20 | --- 21 | ## ::: ultralytics.hub.utils.requests_with_progress 22 |

23 | 24 | --- 25 | ## ::: ultralytics.hub.utils.smart_request 26 |

27 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/fastsam/model.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn all about Ultralytics FastSAM model. Dive into our comprehensive guide for seamless integration and efficient model training. 3 | keywords: Ultralytics, FastSAM model, Model documentation, Efficient model training 4 | --- 5 | 6 | # Reference for `ultralytics/models/fastsam/model.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.fastsam.model.FastSAM 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/fastsam/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Get detailed insights about Ultralytics FastSAMPredictor. Learn to predict and optimize your AI models with our properly documented guidelines. 3 | keywords: Ultralytics, FastSAMPredictor, predictive modeling, AI optimization, machine learning, deep learning, Ultralytics documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/fastsam/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.fastsam.predict.FastSAMPredictor 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/fastsam/prompt.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn to effectively utilize FastSAMPrompt model from Ultralytics. Detailed guide to help you get the most out of your machine learning models. 3 | keywords: Ultralytics, FastSAMPrompt, machine learning, model, guide, documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/fastsam/prompt.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/prompt.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/prompt.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.fastsam.prompt.FastSAMPrompt 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/fastsam/utils.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn how to adjust bounding boxes to image borders in Ultralytics models using the bbox_iou utility. Enhance your object detection performance. 3 | keywords: Ultralytics, bounding boxes, Bboxes, image borders, object detection, bbox_iou, model utilities 4 | --- 5 | 6 | # Reference for `ultralytics/models/fastsam/utils.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.fastsam.utils.bbox_iou 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/fastsam/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about FastSAMValidator in Ultralytics models. Comprehensive guide to enhancing AI capabilities with Ultralytics. 3 | keywords: Ultralytics, FastSAMValidator, model, synthetic, AI, machine learning, validation 4 | --- 5 | 6 | # Reference for `ultralytics/models/fastsam/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.fastsam.val.FastSAMValidator 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/nas/model.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn how our NAS model operates in Ultralytics. Comprehensive guide with detailed examples. Master the nuances of Ultralytics NAS model. 3 | keywords: Ultralytics, NAS model, NAS guide, machine learning, model documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/nas/model.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.nas.model.NAS 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/nas/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics NASPredictor. Understand high-level architecture of the model for effective implementation and efficient predictions. 3 | keywords: NASPredictor, Ultralytics, Ultralytics model, model architecture, efficient predictions 4 | --- 5 | 6 | # Reference for `ultralytics/models/nas/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.nas.predict.NASPredictor 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/nas/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the utilities and functions of the Ultralytics NASValidator. Find out how it benefits allocation and optimization in AI models. 3 | keywords: Ultralytics, NASValidator, models.nas.val.NASValidator, AI models, allocation, optimization 4 | --- 5 | 6 | # Reference for `ultralytics/models/nas/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.nas.val.NASValidator 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/rtdetr/model.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the specifics of using the RTDETR model in Ultralytics. Detailed documentation layered with explanations and examples. 3 | keywords: Ultralytics, RTDETR model, Ultralytics models, object detection, Ultralytics documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/rtdetr/model.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.rtdetr.model.RTDETR 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/rtdetr/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn how to use the RTDETRPredictor model of the Ultralytics package. Detailed documentation, usage instructions, and advice. 3 | keywords: Ultralytics, RTDETRPredictor, model documentation, guide, real-time object detection 4 | --- 5 | 6 | # Reference for `ultralytics/models/rtdetr/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.rtdetr.predict.RTDETRPredictor 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/rtdetr/train.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Get insights into RTDETRTrainer, a crucial component of Ultralytics for effective model training. Explore detailed documentation at Ultralytics. 3 | keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorch models, neural networks, machine learning, deep learning 4 | --- 5 | 6 | # Reference for `ultralytics/models/rtdetr/train.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/train.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.rtdetr.train.RTDETRTrainer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.rtdetr.train.train 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/rtdetr/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore RTDETRDataset in Ultralytics Models. Learn about the RTDETRValidator function, understand its usage in real-time object detection. 3 | keywords: Ultralytics, RTDETRDataset, RTDETRValidator, real-time object detection, models documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/rtdetr/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.rtdetr.val.RTDETRDataset 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.rtdetr.val.RTDETRValidator 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/amg.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics methods for mask data processing, transformation and encoding. Deepen your understanding of RLE encoding, image cropping and more. 3 | keywords: Ultralytics, Mask Data, Transformation, Encoding, RLE encoding, Image cropping, Pytorch, SAM, AMG, Ultralytics model 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/amg.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/amg.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/amg.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.amg.MaskData 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.sam.amg.is_box_near_crop_edge 18 |

19 | 20 | --- 21 | ## ::: ultralytics.models.sam.amg.box_xyxy_to_xywh 22 |

23 | 24 | --- 25 | ## ::: ultralytics.models.sam.amg.batch_iterator 26 |

27 | 28 | --- 29 | ## ::: ultralytics.models.sam.amg.mask_to_rle_pytorch 30 |

31 | 32 | --- 33 | ## ::: ultralytics.models.sam.amg.rle_to_mask 34 |

35 | 36 | --- 37 | ## ::: ultralytics.models.sam.amg.area_from_rle 38 |

39 | 40 | --- 41 | ## ::: ultralytics.models.sam.amg.calculate_stability_score 42 |

43 | 44 | --- 45 | ## ::: ultralytics.models.sam.amg.build_point_grid 46 |

47 | 48 | --- 49 | ## ::: ultralytics.models.sam.amg.build_all_layer_point_grids 50 |

51 | 52 | --- 53 | ## ::: ultralytics.models.sam.amg.generate_crop_boxes 54 |

55 | 56 | --- 57 | ## ::: ultralytics.models.sam.amg.uncrop_boxes_xyxy 58 |

59 | 60 | --- 61 | ## ::: ultralytics.models.sam.amg.uncrop_points 62 |

63 | 64 | --- 65 | ## ::: ultralytics.models.sam.amg.uncrop_masks 66 |

67 | 68 | --- 69 | ## ::: ultralytics.models.sam.amg.remove_small_regions 70 |

71 | 72 | --- 73 | ## ::: ultralytics.models.sam.amg.coco_encode_rle 74 |

75 | 76 | --- 77 | ## ::: ultralytics.models.sam.amg.batched_mask_to_box 78 |

79 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/build.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Master building SAM ViT models with Ultralytics. Discover steps to leverage the power of SAM and Vision Transformer sessions. 3 | keywords: Ultralytics, SAM, build sam, vision transformer, vits, build_sam_vit_l, build_sam_vit_b, build_sam 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/build.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/build.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/build.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.build.build_sam_vit_h 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.sam.build.build_sam_vit_l 18 |

19 | 20 | --- 21 | ## ::: ultralytics.models.sam.build.build_sam_vit_b 22 |

23 | 24 | --- 25 | ## ::: ultralytics.models.sam.build.build_mobile_sam 26 |

27 | 28 | --- 29 | ## ::: ultralytics.models.sam.build._build_sam 30 |

31 | 32 | --- 33 | ## ::: ultralytics.models.sam.build.build_sam 34 |

35 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/model.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Dive into the SAM model details in the Ultralytics YOLO documentation. Understand, implement, and optimize your model use. 3 | keywords: Ultralytics, YOLO, SAM Model, Documentations, Machine Learning, AI, Convolutional neural network 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/model.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.model.SAM 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/modules/decoders.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore MaskDecoder, a part of the Ultralytics models. Gain insights on how to utilize it effectively in the SAM modules decoders MLP. 3 | keywords: Ultralytics, MaskDecoder, SAM modules, decoders, MLP, YOLO, machine learning, image recognition 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/modules/decoders.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/decoders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/decoders.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.modules.decoders.MaskDecoder 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.sam.modules.decoders.MLP 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/modules/encoders.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover detailed information on ImageEncoderViT, PositionEmbeddingRandom, Attention, window_partition, get_rel_pos and more in Ultralytics models encoders documentation. 3 | keywords: Ultralytics, Encoders, Modules, Documentation, ImageEncoderViT, PositionEmbeddingRandom, Attention, window_partition, get_rel_pos 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/modules/encoders.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/encoders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/encoders.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.modules.encoders.ImageEncoderViT 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.sam.modules.encoders.PromptEncoder 18 |

19 | 20 | --- 21 | ## ::: ultralytics.models.sam.modules.encoders.PositionEmbeddingRandom 22 |

23 | 24 | --- 25 | ## ::: ultralytics.models.sam.modules.encoders.Block 26 |

27 | 28 | --- 29 | ## ::: ultralytics.models.sam.modules.encoders.Attention 30 |

31 | 32 | --- 33 | ## ::: ultralytics.models.sam.modules.encoders.PatchEmbed 34 |

35 | 36 | --- 37 | ## ::: ultralytics.models.sam.modules.encoders.window_partition 38 |

39 | 40 | --- 41 | ## ::: ultralytics.models.sam.modules.encoders.window_unpartition 42 |

43 | 44 | --- 45 | ## ::: ultralytics.models.sam.modules.encoders.get_rel_pos 46 |

47 | 48 | --- 49 | ## ::: ultralytics.models.sam.modules.encoders.add_decomposed_rel_pos 50 |

51 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/modules/sam.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the Sam module of Ultralytics. Discover detailed methods, classes, and information for efficient deep-learning model training!. 3 | keywords: Ultralytics, Sam module, deep learning, model training, Ultralytics documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/modules/sam.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/sam.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/sam.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.modules.sam.Sam 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/modules/tiny_encoder.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Get in-depth insights about Ultralytics Tiny Encoder Modules such as Conv2d_BN, MBConv, ConvLayer, Attention, BasicLayer, and TinyViT. Improve your understanding of machine learning model components. 3 | keywords: Ultralytics, Tiny Encoder, Conv2d_BN, MBConv, ConvLayer, Attention, BasicLayer, TinyViT, Machine learning modules, Ultralytics models 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/modules/tiny_encoder.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/tiny_encoder.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/tiny_encoder.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.modules.tiny_encoder.Conv2d_BN 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.sam.modules.tiny_encoder.PatchEmbed 18 |

19 | 20 | --- 21 | ## ::: ultralytics.models.sam.modules.tiny_encoder.MBConv 22 |

23 | 24 | --- 25 | ## ::: ultralytics.models.sam.modules.tiny_encoder.PatchMerging 26 |

27 | 28 | --- 29 | ## ::: ultralytics.models.sam.modules.tiny_encoder.ConvLayer 30 |

31 | 32 | --- 33 | ## ::: ultralytics.models.sam.modules.tiny_encoder.Mlp 34 |

35 | 36 | --- 37 | ## ::: ultralytics.models.sam.modules.tiny_encoder.Attention 38 |

39 | 40 | --- 41 | ## ::: ultralytics.models.sam.modules.tiny_encoder.TinyViTBlock 42 |

43 | 44 | --- 45 | ## ::: ultralytics.models.sam.modules.tiny_encoder.BasicLayer 46 |

47 | 48 | --- 49 | ## ::: ultralytics.models.sam.modules.tiny_encoder.LayerNorm2d 50 |

51 | 52 | --- 53 | ## ::: ultralytics.models.sam.modules.tiny_encoder.TinyViT 54 |

55 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/modules/transformer.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about TwoWayTransformer and Attention modules in Ultralytics. Leverage these tools to enhance your AI models. 3 | keywords: Ultralytics, TwoWayTransformer, Attention, AI models, transformers 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/modules/transformer.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/transformer.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/transformer.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.modules.transformer.TwoWayTransformer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.sam.modules.transformer.TwoWayAttentionBlock 18 |

19 | 20 | --- 21 | ## ::: ultralytics.models.sam.modules.transformer.Attention 22 |

23 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/sam/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Master the ultralytics.models.sam.predict.Predictor class with our comprehensive guide. Discover techniques to enhance your model predictions. 3 | keywords: Ultralytics, predictor, models, sam.predict.Predictor, AI, machine learning, predictive models 4 | --- 5 | 6 | # Reference for `ultralytics/models/sam/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.sam.predict.Predictor 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/utils/loss.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn to use the DETRLoss function provided by Ultralytics YOLO. Understand how to utilize loss in RTDETR detection models to improve accuracy. 3 | keywords: Ultralytics, YOLO, Documentation, DETRLoss, Detection Loss, Loss function, DETR, RTDETR Detection Models 4 | --- 5 | 6 | # Reference for `ultralytics/models/utils/loss.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/utils/loss.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/utils/loss.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.utils.loss.DETRLoss 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.utils.loss.RTDETRDetectionLoss 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/utils/ops.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover details for "HungarianMatcher" & "inverse_sigmoid" functions in Ultralytics YOLO, advanced tools supporting detection models. 3 | keywords: Ultralytics, YOLO, HungarianMatcher, inverse_sigmoid, detection models, model utilities, ops 4 | --- 5 | 6 | # Reference for `ultralytics/models/utils/ops.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/utils/ops.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/utils/ops.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.utils.ops.HungarianMatcher 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.utils.ops.get_cdn_group 18 |

19 | 20 | --- 21 | ## ::: ultralytics.models.utils.ops.inverse_sigmoid 22 |

23 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/classify/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the Ultralytics ClassificationPredictor guide for model prediction and visualization. Build powerful AI models with YOLO. 3 | keywords: Ultralytics, classification predictor, predict, YOLO, AI models, model visualization 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/classify/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.classify.predict.ClassificationPredictor 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.classify.predict.predict 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/classify/train.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Delve into Classification Trainer at Ultralytics YOLO docs and optimize your model's training process with insights from the masters!. 3 | keywords: Ultralytics, YOLO, Classification Trainer, deep learning, training process, AI models, documentation 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/classify/train.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/train.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.classify.train.ClassificationTrainer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.classify.train.train 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/classify/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore YOLO ClassificationValidator, a key element of Ultralytics YOLO models. Learn how it validates and fine-tunes model outputs. 3 | keywords: Ultralytics, YOLO, ClassificationValidator, model validation, model fine-tuning, deep learning, computer vision 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/classify/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.classify.val.ClassificationValidator 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.classify.val.val 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/detect/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the guide to using the DetectionPredictor in Ultralytics YOLO. Learn how to predict, detect and analyze objects accurately. 3 | keywords: Ultralytics, YOLO, DetectionPredictor, detect, predict, object detection, analysis 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/detect/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/detect/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/detect/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.detect.predict.DetectionPredictor 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.detect.predict.predict 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/detect/train.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Maximize your model's potential with Ultralytics YOLO Detection Trainer. Learn advanced techniques, tips, and tricks for training. 3 | keywords: Ultralytics YOLO, YOLO, Detection Trainer, Model Training, Machine Learning, Deep Learning, Computer Vision 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/detect/train.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/detect/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/detect/train.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.detect.train.DetectionTrainer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.detect.train.train 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/detect/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover function valuation of your YOLO models with the Ultralytics Detection Validator. Enhance precision and recall rates today. 3 | keywords: Ultralytics, YOLO, Detection Validator, model valuation, precision, recall 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/detect/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/detect/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/detect/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.detect.val.DetectionValidator 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.detect.val.val 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/model.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover the Ultralytics YOLO model class. Learn advanced techniques, tips, and tricks for training. 3 | keywords: Ultralytics YOLO, YOLO, YOLO model, Model Training, Machine Learning, Deep Learning, Computer Vision 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/model.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.model.YOLO 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/pose/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover how to use PosePredictor in the Ultralytics YOLO model. Includes detailed guides, code examples, and explanations. 3 | keywords: Ultralytics, YOLO, PosePredictor, machine learning, AI, predictive models 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/pose/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.pose.predict.PosePredictor 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.pose.predict.predict 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/pose/train.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics PoseTrainer for YOLO models. Get a step-by-step guide on how to train on custom pose data for more accurate AI modeling. 3 | keywords: Ultralytics, YOLO, PoseTrainer, pose training, AI modeling, custom data training 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/pose/train.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/train.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.pose.train.PoseTrainer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.pose.train.train 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/pose/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the PoseValidator—review how Ultralytics YOLO validates poses for object detection. Improve your understanding of YOLO. 3 | keywords: PoseValidator, Ultralytics, YOLO, Object detection, Pose validation 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/pose/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.pose.val.PoseValidator 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.pose.val.val 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/segment/predict.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover how to utilize the YOLO Segmentation Predictor in Ultralytics. Enhance your objects detection skills with us. 3 | keywords: YOLO, Ultralytics, object detection, segmentation predictor 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/segment/predict.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.segment.predict.SegmentationPredictor 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.segment.predict.predict 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/segment/train.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Maximize your YOLO model's performance with our SegmentationTrainer. Explore comprehensive guides and tutorials on ultralytics.com. 3 | keywords: Ultralytics, YOLO, SegmentationTrainer, image segmentation, object detection, model training, YOLO model 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/segment/train.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/train.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.segment.train.SegmentationTrainer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.segment.train.train 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/models/yolo/segment/val.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Get practical insights about our SegmentationValidator in YOLO Ultralytics models. Discover functionality details, methods, inputs, and outputs. 3 | keywords: Ultralytics, YOLO, SegmentationValidator, model segmentation, image classification, object detection 4 | --- 5 | 6 | # Reference for `ultralytics/models/yolo/segment/val.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.models.yolo.segment.val.SegmentationValidator 14 |

15 | 16 | --- 17 | ## ::: ultralytics.models.yolo.segment.val.val 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/autobackend.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Get to know more about Ultralytics nn.autobackend.check_class_names functionality. Optimize your YOLO models seamlessly. 3 | keywords: Ultralytics, AutoBackend, check_class_names, YOLO, YOLO models, optimization 4 | --- 5 | 6 | # Reference for `ultralytics/nn/autobackend.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/autobackend.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/autobackend.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.nn.autobackend.AutoBackend 14 |

15 | 16 | --- 17 | ## ::: ultralytics.nn.autobackend.check_class_names 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/modules/block.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics YOLO neural network modules, Proto to BottleneckCSP. Detailed explanation of each module with easy-to-follow code examples. 3 | keywords: YOLO, Ultralytics, neural network, nn.modules.block, Proto, HGBlock, SPPF, C2, C3, RepC3, C3Ghost, Bottleneck, BottleneckCSP 4 | --- 5 | 6 | # Reference for `ultralytics/nn/modules/block.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/block.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/block.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.nn.modules.block.DFL 14 |

15 | 16 | --- 17 | ## ::: ultralytics.nn.modules.block.Proto 18 |

19 | 20 | --- 21 | ## ::: ultralytics.nn.modules.block.HGStem 22 |

23 | 24 | --- 25 | ## ::: ultralytics.nn.modules.block.HGBlock 26 |

27 | 28 | --- 29 | ## ::: ultralytics.nn.modules.block.SPP 30 |

31 | 32 | --- 33 | ## ::: ultralytics.nn.modules.block.SPPF 34 |

35 | 36 | --- 37 | ## ::: ultralytics.nn.modules.block.C1 38 |

39 | 40 | --- 41 | ## ::: ultralytics.nn.modules.block.C2 42 |

43 | 44 | --- 45 | ## ::: ultralytics.nn.modules.block.C2f 46 |

47 | 48 | --- 49 | ## ::: ultralytics.nn.modules.block.C3 50 |

51 | 52 | --- 53 | ## ::: ultralytics.nn.modules.block.C3x 54 |

55 | 56 | --- 57 | ## ::: ultralytics.nn.modules.block.RepC3 58 |

59 | 60 | --- 61 | ## ::: ultralytics.nn.modules.block.C3TR 62 |

63 | 64 | --- 65 | ## ::: ultralytics.nn.modules.block.C3Ghost 66 |

67 | 68 | --- 69 | ## ::: ultralytics.nn.modules.block.GhostBottleneck 70 |

71 | 72 | --- 73 | ## ::: ultralytics.nn.modules.block.Bottleneck 74 |

75 | 76 | --- 77 | ## ::: ultralytics.nn.modules.block.BottleneckCSP 78 |

79 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/modules/conv.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore various Ultralytics convolution modules including Conv2, DWConv, ConvTranspose, GhostConv, Channel Attention and more. 3 | keywords: Ultralytics, Convolution Modules, Conv2, DWConv, ConvTranspose, GhostConv, ChannelAttention, CBAM, autopad 4 | --- 5 | 6 | # Reference for `ultralytics/nn/modules/conv.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/conv.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/conv.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.nn.modules.conv.Conv 14 |

15 | 16 | --- 17 | ## ::: ultralytics.nn.modules.conv.Conv2 18 |

19 | 20 | --- 21 | ## ::: ultralytics.nn.modules.conv.LightConv 22 |

23 | 24 | --- 25 | ## ::: ultralytics.nn.modules.conv.DWConv 26 |

27 | 28 | --- 29 | ## ::: ultralytics.nn.modules.conv.DWConvTranspose2d 30 |

31 | 32 | --- 33 | ## ::: ultralytics.nn.modules.conv.ConvTranspose 34 |

35 | 36 | --- 37 | ## ::: ultralytics.nn.modules.conv.Focus 38 |

39 | 40 | --- 41 | ## ::: ultralytics.nn.modules.conv.GhostConv 42 |

43 | 44 | --- 45 | ## ::: ultralytics.nn.modules.conv.RepConv 46 |

47 | 48 | --- 49 | ## ::: ultralytics.nn.modules.conv.ChannelAttention 50 |

51 | 52 | --- 53 | ## ::: ultralytics.nn.modules.conv.SpatialAttention 54 |

55 | 56 | --- 57 | ## ::: ultralytics.nn.modules.conv.CBAM 58 |

59 | 60 | --- 61 | ## ::: ultralytics.nn.modules.conv.Concat 62 |

63 | 64 | --- 65 | ## ::: ultralytics.nn.modules.conv.autopad 66 |

67 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/modules/head.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore docs covering Ultralytics YOLO detection, pose & RTDETRDecoder. Comprehensive guides to help you understand Ultralytics nn modules. 3 | keywords: Ultralytics, YOLO, Detection, Pose, RTDETRDecoder, nn modules, guides 4 | --- 5 | 6 | # Reference for `ultralytics/nn/modules/head.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/head.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/head.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.nn.modules.head.Detect 14 |

15 | 16 | --- 17 | ## ::: ultralytics.nn.modules.head.Segment 18 |

19 | 20 | --- 21 | ## ::: ultralytics.nn.modules.head.Pose 22 |

23 | 24 | --- 25 | ## ::: ultralytics.nn.modules.head.Classify 26 |

27 | 28 | --- 29 | ## ::: ultralytics.nn.modules.head.RTDETRDecoder 30 |

31 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/modules/transformer.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about Ultralytics transformer encoder, layer, MLP block, LayerNorm2d and the deformable transformer decoder layer. Expand your understanding of these crucial AI modules. 3 | keywords: Ultralytics, Ultralytics documentation, TransformerEncoderLayer, TransformerLayer, MLPBlock, LayerNorm2d, DeformableTransformerDecoderLayer 4 | --- 5 | 6 | # Reference for `ultralytics/nn/modules/transformer.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/transformer.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/transformer.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.nn.modules.transformer.TransformerEncoderLayer 14 |

15 | 16 | --- 17 | ## ::: ultralytics.nn.modules.transformer.AIFI 18 |

19 | 20 | --- 21 | ## ::: ultralytics.nn.modules.transformer.TransformerLayer 22 |

23 | 24 | --- 25 | ## ::: ultralytics.nn.modules.transformer.TransformerBlock 26 |

27 | 28 | --- 29 | ## ::: ultralytics.nn.modules.transformer.MLPBlock 30 |

31 | 32 | --- 33 | ## ::: ultralytics.nn.modules.transformer.MLP 34 |

35 | 36 | --- 37 | ## ::: ultralytics.nn.modules.transformer.LayerNorm2d 38 |

39 | 40 | --- 41 | ## ::: ultralytics.nn.modules.transformer.MSDeformAttn 42 |

43 | 44 | --- 45 | ## ::: ultralytics.nn.modules.transformer.DeformableTransformerDecoderLayer 46 |

47 | 48 | --- 49 | ## ::: ultralytics.nn.modules.transformer.DeformableTransformerDecoder 50 |

51 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/modules/utils.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics neural network utils, such as bias_init_with_prob, inverse_sigmoid and multi_scale_deformable_attn_pytorch functions. 3 | keywords: Ultralytics, neural network, nn.modules.utils, bias_init_with_prob, inverse_sigmoid, multi_scale_deformable_attn_pytorch 4 | --- 5 | 6 | # Reference for `ultralytics/nn/modules/utils.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.nn.modules.utils._get_clones 14 |

15 | 16 | --- 17 | ## ::: ultralytics.nn.modules.utils.bias_init_with_prob 18 |

19 | 20 | --- 21 | ## ::: ultralytics.nn.modules.utils.linear_init_ 22 |

23 | 24 | --- 25 | ## ::: ultralytics.nn.modules.utils.inverse_sigmoid 26 |

27 | 28 | --- 29 | ## ::: ultralytics.nn.modules.utils.multi_scale_deformable_attn_pytorch 30 |

31 | -------------------------------------------------------------------------------- /Detector/docs/reference/nn/tasks.md: -------------------------------------------------------------------------------- 1 | # Reference for `ultralytics/nn/tasks.py` 2 | 3 | !!! note 4 | 5 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/tasks.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/tasks.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 6 | 7 | --- 8 | ## ::: ultralytics.nn.tasks.BaseModel 9 |

10 | 11 | --- 12 | ## ::: ultralytics.nn.tasks.DetectionModel 13 |

14 | 15 | --- 16 | ## ::: ultralytics.nn.tasks.SegmentationModel 17 |

18 | 19 | --- 20 | ## ::: ultralytics.nn.tasks.PoseModel 21 |

22 | 23 | --- 24 | ## ::: ultralytics.nn.tasks.ClassificationModel 25 |

26 | 27 | --- 28 | ## ::: ultralytics.nn.tasks.RTDETRDetectionModel 29 |

30 | 31 | --- 32 | ## ::: ultralytics.nn.tasks.Ensemble 33 |

34 | 35 | --- 36 | ## ::: ultralytics.nn.tasks.temporary_modules 37 |

38 | 39 | --- 40 | ## ::: ultralytics.nn.tasks.torch_safe_load 41 |

42 | 43 | --- 44 | ## ::: ultralytics.nn.tasks.attempt_load_weights 45 |

46 | 47 | --- 48 | ## ::: ultralytics.nn.tasks.attempt_load_one_weight 49 |

50 | 51 | --- 52 | ## ::: ultralytics.nn.tasks.parse_model 53 |

54 | 55 | --- 56 | ## ::: ultralytics.nn.tasks.yaml_model_load 57 |

58 | 59 | --- 60 | ## ::: ultralytics.nn.tasks.guess_model_scale 61 |

62 | 63 | --- 64 | ## ::: ultralytics.nn.tasks.guess_model_task 65 |

66 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/basetrack.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Get familiar with TrackState in Ultralytics. Learn how it is used in the BaseTrack of the Ultralytics tracker for enhanced functionality. 3 | keywords: Ultralytics, TrackState, BaseTrack, Ultralytics tracker, Ultralytics documentation 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/basetrack.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/basetrack.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/basetrack.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.basetrack.TrackState 14 |

15 | 16 | --- 17 | ## ::: ultralytics.trackers.basetrack.BaseTrack 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/bot_sort.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Master the use of Ultralytics BOTrack, a key component of the powerful Ultralytics tracking system. Learn to integrate and use BOTSORT in your projects. 3 | keywords: Ultralytics, BOTSORT, BOTrack, tracking system, official documentation, machine learning, AI tracking 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/bot_sort.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/bot_sort.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/bot_sort.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.bot_sort.BOTrack 14 |

15 | 16 | --- 17 | ## ::: ultralytics.trackers.bot_sort.BOTSORT 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/byte_tracker.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Step-in to explore in-depth the functionalities of Ultralytics BYTETracker under STrack. Gain advanced feature insights to streamline your operations. 3 | keywords: STrack, Ultralytics, BYTETracker, documentation, Ultralytics tracker, object tracking, YOLO 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/byte_tracker.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/byte_tracker.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/byte_tracker.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.byte_tracker.STrack 14 |

15 | 16 | --- 17 | ## ::: ultralytics.trackers.byte_tracker.BYTETracker 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/track.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics documentation on prediction function starters & register trackers. Understand our code & its applications better. 3 | keywords: Ultralytics, YOLO, on predict start, register tracker, prediction functions, documentation 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/track.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/track.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/track.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.track.on_predict_start 14 |

15 | 16 | --- 17 | ## ::: ultralytics.trackers.track.on_predict_postprocess_end 18 |

19 | 20 | --- 21 | ## ::: ultralytics.trackers.track.register_tracker 22 |

23 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/utils/gmc.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the Ultralytics GMC tool in our comprehensive documentation. Learn how it works, best practices, and implementation advice. 3 | keywords: Ultralytics, GMC utility, Ultralytics documentation, Ultralytics tracker, machine learning tools 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/utils/gmc.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/gmc.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/gmc.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.utils.gmc.GMC 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/utils/kalman_filter.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore KalmanFilterXYAH, a key component of Ultralytics trackers. Understand its utilities and learn to leverage it in your own projects. 3 | keywords: Ultralytics, KalmanFilterXYAH, tracker, documentation, guide 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/utils/kalman_filter.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/kalman_filter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/kalman_filter.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.utils.kalman_filter.KalmanFilterXYAH 14 |

15 | 16 | --- 17 | ## ::: ultralytics.trackers.utils.kalman_filter.KalmanFilterXYWH 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/trackers/utils/matching.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore in-depth guidance for using Ultralytics trackers utils matching, including merge_matches, linear_assignment, iou_distance, embedding_distance, fuse_motion, and fuse_score. 3 | keywords: Ultralytics, Trackers Utils, Matching, merge_matches, linear_assignment, iou_distance, embedding_distance, fuse_motion, fuse_score, documentation 4 | --- 5 | 6 | # Reference for `ultralytics/trackers/utils/matching.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/matching.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/matching.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.trackers.utils.matching.merge_matches 14 |

15 | 16 | --- 17 | ## ::: ultralytics.trackers.utils.matching._indices_to_matches 18 |

19 | 20 | --- 21 | ## ::: ultralytics.trackers.utils.matching.linear_assignment 22 |

23 | 24 | --- 25 | ## ::: ultralytics.trackers.utils.matching.ious 26 |

27 | 28 | --- 29 | ## ::: ultralytics.trackers.utils.matching.iou_distance 30 |

31 | 32 | --- 33 | ## ::: ultralytics.trackers.utils.matching.v_iou_distance 34 |

35 | 36 | --- 37 | ## ::: ultralytics.trackers.utils.matching.embedding_distance 38 |

39 | 40 | --- 41 | ## ::: ultralytics.trackers.utils.matching.gate_cost_matrix 42 |

43 | 44 | --- 45 | ## ::: ultralytics.trackers.utils.matching.fuse_motion 46 |

47 | 48 | --- 49 | ## ::: ultralytics.trackers.utils.matching.fuse_iou 50 |

51 | 52 | --- 53 | ## ::: ultralytics.trackers.utils.matching.fuse_score 54 |

55 | 56 | --- 57 | ## ::: ultralytics.trackers.utils.matching.bbox_ious 58 |

59 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/autobatch.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics documentation for check_train_batch_size utility in the autobatch module. Understand how it could improve your machine learning process. 3 | keywords: Ultralytics, check_train_batch_size, autobatch, utility, machine learning, documentation 4 | --- 5 | 6 | # Reference for `ultralytics/utils/autobatch.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/autobatch.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/autobatch.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.autobatch.check_train_batch_size 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.autobatch.autobatch 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/benchmarks.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover how to profile your models using Ultralytics utilities. Enhance performance, optimize your benchmarks, and learn best practices. 3 | keywords: Ultralytics, ProfileModels, benchmarks, model profiling, performance optimization 4 | --- 5 | 6 | # Reference for `ultralytics/utils/benchmarks.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/benchmarks.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/benchmarks.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.benchmarks.ProfileModels 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.benchmarks.benchmark 18 |

19 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/clearml.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Uncover the specifics of Ultralytics ClearML callbacks, from pretrain routine start to training end. Boost your ML model performance. 3 | keywords: Ultralytics, clearML, callbacks, pretrain routine start, validation end, train epoch end, training end 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/clearml.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/clearml.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/clearml.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.clearml._log_debug_samples 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.clearml._log_plot 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.clearml.on_pretrain_routine_start 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.callbacks.clearml.on_train_epoch_end 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.callbacks.clearml.on_fit_epoch_end 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.callbacks.clearml.on_val_end 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.callbacks.clearml.on_train_end 38 |

39 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/dvc.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Browse through Ultralytics YOLO docs to learn about important logging and callback functions used in training and pretraining models. 3 | keywords: Ultralytics, YOLO, callbacks, logger, training, pretraining, machine learning, models 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/dvc.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/dvc.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/dvc.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.dvc._logger_disabled 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.dvc._log_images 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.dvc._log_plots 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.callbacks.dvc._log_confusion_matrix 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.callbacks.dvc.on_pretrain_routine_start 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.callbacks.dvc.on_pretrain_routine_end 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.callbacks.dvc.on_train_start 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.callbacks.dvc.on_train_epoch_start 42 |

43 | 44 | --- 45 | ## ::: ultralytics.utils.callbacks.dvc.on_fit_epoch_end 46 |

47 | 48 | --- 49 | ## ::: ultralytics.utils.callbacks.dvc.on_train_end 50 |

51 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/hub.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the detailed information on key Ultralytics callbacks such as on_pretrain_routine_end, on_model_save, on_train_start, and on_predict_start. 3 | keywords: Ultralytics, callbacks, on_pretrain_routine_end, on_model_save, on_train_start, on_predict_start, hub, training 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/hub.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/hub.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/hub.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.hub.on_pretrain_routine_end 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.hub.on_fit_epoch_end 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.hub.on_model_save 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.callbacks.hub.on_train_end 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.callbacks.hub.on_train_start 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.callbacks.hub.on_val_start 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.callbacks.hub.on_predict_start 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.callbacks.hub.on_export_start 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/mlflow.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Understand routines at the end of pre-training and training in Ultralytics. Elevate your MLflow callbacks expertise. 3 | keywords: Ultralytics, MLflow, Callbacks, on_pretrain_routine_end, on_train_end, Machine Learning, Training 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/mlflow.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/mlflow.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/mlflow.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.mlflow.on_pretrain_routine_end 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.mlflow.on_fit_epoch_end 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.mlflow.on_train_end 22 |

23 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/neptune.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore exhaustive details about Ultralytics callbacks in Neptune, with specifics about scalar logging, routine start, and more. 3 | keywords: Ultralytics, Neptune callbacks, on_train_epoch_end, on_val_end, _log_plot, _log_images, on_pretrain_routine_start, on_fit_epoch_end, on_train_end 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/neptune.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/neptune.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/neptune.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.neptune._log_scalars 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.neptune._log_images 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.neptune._log_plot 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.callbacks.neptune.on_pretrain_routine_start 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.callbacks.neptune.on_train_epoch_end 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.callbacks.neptune.on_fit_epoch_end 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.callbacks.neptune.on_val_end 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.callbacks.neptune.on_train_end 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/raytune.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover the functionality of the on_fit_epoch_end callback in the Ultralytics YOLO framework. Learn how to end an epoch in your deep learning projects. 3 | keywords: Ultralytics, YOLO, on_fit_epoch_end, callbacks, documentation, deep learning, YOLO framework 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/raytune.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/raytune.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/raytune.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.raytune.on_fit_epoch_end 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/tensorboard.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics YOLO Docs for a deep understanding of log_scalars, on_batch_end & other callback utilities embedded in the tensorboard module. 3 | keywords: Ultralytics, YOLO, documentation, callback utilities, log_scalars, on_batch_end, tensorboard 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/tensorboard.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/tensorboard.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/tensorboard.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.tensorboard._log_scalars 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.tensorboard.on_pretrain_routine_start 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.tensorboard.on_batch_end 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.callbacks.tensorboard.on_fit_epoch_end 26 |

27 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/callbacks/wb.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Deep dive into Ultralytics callbacks. Learn how to use the _log_plots, on_fit_epoch_end, and on_train_end functions effectively. 3 | keywords: Ultralytics, callbacks, _log_plots, on_fit_epoch_end, on_train_end 4 | --- 5 | 6 | # Reference for `ultralytics/utils/callbacks/wb.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/wb.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/wb.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.callbacks.wb._log_plots 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.callbacks.wb.on_pretrain_routine_start 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.callbacks.wb.on_fit_epoch_end 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.callbacks.wb.on_train_epoch_end 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.callbacks.wb.on_train_end 30 |

31 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/checks.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about our routine checks that safeguard Ultralytics operations including ASCII, font, YOLO file, YAML, Python and torchvision checks. 3 | keywords: Ultralytics, utility checks, ASCII, check_version, pip_update, check_python, check_torchvision, check_yaml, YOLO filename 4 | --- 5 | 6 | # Reference for `ultralytics/utils/checks.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/checks.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/checks.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.checks.is_ascii 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.checks.check_imgsz 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.checks.check_version 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.checks.check_latest_pypi_version 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.checks.check_pip_update_available 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.checks.check_font 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.checks.check_python 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.checks.check_requirements 42 |

43 | 44 | --- 45 | ## ::: ultralytics.utils.checks.check_torchvision 46 |

47 | 48 | --- 49 | ## ::: ultralytics.utils.checks.check_suffix 50 |

51 | 52 | --- 53 | ## ::: ultralytics.utils.checks.check_yolov5u_filename 54 |

55 | 56 | --- 57 | ## ::: ultralytics.utils.checks.check_file 58 |

59 | 60 | --- 61 | ## ::: ultralytics.utils.checks.check_yaml 62 |

63 | 64 | --- 65 | ## ::: ultralytics.utils.checks.check_imshow 66 |

67 | 68 | --- 69 | ## ::: ultralytics.utils.checks.check_yolo 70 |

71 | 72 | --- 73 | ## ::: ultralytics.utils.checks.check_amp 74 |

75 | 76 | --- 77 | ## ::: ultralytics.utils.checks.git_describe 78 |

79 | 80 | --- 81 | ## ::: ultralytics.utils.checks.print_args 82 |

83 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/dist.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover the role of dist.find_free_network_port & dist.generate_ddp_command in Ultralytics DDP utilities. Use our guide for efficient deployment. 3 | keywords: Ultralytics, DDP, DDP utility functions, Distributed Data Processing, find free network port, generate DDP command 4 | --- 5 | 6 | # Reference for `ultralytics/utils/dist.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/dist.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/dist.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.dist.find_free_network_port 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.dist.generate_ddp_file 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.dist.generate_ddp_command 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.dist.ddp_cleanup 26 |

27 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/downloads.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about the download utilities in Ultralytics YOLO, featuring functions like is_url, check_disk_space, get_github_assets, and download. 3 | keywords: Ultralytics, YOLO, download utilities, is_url, check_disk_space, get_github_assets, download, documentation 4 | --- 5 | 6 | # Reference for `ultralytics/utils/downloads.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/downloads.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/downloads.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.downloads.is_url 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.downloads.zip_directory 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.downloads.unzip_file 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.downloads.check_disk_space 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.downloads.get_google_drive_file_info 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.downloads.safe_download 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.downloads.get_github_assets 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.downloads.attempt_download_asset 42 |

43 | 44 | --- 45 | ## ::: ultralytics.utils.downloads.download 46 |

47 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/errors.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about the HUBModelError in Ultralytics. Enhance your understanding, troubleshoot errors and optimize your machine learning projects. 3 | keywords: Ultralytics, HUBModelError, Machine Learning, Error troubleshooting, Ultralytics documentation 4 | --- 5 | 6 | # Reference for `ultralytics/utils/errors.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/errors.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/errors.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.errors.HUBModelError 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/files.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Discover how to use Ultralytics utility functions for file-related operations including incrementing paths, finding file age, checking file size and creating directories. 3 | keywords: Ultralytics, utility functions, file operations, working directory, file age, file size, create directories 4 | --- 5 | 6 | # Reference for `ultralytics/utils/files.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/files.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/files.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.files.WorkingDirectory 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.files.spaces_in_path 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.files.increment_path 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.files.file_age 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.files.file_date 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.files.file_size 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.files.get_latest_run 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.files.make_dirs 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/instance.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Dive into Ultralytics detailed utility guide. Learn about Bboxes, _ntuple and more from Ultralytics utils.instance module. 3 | keywords: Ultralytics, Bboxes, _ntuple, utility, ultralytics utils.instance 4 | --- 5 | 6 | # Reference for `ultralytics/utils/instance.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/instance.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/instance.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.instance.Bboxes 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.instance.Instances 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.instance._ntuple 22 |

23 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/loss.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics' versatile loss functions - VarifocalLoss, BboxLoss, v8DetectionLoss, v8PoseLoss. Improve your accuracy on YOLO implementations. 3 | keywords: Ultralytics, Loss functions, VarifocalLoss, BboxLoss, v8DetectionLoss, v8PoseLoss, YOLO, Ultralytics Documentation 4 | --- 5 | 6 | # Reference for `ultralytics/utils/loss.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/loss.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/loss.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.loss.VarifocalLoss 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.loss.FocalLoss 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.loss.BboxLoss 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.loss.KeypointLoss 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.loss.v8DetectionLoss 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.loss.v8SegmentationLoss 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.loss.v8PoseLoss 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.loss.v8ClassificationLoss 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/patches.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn about Ultralytics utils patches including imread, imshow and torch_save. Enhance your image processing skills. 3 | keywords: Ultralytics, Utils, Patches, imread, imshow, torch_save, image processing 4 | --- 5 | 6 | # Reference for `ultralytics/utils/patches.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/patches.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/patches.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.patches.imread 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.patches.imwrite 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.patches.imshow 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.patches.torch_save 26 |

27 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/plotting.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Master advanced plotting utils from Ultralytics including color annotations, label and image plotting, and feature visualization. 3 | keywords: Ultralytics, plotting, utils, color annotation, label plotting, image plotting, feature visualization 4 | --- 5 | 6 | # Reference for `ultralytics/utils/plotting.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/plotting.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/plotting.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.plotting.Colors 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.plotting.Annotator 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.plotting.plot_labels 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.plotting.save_one_box 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.plotting.plot_images 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.plotting.plot_results 34 |

35 | 36 | --- 37 | ## ::: ultralytics.utils.plotting.output_to_target 38 |

39 | 40 | --- 41 | ## ::: ultralytics.utils.plotting.feature_visualization 42 |

43 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/tal.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore Ultralytics utilities for optimized task assignment, bounding box creation, and distance calculation. Learn more about algorithm implementations. 3 | keywords: Ultralytics, task aligned assigner, select highest overlaps, make anchors, dist2bbox, bbox2dist, utilities, algorithm 4 | --- 5 | 6 | # Reference for `ultralytics/utils/tal.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tal.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tal.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.tal.TaskAlignedAssigner 14 |

15 | 16 | --- 17 | ## ::: ultralytics.utils.tal.select_candidates_in_gts 18 |

19 | 20 | --- 21 | ## ::: ultralytics.utils.tal.select_highest_overlaps 22 |

23 | 24 | --- 25 | ## ::: ultralytics.utils.tal.make_anchors 26 |

27 | 28 | --- 29 | ## ::: ultralytics.utils.tal.dist2bbox 30 |

31 | 32 | --- 33 | ## ::: ultralytics.utils.tal.bbox2dist 34 |

35 | -------------------------------------------------------------------------------- /Detector/docs/reference/utils/tuner.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Learn to utilize the run_ray_tune function with Ultralytics. Make your machine learning tuning process easier and more efficient. 3 | keywords: Ultralytics, run_ray_tune, machine learning tuning, machine learning efficiency 4 | --- 5 | 6 | # Reference for `ultralytics/utils/tuner.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tuner.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tuner.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏! 11 | 12 | --- 13 | ## ::: ultralytics.utils.tuner.run_ray_tune 14 |

15 | -------------------------------------------------------------------------------- /Detector/docs/robots.txt: -------------------------------------------------------------------------------- 1 | User-agent: * 2 | -------------------------------------------------------------------------------- /Detector/docs/stylesheets/style.css: -------------------------------------------------------------------------------- 1 | /* Table format like GitHub ----------------------------------------------------------------------------------------- */ 2 | th, td { 3 | border: 1px solid var(--md-typeset-table-color); 4 | border-spacing: 0; 5 | border-bottom: none; 6 | border-left: none; 7 | border-top: none; 8 | } 9 | 10 | .md-typeset__table { 11 | line-height: 1; 12 | } 13 | 14 | .md-typeset__table table:not([class]) { 15 | font-size: .74rem; 16 | border-right: none; 17 | } 18 | 19 | .md-typeset__table table:not([class]) td, 20 | .md-typeset__table table:not([class]) th { 21 | padding: 9px; 22 | } 23 | 24 | /* light mode alternating table bg colors */ 25 | .md-typeset__table tr:nth-child(2n) { 26 | background-color: #f8f8f8; 27 | } 28 | 29 | /* dark mode alternating table bg colors */ 30 | [data-md-color-scheme="slate"] .md-typeset__table tr:nth-child(2n) { 31 | background-color: hsla(var(--md-hue),25%,25%,1) 32 | } 33 | /* Table format like GitHub ----------------------------------------------------------------------------------------- */ 34 | 35 | /* Code block vertical scroll */ 36 | div.highlight { 37 | max-height: 20rem; 38 | overflow-y: auto; /* for adding a scrollbar when needed */ 39 | } 40 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-CPP-Inference/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.5) 2 | 3 | project(Yolov8CPPInference VERSION 0.1) 4 | 5 | set(CMAKE_INCLUDE_CURRENT_DIR ON) 6 | 7 | # CUDA 8 | set(CUDA_TOOLKIT_ROOT_DIR "/usr/local/cuda") 9 | find_package(CUDA 11 REQUIRED) 10 | 11 | set(CMAKE_CUDA_STANDARD 11) 12 | set(CMAKE_CUDA_STANDARD_REQUIRED ON) 13 | # !CUDA 14 | 15 | # OpenCV 16 | find_package(OpenCV REQUIRED) 17 | include_directories(${OpenCV_INCLUDE_DIRS}) 18 | # !OpenCV 19 | 20 | set(PROJECT_SOURCES 21 | main.cpp 22 | 23 | inference.h 24 | inference.cpp 25 | ) 26 | 27 | add_executable(Yolov8CPPInference ${PROJECT_SOURCES}) 28 | target_link_libraries(Yolov8CPPInference ${OpenCV_LIBS}) 29 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-CPP-Inference/README.md: -------------------------------------------------------------------------------- 1 | # YOLOv8/YOLOv5 Inference C++ 2 | 3 | This example demonstrates how to perform inference using YOLOv8 and YOLOv5 models in C++ with OpenCV's DNN API. 4 | 5 | ## Usage 6 | 7 | ```bash 8 | git clone ultralytics 9 | cd ultralytics 10 | pip install . 11 | cd examples/cpp_ 12 | 13 | # Add a **yolov8\_.onnx** and/or **yolov5\_.onnx** model(s) to the ultralytics folder. 14 | # Edit the **main.cpp** to change the **projectBasePath** to match your user. 15 | 16 | # Note that by default the CMake file will try and import the CUDA library to be used with the OpenCVs dnn (cuDNN) GPU Inference. 17 | # If your OpenCV build does not use CUDA/cuDNN you can remove that import call and run the example on CPU. 18 | 19 | mkdir build 20 | cd build 21 | cmake .. 22 | make 23 | ./Yolov8CPPInference 24 | ``` 25 | 26 | ## Exporting YOLOv8 and YOLOv5 Models 27 | 28 | To export YOLOv8 models: 29 | 30 | ```commandline 31 | yolo export model=yolov8s.pt imgsz=480,640 format=onnx opset=12 32 | ``` 33 | 34 | To export YOLOv5 models: 35 | 36 | ```commandline 37 | python3 export.py --weights yolov5s.pt --img 480 640 --include onnx --opset 12 38 | ``` 39 | 40 | yolov8s.onnx: 41 | 42 | ![image](https://user-images.githubusercontent.com/40023722/217356132-a4cecf2e-2729-4acb-b80a-6559022d7707.png) 43 | 44 | yolov5s.onnx: 45 | 46 | ![image](https://user-images.githubusercontent.com/40023722/217357005-07464492-d1da-42e3-98a7-fc753f87d5e6.png) 47 | 48 | This repository utilizes OpenCV's DNN API to run ONNX exported models of YOLOv5 and YOLOv8. In theory, it should work for YOLOv6 and YOLOv7 as well, but they have not been tested. Note that the example networks are exported with rectangular (640x480) resolutions, but any exported resolution will work. You may want to use the letterbox approach for square images, depending on your use case. 49 | 50 | The **main** branch version uses Qt as a GUI wrapper. The primary focus here is the **Inference** class file, which demonstrates how to transpose YOLOv8 models to work as YOLOv5 models. 51 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-ONNXRuntime-CPP/README.md: -------------------------------------------------------------------------------- 1 | # YOLOv8 OnnxRuntime C++ 2 | 3 | This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API. 4 | 5 | We recommend using Visual Studio to build the project. 6 | 7 | ## Benefits 8 | 9 | - Friendly for deployment in the industrial sector. 10 | - Faster than OpenCV's DNN inference on both CPU and GPU. 11 | - Supports CUDA acceleration. 12 | - Easy to add FP16 inference (using template functions). 13 | 14 | ## Exporting YOLOv8 Models 15 | 16 | To export YOLOv8 models, use the following Python script: 17 | 18 | ```python 19 | from ultralytics import YOLO 20 | 21 | # Load a YOLOv8 model 22 | model = YOLO("yolov8n.pt") 23 | 24 | # Export the model 25 | model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640) 26 | ``` 27 | 28 | ## Dependencies 29 | 30 | | Dependency | Version | 31 | | ----------------------- | -------- | 32 | | Onnxruntime-win-x64-gpu | >=1.14.1 | 33 | | OpenCV | >=4.0.0 | 34 | | C++ | >=17 | 35 | 36 | Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature. 37 | 38 | ## Usage 39 | 40 | ```c++ 41 | // CPU inference 42 | DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, false}; 43 | // GPU inference 44 | DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, true}; 45 | 46 | // Load your image 47 | cv::Mat img = cv::imread(img_path); 48 | 49 | char* ret = p1->CreateSession(params); 50 | 51 | ret = p->RunSession(img, res); 52 | ``` 53 | 54 | This repository should also work for YOLOv5, which needs a permute operator for the output of the YOLOv5 model, but this has not been implemented yet. 55 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-ONNXRuntime-CPP/inference.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #define _CRT_SECURE_NO_WARNINGS 4 | #define RET_OK nullptr 5 | 6 | #include 7 | #include 8 | #include 9 | #include "io.h" 10 | #include "direct.h" 11 | #include "opencv.hpp" 12 | #include 13 | #include "onnxruntime_cxx_api.h" 14 | 15 | 16 | enum MODEL_TYPE 17 | { 18 | //FLOAT32 MODEL 19 | YOLO_ORIGIN_V5 = 0, 20 | YOLO_ORIGIN_V8 = 1,//only support v8 detector currently 21 | YOLO_POSE_V8 = 2, 22 | YOLO_CLS_V8 = 3 23 | }; 24 | 25 | 26 | typedef struct _DCSP_INIT_PARAM 27 | { 28 | std::string ModelPath; 29 | MODEL_TYPE ModelType = YOLO_ORIGIN_V8; 30 | std::vector imgSize={640, 640}; 31 | 32 | int classesNum=80; 33 | float RectConfidenceThreshold = 0.6; 34 | float iouThreshold = 0.5; 35 | bool CudaEnable = false; 36 | int LogSeverityLevel = 3; 37 | int IntraOpNumThreads = 1; 38 | }DCSP_INIT_PARAM; 39 | 40 | 41 | typedef struct _DCSP_RESULT 42 | { 43 | int classId; 44 | float confidence; 45 | cv::Rect box; 46 | }DCSP_RESULT; 47 | 48 | 49 | class DCSP_CORE 50 | { 51 | public: 52 | DCSP_CORE(); 53 | ~DCSP_CORE(); 54 | 55 | public: 56 | char* CreateSession(DCSP_INIT_PARAM &iParams); 57 | 58 | 59 | char* RunSession(cv::Mat &iImg, std::vector& oResult); 60 | 61 | 62 | char* WarmUpSession(); 63 | 64 | 65 | template 66 | char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector& inputNodeDims, std::vector& oResult); 67 | 68 | 69 | private: 70 | Ort::Env env; 71 | Ort::Session* session; 72 | bool cudaEnable; 73 | Ort::RunOptions options; 74 | std::vector inputNodeNames; 75 | std::vector outputNodeNames; 76 | 77 | 78 | int classesNum; 79 | MODEL_TYPE modelType; 80 | std::vector imgSize; 81 | float rectConfidenceThreshold; 82 | float iouThreshold; 83 | }; 84 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-ONNXRuntime-CPP/main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include "inference.h" 4 | #include 5 | 6 | 7 | 8 | void file_iterator(DCSP_CORE*& p) 9 | { 10 | std::filesystem::path img_path = R"(E:\project\Project_C++\DCPS_ONNX\TEST_ORIGIN)"; 11 | int k = 0; 12 | for (auto& i : std::filesystem::directory_iterator(img_path)) 13 | { 14 | if (i.path().extension() == ".jpg") 15 | { 16 | std::string img_path = i.path().string(); 17 | //std::cout << img_path << std::endl; 18 | cv::Mat img = cv::imread(img_path); 19 | std::vector res; 20 | char* ret = p->RunSession(img, res); 21 | for (int i = 0; i < res.size(); i++) 22 | { 23 | cv::rectangle(img, res.at(i).box, cv::Scalar(125, 123, 0), 3); 24 | } 25 | 26 | k++; 27 | cv::imshow("TEST_ORIGIN", img); 28 | cv::waitKey(0); 29 | cv::destroyAllWindows(); 30 | //cv::imwrite("E:\\output\\" + std::to_string(k) + ".png", img); 31 | } 32 | } 33 | } 34 | 35 | 36 | 37 | int main() 38 | { 39 | DCSP_CORE* p1 = new DCSP_CORE; 40 | std::string model_path = "yolov8n.onnx"; 41 | DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 80, 0.1, 0.5, false }; 42 | char* ret = p1->CreateSession(params); 43 | file_iterator(p1); 44 | } 45 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-ONNXRuntime/README.md: -------------------------------------------------------------------------------- 1 | # YOLOv8 - ONNX Runtime 2 | 3 | This project implements YOLOv8 using ONNX Runtime. 4 | 5 | ## Installation 6 | 7 | To run this project, you need to install the required dependencies. The following instructions will guide you through the installation process. 8 | 9 | ### Installing Required Dependencies 10 | 11 | You can install the required dependencies by running the following command: 12 | 13 | ```bash 14 | pip install -r requirements.txt 15 | ``` 16 | 17 | ### Installing `onnxruntime-gpu` 18 | 19 | If you have an NVIDIA GPU and want to leverage GPU acceleration, you can install the onnxruntime-gpu package using the following command: 20 | 21 | ```bash 22 | pip install onnxruntime-gpu 23 | ``` 24 | 25 | Note: Make sure you have the appropriate GPU drivers installed on your system. 26 | 27 | ### Installing `onnxruntime` (CPU version) 28 | 29 | If you don't have an NVIDIA GPU or prefer to use the CPU version of onnxruntime, you can install the onnxruntime package using the following command: 30 | 31 | ```bash 32 | pip install onnxruntime 33 | ``` 34 | 35 | ### Usage 36 | 37 | After successfully installing the required packages, you can run the YOLOv8 implementation using the following command: 38 | 39 | ```bash 40 | python main.py --model yolov8n.onnx --img image.jpg --conf-thres 0.5 --iou-thres 0.5 41 | ``` 42 | 43 | Make sure to replace yolov8n.onnx with the path to your YOLOv8 ONNX model file, image.jpg with the path to your input image, and adjust the confidence threshold (conf-thres) and IoU threshold (iou-thres) values as needed. 44 | -------------------------------------------------------------------------------- /Detector/examples/YOLOv8-OpenCV-ONNX-Python/README.md: -------------------------------------------------------------------------------- 1 | # YOLOv8 - OpenCV 2 | 3 | Implementation YOLOv8 on OpenCV using ONNX Format. 4 | 5 | Just simply clone and run 6 | 7 | ```bash 8 | pip install -r requirements.txt 9 | python main.py --model yolov8n.onnx --img image.jpg 10 | ``` 11 | 12 | If you start from scratch: 13 | 14 | ```bash 15 | pip install ultralytics 16 | yolo export model=yolov8n.pt imgsz=640 format=onnx opset=12 17 | ``` 18 | 19 | _\*Make sure to include "opset=12"_ 20 | -------------------------------------------------------------------------------- /Detector/requirements.txt: -------------------------------------------------------------------------------- 1 | # Ultralytics requirements 2 | # Usage: pip install -r requirements.txt 3 | 4 | # Base ---------------------------------------- 5 | matplotlib>=3.2.2 6 | numpy>=1.22.2 # pinned by Snyk to avoid a vulnerability 7 | opencv-python>=4.6.0 8 | pillow>=7.1.2 9 | pyyaml>=5.3.1 10 | requests>=2.23.0 11 | scipy>=1.4.1 12 | torch>=1.7.0 13 | torchvision>=0.8.1 14 | tqdm>=4.64.0 15 | 16 | # Logging ------------------------------------- 17 | # tensorboard>=2.13.0 18 | # dvclive>=2.12.0 19 | # clearml 20 | # comet 21 | 22 | # Plotting ------------------------------------ 23 | pandas>=1.1.4 24 | seaborn>=0.11.0 25 | 26 | # Export -------------------------------------- 27 | # coremltools>=6.0,<=6.2 # CoreML export 28 | # onnx>=1.12.0 # ONNX export 29 | # onnxsim>=0.4.1 # ONNX simplifier 30 | # nvidia-pyindex # TensorRT export 31 | # nvidia-tensorrt # TensorRT export 32 | # scikit-learn==0.19.2 # CoreML quantization 33 | # tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) 34 | # tflite-support 35 | # tensorflowjs>=3.9.0 # TF.js export 36 | # openvino-dev>=2023.0 # OpenVINO export 37 | 38 | # Extras -------------------------------------- 39 | psutil # system utilization 40 | py-cpuinfo # display CPU info 41 | # thop>=0.1.1 # FLOPs computation 42 | # ipython # interactive notebook 43 | # albumentations>=1.0.3 # training augmentations 44 | # pycocotools>=2.0.6 # COCO mAP 45 | # roboflow 46 | -------------------------------------------------------------------------------- /Detector/setup.cfg: -------------------------------------------------------------------------------- 1 | # Project-wide configuration file, can be used for package metadata and other toll configurations 2 | # Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments 3 | # Local usage: pip install pre-commit, pre-commit run --all-files 4 | 5 | [metadata] 6 | license_files = LICENSE 7 | description_file = README.md 8 | 9 | [tool:pytest] 10 | norecursedirs = 11 | .git 12 | dist 13 | build 14 | addopts = 15 | --doctest-modules 16 | --durations=25 17 | --color=yes 18 | 19 | [flake8] 20 | max-line-length = 120 21 | exclude = .tox,*.egg,build,temp 22 | select = E,W,F 23 | doctests = True 24 | verbose = 2 25 | # https://pep8.readthedocs.io/en/latest/intro.html#error-codes 26 | format = pylint 27 | # see: https://www.flake8rules.com/ 28 | ignore = E731,F405,E402,W504,E501 29 | # E731: Do not assign a lambda expression, use a def 30 | # F405: name may be undefined, or defined from star imports: module 31 | # E402: module level import not at top of file 32 | # W504: line break after binary operator 33 | # E501: line too long 34 | # removed: 35 | # F401: module imported but unused 36 | # E231: missing whitespace after ‘,’, ‘;’, or ‘:’ 37 | # E127: continuation line over-indented for visual indent 38 | # F403: ‘from module import *’ used; unable to detect undefined names 39 | 40 | 41 | [isort] 42 | # https://pycqa.github.io/isort/docs/configuration/options.html 43 | line_length = 120 44 | # see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html 45 | multi_line_output = 0 46 | 47 | [yapf] 48 | based_on_style = pep8 49 | spaces_before_comment = 2 50 | COLUMN_LIMIT = 120 51 | COALESCE_BRACKETS = True 52 | SPACES_AROUND_POWER_OPERATOR = True 53 | SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = True 54 | SPLIT_BEFORE_CLOSING_BRACKET = False 55 | SPLIT_BEFORE_FIRST_ARGUMENT = False 56 | # EACH_DICT_ENTRY_ON_SEPARATE_LINE = False 57 | -------------------------------------------------------------------------------- /Detector/tests/conftest.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | 3 | 4 | def pytest_addoption(parser): 5 | parser.addoption('--runslow', action='store_true', default=False, help='run slow tests') 6 | 7 | 8 | def pytest_configure(config): 9 | config.addinivalue_line('markers', 'slow: mark test as slow to run') 10 | 11 | 12 | def pytest_collection_modifyitems(config, items): 13 | if config.getoption('--runslow'): 14 | # --runslow given in cli: do not skip slow tests 15 | return 16 | skip_slow = pytest.mark.skip(reason='need --runslow option to run') 17 | for item in items: 18 | if 'slow' in item.keywords: 19 | item.add_marker(skip_slow) 20 | -------------------------------------------------------------------------------- /Detector/ultralytics/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | __version__ = '8.0.149' 4 | 5 | from ultralytics.hub import start 6 | from ultralytics.models import RTDETR, SAM, YOLO 7 | from ultralytics.models.fastsam import FastSAM 8 | from ultralytics.models.nas import NAS 9 | from ultralytics.utils import SETTINGS as settings 10 | from ultralytics.utils.checks import check_yolo as checks 11 | from ultralytics.utils.downloads import download 12 | 13 | __all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'start', 'settings' # allow simpler import 14 | -------------------------------------------------------------------------------- /Detector/ultralytics/assets/bus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/ultralytics/assets/bus.jpg -------------------------------------------------------------------------------- /Detector/ultralytics/assets/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/ultralytics/assets/zidane.jpg -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/datasets/coco-pose.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO 2017 dataset http://cocodataset.org by Microsoft 3 | # Example usage: yolo train data=coco-pose.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco-pose ← downloads here (20.1 GB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco-pose # dataset root dir 12 | train: train2017.txt # train images (relative to 'path') 118287 images 13 | val: val2017.txt # val images (relative to 'path') 5000 images 14 | test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 15 | 16 | # Keypoints 17 | kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 18 | flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] 19 | 20 | # Classes 21 | names: 22 | 0: person 23 | 24 | # Download script/URL (optional) 25 | download: | 26 | from ultralytics.utils.downloads import download 27 | from pathlib import Path 28 | 29 | # Download labels 30 | dir = Path(yaml['path']) # dataset root dir 31 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 32 | urls = [url + 'coco2017labels-pose.zip'] # labels 33 | download(urls, dir=dir.parent) 34 | # Download data 35 | urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 36 | 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 37 | 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) 38 | download(urls, dir=dir / 'images', threads=3) 39 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/datasets/coco8-pose.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics 3 | # Example usage: yolo train data=coco8-pose.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco8-pose ← downloads here (1 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco8-pose # dataset root dir 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Keypoints 17 | kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 18 | flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] 19 | 20 | # Classes 21 | names: 22 | 0: person 23 | 24 | # Download script/URL (optional) 25 | download: https://ultralytics.com/assets/coco8-pose.zip 26 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v3/yolov3-spp.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv3-SPP object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | depth_multiple: 1.0 # model depth multiple 7 | width_multiple: 1.0 # layer channel multiple 8 | 9 | # darknet53 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [32, 3, 1]], # 0 13 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 14 | [-1, 1, Bottleneck, [64]], 15 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 16 | [-1, 2, Bottleneck, [128]], 17 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 18 | [-1, 8, Bottleneck, [256]], 19 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 20 | [-1, 8, Bottleneck, [512]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 22 | [-1, 4, Bottleneck, [1024]], # 10 23 | ] 24 | 25 | # YOLOv3-SPP head 26 | head: 27 | [[-1, 1, Bottleneck, [1024, False]], 28 | [-1, 1, SPP, [512, [5, 9, 13]]], 29 | [-1, 1, Conv, [1024, 3, 1]], 30 | [-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 32 | 33 | [-2, 1, Conv, [256, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 36 | [-1, 1, Bottleneck, [512, False]], 37 | [-1, 1, Bottleneck, [512, False]], 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 40 | 41 | [-2, 1, Conv, [128, 1, 1]], 42 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 43 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 44 | [-1, 1, Bottleneck, [256, False]], 45 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 46 | 47 | [[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v3/yolov3-tiny.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | depth_multiple: 1.0 # model depth multiple 7 | width_multiple: 1.0 # layer channel multiple 8 | 9 | # YOLOv3-tiny backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [16, 3, 1]], # 0 13 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 14 | [-1, 1, Conv, [32, 3, 1]], 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 16 | [-1, 1, Conv, [64, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 18 | [-1, 1, Conv, [128, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 20 | [-1, 1, Conv, [256, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 22 | [-1, 1, Conv, [512, 3, 1]], 23 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 24 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 25 | ] 26 | 27 | # YOLOv3-tiny head 28 | head: 29 | [[-1, 1, Conv, [1024, 3, 1]], 30 | [-1, 1, Conv, [256, 1, 1]], 31 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 32 | 33 | [-2, 1, Conv, [128, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 36 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 37 | 38 | [[19, 15], 1, Detect, [nc]], # Detect(P4, P5) 39 | ] 40 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v3/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv3 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | depth_multiple: 1.0 # model depth multiple 7 | width_multiple: 1.0 # layer channel multiple 8 | 9 | # darknet53 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [32, 3, 1]], # 0 13 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 14 | [-1, 1, Bottleneck, [64]], 15 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 16 | [-1, 2, Bottleneck, [128]], 17 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 18 | [-1, 8, Bottleneck, [256]], 19 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 20 | [-1, 8, Bottleneck, [512]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 22 | [-1, 4, Bottleneck, [1024]], # 10 23 | ] 24 | 25 | # YOLOv3 head 26 | head: 27 | [[-1, 1, Bottleneck, [1024, False]], 28 | [-1, 1, Conv, [512, 1, 1]], 29 | [-1, 1, Conv, [1024, 3, 1]], 30 | [-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 32 | 33 | [-2, 1, Conv, [256, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 36 | [-1, 1, Bottleneck, [512, False]], 37 | [-1, 1, Bottleneck, [512, False]], 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 40 | 41 | [-2, 1, Conv, [128, 1, 1]], 42 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 43 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 44 | [-1, 1, Bottleneck, [256, False]], 45 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 46 | 47 | [[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v5/yolov5.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv5 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov5 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 1024] 11 | l: [1.00, 1.00, 1024] 12 | x: [1.33, 1.25, 1024] 13 | 14 | # YOLOv5 v6.0 backbone 15 | backbone: 16 | # [from, number, module, args] 17 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 18 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 19 | [-1, 3, C3, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 21 | [-1, 6, C3, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 23 | [-1, 9, C3, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 25 | [-1, 3, C3, [1024]], 26 | [-1, 1, SPPF, [1024, 5]], # 9 27 | ] 28 | 29 | # YOLOv5 v6.0 head 30 | head: 31 | [[-1, 1, Conv, [512, 1, 1]], 32 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 33 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 34 | [-1, 3, C3, [512, False]], # 13 35 | 36 | [-1, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 39 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 40 | 41 | [-1, 1, Conv, [256, 3, 2]], 42 | [[-1, 14], 1, Concat, [1]], # cat head P4 43 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 44 | 45 | [-1, 1, Conv, [512, 3, 2]], 46 | [[-1, 10], 1, Concat, [1]], # cat head P5 47 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 48 | 49 | [[17, 20, 23], 1, Detect, [nc]], # Detect(P3, P4, P5) 50 | ] 51 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v6/yolov6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | activation: nn.ReLU() # (optional) model default activation function 7 | scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n' 8 | # [depth, width, max_channels] 9 | n: [0.33, 0.25, 1024] 10 | s: [0.33, 0.50, 1024] 11 | m: [0.67, 0.75, 768] 12 | l: [1.00, 1.00, 512] 13 | x: [1.00, 1.25, 512] 14 | 15 | # YOLOv6-3.0s backbone 16 | backbone: 17 | # [from, repeats, module, args] 18 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 19 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 20 | - [-1, 6, Conv, [128, 3, 1]] 21 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 22 | - [-1, 12, Conv, [256, 3, 1]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 18, Conv, [512, 3, 1]] 25 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 26 | - [-1, 6, Conv, [1024, 3, 1]] 27 | - [-1, 1, SPPF, [1024, 5]] # 9 28 | 29 | # YOLOv6-3.0s head 30 | head: 31 | - [-1, 1, Conv, [256, 1, 1]] 32 | - [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]] 33 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 34 | - [-1, 1, Conv, [256, 3, 1]] 35 | - [-1, 9, Conv, [256, 3, 1]] # 14 36 | 37 | - [-1, 1, Conv, [128, 1, 1]] 38 | - [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]] 39 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 40 | - [-1, 1, Conv, [128, 3, 1]] 41 | - [-1, 9, Conv, [128, 3, 1]] # 19 42 | 43 | - [-1, 1, Conv, [128, 3, 2]] 44 | - [[-1, 15], 1, Concat, [1]] # cat head P4 45 | - [-1, 1, Conv, [256, 3, 1]] 46 | - [-1, 9, Conv, [256, 3, 1]] # 23 47 | 48 | - [-1, 1, Conv, [256, 3, 2]] 49 | - [[-1, 10], 1, Concat, [1]] # cat head P5 50 | - [-1, 1, Conv, [512, 3, 1]] 51 | - [-1, 9, Conv, [512, 3, 1]] # 27 52 | 53 | - [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5) 54 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8-cls.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify 3 | 4 | # Parameters 5 | nc: 1000 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 1024] 11 | l: [1.00, 1.00, 1024] 12 | x: [1.00, 1.25, 1024] 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | 27 | # YOLOv8.0n head 28 | head: 29 | - [-1, 1, Classify, [nc]] # Classify 30 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8-p2.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0-p2 head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 39 | - [[-1, 2], 1, Concat, [1]] # cat backbone P2 40 | - [-1, 3, C2f, [128]] # 18 (P2/4-xsmall) 41 | 42 | - [-1, 1, Conv, [128, 3, 2]] 43 | - [[-1, 15], 1, Concat, [1]] # cat head P3 44 | - [-1, 3, C2f, [256]] # 21 (P3/8-small) 45 | 46 | - [-1, 1, Conv, [256, 3, 2]] 47 | - [[-1, 12], 1, Concat, [1]] # cat head P4 48 | - [-1, 3, C2f, [512]] # 24 (P4/16-medium) 49 | 50 | - [-1, 1, Conv, [512, 3, 2]] 51 | - [[-1, 9], 1, Concat, [1]] # cat head P5 52 | - [-1, 3, C2f, [1024]] # 27 (P5/32-large) 53 | 54 | - [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5) 55 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [768, True]] 26 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 27 | - [-1, 3, C2f, [1024, True]] 28 | - [-1, 1, SPPF, [1024, 5]] # 11 29 | 30 | # YOLOv8.0x6 head 31 | head: 32 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 33 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 34 | - [-1, 3, C2, [768, False]] # 14 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 38 | - [-1, 3, C2, [512, False]] # 17 39 | 40 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 41 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 42 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 43 | 44 | - [-1, 1, Conv, [256, 3, 2]] 45 | - [[-1, 17], 1, Concat, [1]] # cat head P4 46 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 47 | 48 | - [-1, 1, Conv, [512, 3, 2]] 49 | - [[-1, 14], 1, Concat, [1]] # cat head P5 50 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 51 | 52 | - [-1, 1, Conv, [768, 3, 2]] 53 | - [[-1, 11], 1, Concat, [1]] # cat head P6 54 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 55 | 56 | - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6) 57 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8-pose.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose 3 | 4 | # Parameters 5 | nc: 1 # number of classes 6 | kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 7 | scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n' 8 | # [depth, width, max_channels] 9 | n: [0.33, 0.25, 1024] 10 | s: [0.33, 0.50, 1024] 11 | m: [0.67, 0.75, 768] 12 | l: [1.00, 1.00, 512] 13 | x: [1.00, 1.25, 512] 14 | 15 | # YOLOv8.0n backbone 16 | backbone: 17 | # [from, repeats, module, args] 18 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 19 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 20 | - [-1, 3, C2f, [128, True]] 21 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 22 | - [-1, 6, C2f, [256, True]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [1024, True]] 27 | - [-1, 1, SPPF, [1024, 5]] # 9 28 | 29 | # YOLOv8.0n head 30 | head: 31 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 32 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 33 | - [-1, 3, C2f, [512]] # 12 34 | 35 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 36 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 37 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 38 | 39 | - [-1, 1, Conv, [256, 3, 2]] 40 | - [[-1, 12], 1, Concat, [1]] # cat head P4 41 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 42 | 43 | - [-1, 1, Conv, [512, 3, 2]] 44 | - [[-1, 9], 1, Concat, [1]] # cat head P5 45 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 46 | 47 | - [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5) 48 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8-rtdetr.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8-seg.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/models/v8/yolov8.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/trackers/botsort.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT 3 | 4 | tracker_type: botsort # tracker type, ['botsort', 'bytetrack'] 5 | track_high_thresh: 0.5 # threshold for the first association 6 | track_low_thresh: 0.1 # threshold for the second association 7 | new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks 8 | track_buffer: 30 # buffer to calculate the time when to remove tracks 9 | match_thresh: 0.8 # threshold for matching tracks 10 | # min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now) 11 | # mot20: False # for tracker evaluation(not used for now) 12 | 13 | # BoT-SORT settings 14 | cmc_method: sparseOptFlow # method of global motion compensation 15 | # ReID model related thresh (not supported yet) 16 | proximity_thresh: 0.5 17 | appearance_thresh: 0.25 18 | with_reid: False 19 | -------------------------------------------------------------------------------- /Detector/ultralytics/cfg/trackers/bytetrack.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack 3 | 4 | tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack'] 5 | track_high_thresh: 0.5 # threshold for the first association 6 | track_low_thresh: 0.1 # threshold for the second association 7 | new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks 8 | track_buffer: 30 # buffer to calculate the time when to remove tracks 9 | match_thresh: 0.8 # threshold for matching tracks 10 | # min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now) 11 | # mot20: False # for tracker evaluation(not used for now) 12 | -------------------------------------------------------------------------------- /Detector/ultralytics/data/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .base import BaseDataset 4 | from .build import build_dataloader, build_yolo_dataset, load_inference_source 5 | from .dataset import ClassificationDataset, SemanticDataset, YOLODataset 6 | 7 | __all__ = ('BaseDataset', 'ClassificationDataset', 'SemanticDataset', 'YOLODataset', 'build_yolo_dataset', 8 | 'build_dataloader', 'load_inference_source') 9 | -------------------------------------------------------------------------------- /Detector/ultralytics/data/annotator.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | 3 | from ultralytics import SAM, YOLO 4 | 5 | 6 | def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None): 7 | """ 8 | Automatically annotates images using a YOLO object detection model and a SAM segmentation model. 9 | Args: 10 | data (str): Path to a folder containing images to be annotated. 11 | det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. 12 | sam_model (str, optional): Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. 13 | device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available). 14 | output_dir (str | None | optional): Directory to save the annotated results. 15 | Defaults to a 'labels' folder in the same directory as 'data'. 16 | """ 17 | det_model = YOLO(det_model) 18 | sam_model = SAM(sam_model) 19 | 20 | if not output_dir: 21 | output_dir = Path(str(data)).parent / 'labels' 22 | Path(output_dir).mkdir(exist_ok=True, parents=True) 23 | 24 | det_results = det_model(data, stream=True, device=device) 25 | 26 | for result in det_results: 27 | class_ids = result.boxes.cls.int().tolist() # noqa 28 | if len(class_ids): 29 | boxes = result.boxes.xyxy # Boxes object for bbox outputs 30 | sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device) 31 | segments = sam_results[0].masks.xyn # noqa 32 | 33 | with open(f'{str(Path(output_dir) / Path(result.path).stem)}.txt', 'w') as f: 34 | for i in range(len(segments)): 35 | s = segments[i] 36 | if len(s) == 0: 37 | continue 38 | segment = map(str, segments[i].reshape(-1).tolist()) 39 | f.write(f'{class_ids[i]} ' + ' '.join(segment) + '\n') 40 | -------------------------------------------------------------------------------- /Detector/ultralytics/data/dataloaders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/ultralytics/data/dataloaders/__init__.py -------------------------------------------------------------------------------- /Detector/ultralytics/data/scripts/download_weights.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Ultralytics YOLO 🚀, AGPL-3.0 license 3 | # Download latest models from https://github.com/ultralytics/assets/releases 4 | # Example usage: bash ultralytics/data/scripts/download_weights.sh 5 | # parent 6 | # └── weights 7 | # ├── yolov8n.pt ← downloads here 8 | # ├── yolov8s.pt 9 | # └── ... 10 | 11 | python - < None: 26 | assert Path(model).suffix not in ('.yaml', '.yml'), 'YOLO-NAS models only support pre-trained models.' 27 | super().__init__(model, task='detect') 28 | 29 | @smart_inference_mode() 30 | def _load(self, weights: str, task: str): 31 | # Load or create new NAS model 32 | import super_gradients 33 | suffix = Path(weights).suffix 34 | if suffix == '.pt': 35 | self.model = torch.load(weights) 36 | elif suffix == '': 37 | self.model = super_gradients.training.models.get(weights, pretrained_weights='coco') 38 | # Standardize model 39 | self.model.fuse = lambda verbose=True: self.model 40 | self.model.stride = torch.tensor([32]) 41 | self.model.names = dict(enumerate(self.model._class_names)) 42 | self.model.is_fused = lambda: False # for info() 43 | self.model.yaml = {} # for info() 44 | self.model.pt_path = weights # for export() 45 | self.model.task = 'detect' # for export() 46 | 47 | def info(self, detailed=False, verbose=True): 48 | """ 49 | Logs model info. 50 | 51 | Args: 52 | detailed (bool): Show detailed information about model. 53 | verbose (bool): Controls verbosity. 54 | """ 55 | return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640) 56 | 57 | @property 58 | def task_map(self): 59 | return {'detect': {'predictor': NASPredictor, 'validator': NASValidator}} 60 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/nas/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.engine.predictor import BasePredictor 6 | from ultralytics.engine.results import Results 7 | from ultralytics.utils import ops 8 | from ultralytics.utils.ops import xyxy2xywh 9 | 10 | 11 | class NASPredictor(BasePredictor): 12 | 13 | def postprocess(self, preds_in, img, orig_imgs): 14 | """Postprocesses predictions and returns a list of Results objects.""" 15 | 16 | # Cat boxes and class scores 17 | boxes = xyxy2xywh(preds_in[0][0]) 18 | preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1) 19 | 20 | preds = ops.non_max_suppression(preds, 21 | self.args.conf, 22 | self.args.iou, 23 | agnostic=self.args.agnostic_nms, 24 | max_det=self.args.max_det, 25 | classes=self.args.classes) 26 | 27 | results = [] 28 | for i, pred in enumerate(preds): 29 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 30 | if not isinstance(orig_imgs, torch.Tensor): 31 | pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) 32 | path = self.batch[0] 33 | img_path = path[i] if isinstance(path, list) else path 34 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) 35 | return results 36 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/nas/val.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.models.yolo.detect import DetectionValidator 6 | from ultralytics.utils import ops 7 | from ultralytics.utils.ops import xyxy2xywh 8 | 9 | __all__ = ['NASValidator'] 10 | 11 | 12 | class NASValidator(DetectionValidator): 13 | 14 | def postprocess(self, preds_in): 15 | """Apply Non-maximum suppression to prediction outputs.""" 16 | boxes = xyxy2xywh(preds_in[0][0]) 17 | preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1) 18 | return ops.non_max_suppression(preds, 19 | self.args.conf, 20 | self.args.iou, 21 | labels=self.lb, 22 | multi_label=False, 23 | agnostic=self.args.single_cls, 24 | max_det=self.args.max_det, 25 | max_time_img=0.5) 26 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/rtdetr/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .model import RTDETR 4 | from .predict import RTDETRPredictor 5 | from .val import RTDETRValidator 6 | 7 | __all__ = 'RTDETRPredictor', 'RTDETRValidator', 'RTDETR' 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/rtdetr/model.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | """ 3 | RT-DETR model interface 4 | """ 5 | from ultralytics.engine.model import Model 6 | from ultralytics.nn.tasks import RTDETRDetectionModel 7 | 8 | from .predict import RTDETRPredictor 9 | from .train import RTDETRTrainer 10 | from .val import RTDETRValidator 11 | 12 | 13 | class RTDETR(Model): 14 | """ 15 | RTDETR model interface. 16 | """ 17 | 18 | def __init__(self, model='rtdetr-l.pt') -> None: 19 | if model and not model.split('.')[-1] in ('pt', 'yaml', 'yml'): 20 | raise NotImplementedError('RT-DETR only supports creating from *.pt file or *.yaml file.') 21 | super().__init__(model=model, task='detect') 22 | 23 | @property 24 | def task_map(self): 25 | return { 26 | 'detect': { 27 | 'predictor': RTDETRPredictor, 28 | 'validator': RTDETRValidator, 29 | 'trainer': RTDETRTrainer, 30 | 'model': RTDETRDetectionModel}} 31 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/rtdetr/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.data.augment import LetterBox 6 | from ultralytics.engine.predictor import BasePredictor 7 | from ultralytics.engine.results import Results 8 | from ultralytics.utils import ops 9 | 10 | 11 | class RTDETRPredictor(BasePredictor): 12 | 13 | def postprocess(self, preds, img, orig_imgs): 14 | """Postprocess predictions and returns a list of Results objects.""" 15 | nd = preds[0].shape[-1] 16 | bboxes, scores = preds[0].split((4, nd - 4), dim=-1) 17 | results = [] 18 | for i, bbox in enumerate(bboxes): # (300, 4) 19 | bbox = ops.xywh2xyxy(bbox) 20 | score, cls = scores[i].max(-1, keepdim=True) # (300, 1) 21 | idx = score.squeeze(-1) > self.args.conf # (300, ) 22 | if self.args.classes is not None: 23 | idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx 24 | pred = torch.cat([bbox, score, cls], dim=-1)[idx] # filter 25 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 26 | oh, ow = orig_img.shape[:2] 27 | if not isinstance(orig_imgs, torch.Tensor): 28 | pred[..., [0, 2]] *= ow 29 | pred[..., [1, 3]] *= oh 30 | path = self.batch[0] 31 | img_path = path[i] if isinstance(path, list) else path 32 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) 33 | return results 34 | 35 | def pre_transform(self, im): 36 | """Pre-transform input image before inference. 37 | 38 | Args: 39 | im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list. 40 | 41 | Return: A list of transformed imgs. 42 | """ 43 | # The size must be square(640) and scaleFilled. 44 | return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im] 45 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/sam/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .model import SAM 4 | from .predict import Predictor 5 | 6 | # from .build import build_sam 7 | 8 | __all__ = 'SAM', 'Predictor' # tuple or list 9 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/sam/model.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | """ 3 | SAM model interface 4 | """ 5 | 6 | from ultralytics.engine.model import Model 7 | from ultralytics.utils.torch_utils import model_info 8 | 9 | from .build import build_sam 10 | from .predict import Predictor 11 | 12 | 13 | class SAM(Model): 14 | """ 15 | SAM model interface. 16 | """ 17 | 18 | def __init__(self, model='sam_b.pt') -> None: 19 | if model and not model.endswith('.pt') and not model.endswith('.pth'): 20 | # Should raise AssertionError instead? 21 | raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint') 22 | super().__init__(model=model, task='segment') 23 | 24 | def _load(self, weights: str, task=None): 25 | self.model = build_sam(weights) 26 | 27 | def predict(self, source, stream=False, bboxes=None, points=None, labels=None, **kwargs): 28 | """Predicts and returns segmentation masks for given image or video source.""" 29 | overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024) 30 | kwargs.update(overrides) 31 | prompts = dict(bboxes=bboxes, points=points, labels=labels) 32 | return super().predict(source, stream, prompts=prompts, **kwargs) 33 | 34 | def __call__(self, source=None, stream=False, bboxes=None, points=None, labels=None, **kwargs): 35 | """Calls the 'predict' function with given arguments to perform object detection.""" 36 | return self.predict(source, stream, bboxes, points, labels, **kwargs) 37 | 38 | def info(self, detailed=False, verbose=True): 39 | """ 40 | Logs model info. 41 | 42 | Args: 43 | detailed (bool): Show detailed information about model. 44 | verbose (bool): Controls verbosity. 45 | """ 46 | return model_info(self.model, detailed=detailed, verbose=verbose) 47 | 48 | @property 49 | def task_map(self): 50 | return {'segment': {'predictor': Predictor}} 51 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/sam/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.models.yolo import classify, detect, pose, segment 4 | 5 | from .model import YOLO 6 | 7 | __all__ = 'classify', 'segment', 'detect', 'pose', 'YOLO' 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/classify/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.models.yolo.classify.predict import ClassificationPredictor, predict 4 | from ultralytics.models.yolo.classify.train import ClassificationTrainer, train 5 | from ultralytics.models.yolo.classify.val import ClassificationValidator, val 6 | 7 | __all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val' 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/classify/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.engine.predictor import BasePredictor 6 | from ultralytics.engine.results import Results 7 | from ultralytics.utils import DEFAULT_CFG, ROOT 8 | 9 | 10 | class ClassificationPredictor(BasePredictor): 11 | 12 | def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): 13 | super().__init__(cfg, overrides, _callbacks) 14 | self.args.task = 'classify' 15 | 16 | def preprocess(self, img): 17 | """Converts input image to model-compatible data type.""" 18 | if not isinstance(img, torch.Tensor): 19 | img = torch.stack([self.transforms(im) for im in img], dim=0) 20 | img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) 21 | return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 22 | 23 | def postprocess(self, preds, img, orig_imgs): 24 | """Postprocesses predictions to return Results objects.""" 25 | results = [] 26 | for i, pred in enumerate(preds): 27 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 28 | path = self.batch[0] 29 | img_path = path[i] if isinstance(path, list) else path 30 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred)) 31 | 32 | return results 33 | 34 | 35 | def predict(cfg=DEFAULT_CFG, use_python=False): 36 | """Run YOLO model predictions on input images/videos.""" 37 | model = cfg.model or 'yolov8n-cls.pt' # or "resnet18" 38 | source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ 39 | else 'https://ultralytics.com/images/bus.jpg' 40 | 41 | args = dict(model=model, source=source) 42 | if use_python: 43 | from ultralytics import YOLO 44 | YOLO(model)(**args) 45 | else: 46 | predictor = ClassificationPredictor(overrides=args) 47 | predictor.predict_cli() 48 | 49 | 50 | if __name__ == '__main__': 51 | predict() 52 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/detect/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .predict import DetectionPredictor, predict 4 | from .train import DetectionTrainer, train 5 | from .val import DetectionValidator, val 6 | 7 | __all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val' 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/detect/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.engine.predictor import BasePredictor 6 | from ultralytics.engine.results import Results 7 | from ultralytics.utils import DEFAULT_CFG, ROOT, ops 8 | 9 | 10 | class DetectionPredictor(BasePredictor): 11 | 12 | def postprocess(self, preds, img, orig_imgs): 13 | """Postprocesses predictions and returns a list of Results objects.""" 14 | preds = ops.non_max_suppression(preds, 15 | self.args.conf, 16 | self.args.iou, 17 | agnostic=self.args.agnostic_nms, 18 | max_det=self.args.max_det, 19 | classes=self.args.classes) 20 | 21 | results = [] 22 | for i, pred in enumerate(preds): 23 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 24 | if not isinstance(orig_imgs, torch.Tensor): 25 | pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) 26 | path = self.batch[0] 27 | img_path = path[i] if isinstance(path, list) else path 28 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) 29 | return results 30 | 31 | 32 | def predict(cfg=DEFAULT_CFG, use_python=False): 33 | """Runs YOLO model inference on input image(s).""" 34 | model = cfg.model or 'yolov8n.pt' 35 | source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ 36 | else 'https://ultralytics.com/images/bus.jpg' 37 | 38 | args = dict(model=model, source=source) 39 | if use_python: 40 | from ultralytics import YOLO 41 | YOLO(model)(**args) 42 | else: 43 | predictor = DetectionPredictor(overrides=args) 44 | predictor.predict_cli() 45 | 46 | 47 | if __name__ == '__main__': 48 | predict() 49 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/model.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.engine.model import Model 4 | from ultralytics.models import yolo # noqa 5 | from ultralytics.nn.tasks import ClassificationModel, DetectionModel, PoseModel, SegmentationModel 6 | 7 | 8 | class YOLO(Model): 9 | """ 10 | YOLO (You Only Look Once) object detection model. 11 | """ 12 | 13 | @property 14 | def task_map(self): 15 | """Map head to model, trainer, validator, and predictor classes""" 16 | return { 17 | 'classify': { 18 | 'model': ClassificationModel, 19 | 'trainer': yolo.classify.ClassificationTrainer, 20 | 'validator': yolo.classify.ClassificationValidator, 21 | 'predictor': yolo.classify.ClassificationPredictor, }, 22 | 'detect': { 23 | 'model': DetectionModel, 24 | 'trainer': yolo.detect.DetectionTrainer, 25 | 'validator': yolo.detect.DetectionValidator, 26 | 'predictor': yolo.detect.DetectionPredictor, }, 27 | 'segment': { 28 | 'model': SegmentationModel, 29 | 'trainer': yolo.segment.SegmentationTrainer, 30 | 'validator': yolo.segment.SegmentationValidator, 31 | 'predictor': yolo.segment.SegmentationPredictor, }, 32 | 'pose': { 33 | 'model': PoseModel, 34 | 'trainer': yolo.pose.PoseTrainer, 35 | 'validator': yolo.pose.PoseValidator, 36 | 'predictor': yolo.pose.PosePredictor, }, } 37 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/pose/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .predict import PosePredictor, predict 4 | from .train import PoseTrainer, train 5 | from .val import PoseValidator, val 6 | 7 | __all__ = 'PoseTrainer', 'train', 'PoseValidator', 'val', 'PosePredictor', 'predict' 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/models/yolo/segment/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .predict import SegmentationPredictor, predict 4 | from .train import SegmentationTrainer, train 5 | from .val import SegmentationValidator, val 6 | 7 | __all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val' 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/nn/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .tasks import (BaseModel, ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight, 4 | attempt_load_weights, guess_model_scale, guess_model_task, parse_model, torch_safe_load, 5 | yaml_model_load) 6 | 7 | __all__ = ('attempt_load_one_weight', 'attempt_load_weights', 'parse_model', 'yaml_model_load', 'guess_model_task', 8 | 'guess_model_scale', 'torch_safe_load', 'DetectionModel', 'SegmentationModel', 'ClassificationModel', 9 | 'BaseModel') 10 | -------------------------------------------------------------------------------- /Detector/ultralytics/nn/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | """ 3 | Ultralytics modules. Visualize with: 4 | 5 | from ultralytics.nn.modules import * 6 | import torch 7 | import os 8 | 9 | x = torch.ones(1, 128, 40, 40) 10 | m = Conv(128, 128) 11 | f = f'{m._get_name()}.onnx' 12 | torch.onnx.export(m, x, f) 13 | os.system(f'onnxsim {f} {f} && open {f}') 14 | """ 15 | 16 | from .block import (C1, C2, C3, C3TR, DFL, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, GhostBottleneck, 17 | HGBlock, HGStem, Proto, RepC3) 18 | from .conv import (CBAM, ChannelAttention, Concat, Conv, Conv2, ConvTranspose, DWConv, DWConvTranspose2d, Focus, 19 | GhostConv, LightConv, RepConv, SpatialAttention) 20 | from .head import Classify, Detect, Pose, RTDETRDecoder, Segment 21 | from .transformer import (AIFI, MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer, LayerNorm2d, 22 | MLPBlock, MSDeformAttn, TransformerBlock, TransformerEncoderLayer, TransformerLayer) 23 | 24 | __all__ = ('Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 25 | 'GhostConv', 'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'TransformerLayer', 26 | 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 27 | 'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect', 28 | 'Segment', 'Pose', 'Classify', 'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI', 29 | 'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP') 30 | -------------------------------------------------------------------------------- /Detector/ultralytics/trackers/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .bot_sort import BOTSORT 4 | from .byte_tracker import BYTETracker 5 | from .track import register_tracker 6 | 7 | __all__ = 'register_tracker', 'BOTSORT', 'BYTETracker' # allow simpler import 8 | -------------------------------------------------------------------------------- /Detector/ultralytics/trackers/basetrack.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from collections import OrderedDict 4 | 5 | import numpy as np 6 | 7 | 8 | class TrackState: 9 | """Enumeration of possible object tracking states.""" 10 | 11 | New = 0 12 | Tracked = 1 13 | Lost = 2 14 | Removed = 3 15 | 16 | 17 | class BaseTrack: 18 | """Base class for object tracking, handling basic track attributes and operations.""" 19 | 20 | _count = 0 21 | 22 | track_id = 0 23 | is_activated = False 24 | state = TrackState.New 25 | 26 | history = OrderedDict() 27 | features = [] 28 | curr_feature = None 29 | score = 0 30 | start_frame = 0 31 | frame_id = 0 32 | time_since_update = 0 33 | 34 | # Multi-camera 35 | location = (np.inf, np.inf) 36 | 37 | @property 38 | def end_frame(self): 39 | """Return the last frame ID of the track.""" 40 | return self.frame_id 41 | 42 | @staticmethod 43 | def next_id(): 44 | """Increment and return the global track ID counter.""" 45 | BaseTrack._count += 1 46 | return BaseTrack._count 47 | 48 | def activate(self, *args): 49 | """Activate the track with the provided arguments.""" 50 | raise NotImplementedError 51 | 52 | def predict(self): 53 | """Predict the next state of the track.""" 54 | raise NotImplementedError 55 | 56 | def update(self, *args, **kwargs): 57 | """Update the track with new observations.""" 58 | raise NotImplementedError 59 | 60 | def mark_lost(self): 61 | """Mark the track as lost.""" 62 | self.state = TrackState.Lost 63 | 64 | def mark_removed(self): 65 | """Mark the track as removed.""" 66 | self.state = TrackState.Removed 67 | 68 | @staticmethod 69 | def reset_id(): 70 | """Reset the global track ID counter.""" 71 | BaseTrack._count = 0 72 | -------------------------------------------------------------------------------- /Detector/ultralytics/trackers/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/ultralytics/trackers/utils/__init__.py -------------------------------------------------------------------------------- /Detector/ultralytics/utils/callbacks/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .base import add_integration_callbacks, default_callbacks, get_default_callbacks 4 | 5 | __all__ = 'add_integration_callbacks', 'default_callbacks', 'get_default_callbacks' 6 | -------------------------------------------------------------------------------- /Detector/ultralytics/utils/callbacks/raytune.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.utils import SETTINGS 4 | 5 | try: 6 | import ray 7 | from ray import tune 8 | from ray.air import session 9 | 10 | assert SETTINGS['raytune'] is True # verify integration is enabled 11 | except (ImportError, AssertionError): 12 | tune = None 13 | 14 | 15 | def on_fit_epoch_end(trainer): 16 | """Sends training metrics to Ray Tune at end of each epoch.""" 17 | if ray.tune.is_session_enabled(): 18 | metrics = trainer.metrics 19 | metrics['epoch'] = trainer.epoch 20 | session.report(metrics) 21 | 22 | 23 | callbacks = { 24 | 'on_fit_epoch_end': on_fit_epoch_end, } if tune else {} 25 | -------------------------------------------------------------------------------- /Detector/ultralytics/utils/callbacks/tensorboard.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr 4 | 5 | try: 6 | from torch.utils.tensorboard import SummaryWriter 7 | 8 | assert not TESTS_RUNNING # do not log pytest 9 | assert SETTINGS['tensorboard'] is True # verify integration is enabled 10 | 11 | # TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows 12 | except (ImportError, AssertionError, TypeError): 13 | SummaryWriter = None 14 | 15 | writer = None # TensorBoard SummaryWriter instance 16 | 17 | 18 | def _log_scalars(scalars, step=0): 19 | """Logs scalar values to TensorBoard.""" 20 | if writer: 21 | for k, v in scalars.items(): 22 | writer.add_scalar(k, v, step) 23 | 24 | 25 | def on_pretrain_routine_start(trainer): 26 | """Initialize TensorBoard logging with SummaryWriter.""" 27 | if SummaryWriter: 28 | try: 29 | global writer 30 | writer = SummaryWriter(str(trainer.save_dir)) 31 | prefix = colorstr('TensorBoard: ') 32 | LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/") 33 | except Exception as e: 34 | LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}') 35 | 36 | 37 | def on_batch_end(trainer): 38 | """Logs scalar statistics at the end of a training batch.""" 39 | _log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1) 40 | 41 | 42 | def on_fit_epoch_end(trainer): 43 | """Logs epoch metrics at end of training epoch.""" 44 | _log_scalars(trainer.metrics, trainer.epoch + 1) 45 | 46 | 47 | callbacks = { 48 | 'on_pretrain_routine_start': on_pretrain_routine_start, 49 | 'on_fit_epoch_end': on_fit_epoch_end, 50 | 'on_batch_end': on_batch_end} 51 | -------------------------------------------------------------------------------- /Detector/ultralytics/utils/errors.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.utils import emojis 4 | 5 | 6 | class HUBModelError(Exception): 7 | 8 | def __init__(self, message='Model not found. Please check model URL and try again.'): 9 | """Create an exception for when a model is not found.""" 10 | super().__init__(emojis(message)) 11 | -------------------------------------------------------------------------------- /Detector/ultralytics/utils/patches.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | """ 3 | Monkey patches to update/extend functionality of existing functions 4 | """ 5 | 6 | from pathlib import Path 7 | 8 | import cv2 9 | import numpy as np 10 | import torch 11 | 12 | # OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------ 13 | _imshow = cv2.imshow # copy to avoid recursion errors 14 | 15 | 16 | def imread(filename, flags=cv2.IMREAD_COLOR): 17 | return cv2.imdecode(np.fromfile(filename, np.uint8), flags) 18 | 19 | 20 | def imwrite(filename, img): 21 | try: 22 | cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) 23 | return True 24 | except Exception: 25 | return False 26 | 27 | 28 | def imshow(path, im): 29 | _imshow(path.encode('unicode_escape').decode(), im) 30 | 31 | 32 | # PyTorch functions ---------------------------------------------------------------------------------------------------- 33 | _torch_save = torch.save # copy to avoid recursion errors 34 | 35 | 36 | def torch_save(*args, **kwargs): 37 | """Use dill (if exists) to serialize the lambda functions where pickle does not do this.""" 38 | try: 39 | import dill as pickle 40 | except ImportError: 41 | import pickle 42 | 43 | if 'pickle_module' not in kwargs: 44 | kwargs['pickle_module'] = pickle 45 | return _torch_save(*args, **kwargs) 46 | -------------------------------------------------------------------------------- /Detector/ultralytics/yolo/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from . import v8 4 | 5 | __all__ = 'v8', # tuple or list 6 | -------------------------------------------------------------------------------- /Detector/ultralytics/yolo/cfg/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.cfg'] = importlib.import_module('ultralytics.cfg') 8 | 9 | LOGGER.warning("WARNING ⚠️ 'ultralytics.yolo.cfg' is deprecated since '8.0.136' and will be removed in '8.1.0'. " 10 | "Please use 'ultralytics.cfg' instead.") 11 | -------------------------------------------------------------------------------- /Detector/ultralytics/yolo/data/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.data'] = importlib.import_module('ultralytics.data') 8 | # This is for updating old cls models, or the way in following warning won't work. 9 | sys.modules['ultralytics.yolo.data.augment'] = importlib.import_module('ultralytics.data.augment') 10 | 11 | DATA_WARNING = """WARNING ⚠️ 'ultralytics.yolo.data' is deprecated since '8.0.136' and will be removed in '8.1.0'. Please use 'ultralytics.data' instead. 12 | Note this warning may be related to loading older models. You can update your model to current structure with: 13 | import torch 14 | ckpt = torch.load("model.pt") # applies to both official and custom models 15 | torch.save(ckpt, "updated-model.pt") 16 | """ 17 | LOGGER.warning(DATA_WARNING) 18 | -------------------------------------------------------------------------------- /Detector/ultralytics/yolo/engine/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.engine'] = importlib.import_module('ultralytics.engine') 8 | 9 | LOGGER.warning("WARNING ⚠️ 'ultralytics.yolo.engine' is deprecated since '8.0.136' and will be removed in '8.1.0'. " 10 | "Please use 'ultralytics.engine' instead.") 11 | -------------------------------------------------------------------------------- /Detector/ultralytics/yolo/utils/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.utils'] = importlib.import_module('ultralytics.utils') 8 | 9 | UTILS_WARNING = """WARNING ⚠️ 'ultralytics.yolo.utils' is deprecated since '8.0.136' and will be removed in '8.1.0'. Please use 'ultralytics.utils' instead. 10 | Note this warning may be related to loading older models. You can update your model to current structure with: 11 | import torch 12 | ckpt = torch.load("model.pt") # applies to both official and custom models 13 | torch.save(ckpt, "updated-model.pt") 14 | """ 15 | LOGGER.warning(UTILS_WARNING) 16 | -------------------------------------------------------------------------------- /Detector/ultralytics/yolo/v8/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.v8'] = importlib.import_module('ultralytics.models.yolo') 8 | 9 | LOGGER.warning("WARNING ⚠️ 'ultralytics.yolo.v8' is deprecated since '8.0.136' and will be removed in '8.1.0'. " 10 | "Please use 'ultralytics.models.yolo' instead.") 11 | -------------------------------------------------------------------------------- /Detector/yolov8n.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/Detector/yolov8n.pt -------------------------------------------------------------------------------- /cldm/model.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | 4 | from omegaconf import OmegaConf 5 | from ldm.util import instantiate_from_config 6 | 7 | 8 | def get_state_dict(d): 9 | return d.get('state_dict', d) 10 | 11 | 12 | def load_state_dict(ckpt_path, location='cpu'): 13 | _, extension = os.path.splitext(ckpt_path) 14 | if extension.lower() == ".safetensors": 15 | import safetensors.torch 16 | state_dict = safetensors.torch.load_file(ckpt_path, device=location) 17 | else: 18 | state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location))) 19 | state_dict = get_state_dict(state_dict) 20 | print(f'Loaded state_dict from [{ckpt_path}]') 21 | return state_dict 22 | 23 | 24 | def create_model(config_path): 25 | config = OmegaConf.load(config_path) 26 | model = instantiate_from_config(config.model).cpu() 27 | print(f'Loaded model config from [{config_path}]') 28 | return model 29 | -------------------------------------------------------------------------------- /dataset_test_load.py: -------------------------------------------------------------------------------- 1 | import json 2 | import cv2 3 | import numpy as np 4 | 5 | from torch.utils.data import Dataset 6 | import os 7 | 8 | 9 | class MyTestDataset(Dataset): 10 | def __init__(self): 11 | self.data = [] 12 | with open('./sample_test.json', 'rt') as f: 13 | for line in f: 14 | self.data.append(json.loads(line)) 15 | 16 | def __len__(self): 17 | return len(self.data) 18 | 19 | def __getitem__(self, idx): 20 | item = self.data[idx] 21 | 22 | source_filename = item['source'] 23 | target_filename = item['target'] 24 | prompt = item['prompt'] 25 | 26 | source = cv2.imread('./polyp_data/' + source_filename) # replace directory "polyp_data" with your test data source directory 27 | target = cv2.imread('./polyp_data/' + target_filename) 28 | 29 | #source = cv2.resize(source, (64, 64)) # ！！！！！！！！！ 30 | #target = cv2.resize(target, (64, 64)) # ！！！！！！！！！ 31 | 32 | 33 | # Do not forget that OpenCV read images in BGR order. 34 | source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB) 35 | target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB) 36 | 37 | # Normalize source images to [0, 1]. 38 | source = source.astype(np.float16) / 255.0 39 | 40 | # Normalize target images to [-1, 1]. 41 | target = (target.astype(np.float16) / 127.5) - 1.0 42 | 43 | 44 | return dict(jpg=target, txt=prompt, hint=source, target_name = target_filename) 45 | -------------------------------------------------------------------------------- /dataset_train_load.py: -------------------------------------------------------------------------------- 1 | import json 2 | import cv2 3 | import numpy as np 4 | 5 | from torch.utils.data import Dataset 6 | 7 | 8 | class MyDataset(Dataset): 9 | def __init__(self): 10 | self.data = [] 11 | with open('./sample_train.json', 'rt') as f: 12 | for line in f: 13 | self.data.append(json.loads(line)) 14 | 15 | def __len__(self): 16 | return len(self.data) 17 | 18 | def __getitem__(self, idx): 19 | item = self.data[idx] 20 | 21 | source_filename = item['source'] 22 | target_filename = item['target'] 23 | prompt = item['prompt'] 24 | 25 | source = cv2.imread('./polyp_data/' + source_filename) #replace directory "polyp_data" with your source data directory 26 | target = cv2.imread('./polyp_data/' + target_filename) #replace directory "polyp_data" with your target data directory 27 | 28 | #source = cv2.resize(source, (64, 64)) # ！！！！！！！！！ 29 | #target = cv2.resize(target, (64, 64)) # ！！！！！！！！！ 30 | 31 | 32 | # Do not forget that OpenCV read images in BGR order. 33 | source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB) 34 | target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB) 35 | 36 | # Normalize source images to [0, 1]. 37 | source = source.astype(np.float16) / 255.0 38 | 39 | # Normalize target images to [-1, 1]. 40 | target = (target.astype(np.float16) / 127.5) - 1.0 41 | 42 | 43 | return dict(jpg=target, txt=prompt, hint=source) 44 | -------------------------------------------------------------------------------- /figures/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /figures/intro.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/figures/intro.png -------------------------------------------------------------------------------- /ldm/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/ldm/data/__init__.py -------------------------------------------------------------------------------- /ldm/data/util.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from ldm.modules.midas.api import load_midas_transform 4 | 5 | 6 | class AddMiDaS(object): 7 | def __init__(self, model_type): 8 | super().__init__() 9 | self.transform = load_midas_transform(model_type) 10 | 11 | def pt2np(self, x): 12 | x = ((x + 1.0) * .5).detach().cpu().numpy() 13 | return x 14 | 15 | def np2pt(self, x): 16 | x = torch.from_numpy(x) * 2 - 1. 17 | return x 18 | 19 | def __call__(self, sample): 20 | # sample['jpg'] is tensor hwc in [-1, 1] at this point 21 | x = self.pt2np(sample['jpg']) 22 | x = self.transform({"image": x})["image"] 23 | sample['midas_in'] = x 24 | return sample 25 | -------------------------------------------------------------------------------- /ldm/models/diffusion/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/ldm/models/diffusion/__init__.py -------------------------------------------------------------------------------- /ldm/models/diffusion/dpm_solver/__init__.py: -------------------------------------------------------------------------------- 1 | from .sampler import DPMSolverSampler -------------------------------------------------------------------------------- /ldm/models/diffusion/sampling_util.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | 5 | def append_dims(x, target_dims): 6 | """Appends dimensions to the end of a tensor until it has target_dims dimensions. 7 | From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" 8 | dims_to_append = target_dims - x.ndim 9 | if dims_to_append < 0: 10 | raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') 11 | return x[(...,) + (None,) * dims_to_append] 12 | 13 | 14 | def norm_thresholding(x0, value): 15 | s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) 16 | return x0 * (value / s) 17 | 18 | 19 | def spatial_norm_thresholding(x0, value): 20 | # b c h w 21 | s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) 22 | return x0 * (value / s) -------------------------------------------------------------------------------- /ldm/modules/diffusionmodules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/ldm/modules/diffusionmodules/__init__.py -------------------------------------------------------------------------------- /ldm/modules/distributions/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/ldm/modules/distributions/__init__.py -------------------------------------------------------------------------------- /ldm/modules/encoders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vanshali/ControlPolypNet/a7d1cedd781075c485c0852d6783bd8d6698a16b/ldm/modules/encoders/__init__.py -------------------------------------------------------------------------------- /ldm/modules/image_degradation/__init__.py: -------------------------------------------------------------------------------- 1 | from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr 2 | from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light 3 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch==1.12.1 2 | torchvision==0.13.1 3 | numpy==1.23.1 4 | albumentations==1.3.0 5 | opencv-contrib-python==4.4.0.46 6 | imageio==2.9.0 7 | imageio-ffmpeg==0.4.2 8 | pytorch-lightning==1.5.0 9 | omegaconf==2.1.1 10 | test-tube>=0.7.5 11 | streamlit==1.12.1 12 | einops==0.3.0 13 | transformers==4.19.2 14 | webdataset==0.2.5 15 | kornia==0.6 16 | open_clip_torch==2.0.2 17 | invisible-watermark>=0.1.5 18 | streamlit-drawable-canvas==0.8.0 19 | torchmetrics==0.6.0 20 | timm==0.6.12 21 | addict==2.4.0 22 | yapf==0.32.0 23 | prettytable==3.6.0 24 | safetensors==0.2.7 25 | basicsr==1.4.2 26 | ultralytics 27 | -------------------------------------------------------------------------------- /sample_test.json: -------------------------------------------------------------------------------- 1 | {"source": "./negative_data/negative_masked_images_all/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image011171.jpg", "target": "./negative_data/negative_orig/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image011171.jpg", "prompt": "polyp"} 2 | {"source": "./negative_data/negative_masked_images_all/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image008809.jpg", "target": "./negative_data/negative_orig/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image008809.jpg", "prompt": "polyp"} 3 | {"source": "./negative_data/negative_masked_images_all/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image008088.jpg", "target": "./negative_data/negative_orig/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image008088.jpg", "prompt": "polyp"} 4 | {"source": "./negative_data/negative_masked_images_all/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image001353.jpg", "target": "./negative_data/negative_orig/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image001353.jpg", "prompt": "polyp"} 5 | {"source": "./negative_data/negative_masked_images_all/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image007298.jpg", "target": "./negative_data/negative_orig/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image007298.jpg", "prompt": "polyp"} 6 | {"source": "./negative_data/negative_masked_images_all/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image008893.jpg", "target": "./negative_data/negative_orig/case1/case_M_20181109094641_0U62372110931241_1_003_001-1_Negative_ayy_image008893.jpg", "prompt": "polyp"} 7 | -------------------------------------------------------------------------------- /tool_add_control.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | 4 | assert len(sys.argv) == 3, 'Args are wrong.' 5 | 6 | input_path = sys.argv[1] 7 | output_path = sys.argv[2] 8 | 9 | assert os.path.exists(input_path), 'Input model does not exist.' 10 | assert not os.path.exists(output_path), 'Output filename already exists.' 11 | assert os.path.exists(os.path.dirname(output_path)), 'Output path is not valid.' 12 | 13 | import torch 14 | from share import * 15 | from cldm.model import create_model 16 | 17 | 18 | def get_node_name(name, parent_name): 19 | if len(name) <= len(parent_name): 20 | return False, '' 21 | p = name[:len(parent_name)] 22 | if p != parent_name: 23 | return False, '' 24 | return True, name[len(parent_name):] 25 | 26 | 27 | model = create_model(config_path='./models/cldm_v15.yaml') # Replace with path to cldm_v15.yaml file inside models folder 28 | 29 | pretrained_weights = torch.load(input_path) 30 | if 'state_dict' in pretrained_weights: 31 | pretrained_weights = pretrained_weights['state_dict'] 32 | 33 | scratch_dict = model.state_dict() 34 | 35 | target_dict = {} 36 | for k in scratch_dict.keys(): 37 | is_control, name = get_node_name(k, 'control_') 38 | if is_control: 39 | copy_k = 'model.diffusion_' + name 40 | else: 41 | copy_k = k 42 | if copy_k in pretrained_weights: 43 | target_dict[k] = pretrained_weights[copy_k].clone() 44 | else: 45 | target_dict[k] = scratch_dict[k].clone() 46 | print(f'These weights are newly added: {k}') 47 | 48 | model.load_state_dict(target_dict, strict=True) 49 | torch.save(model.state_dict(), output_path) 50 | print('Done.') 51 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # Part of the code used from ControlNet GitHub repository https://github.com/lllyasviel/ControlNet and https://github.com/ultralytics/ultralytics/tree/main 2 | 3 | # from share import * 4 | 5 | import pytorch_lightning as pl 6 | from torch.utils.data import DataLoader 7 | from dataset_train_load import MyDataset 8 | from cldm.logger import ImageLogger 9 | from cldm.model import create_model, load_state_dict 10 | from pytorch_lightning.callbacks import ModelCheckpoint 11 | 12 | from dataset_test_load import MyTestDataset 13 | 14 | 15 | # Configs 16 | resume_path = './models/control_sd15_ini.ckpt' 17 | #resume_path = './model-epoch=55.ckpt' 18 | 19 | batch_size = 32 20 | logger_freq = 300 21 | #learning_rate = 1e-5 22 | #sd_locked = True 23 | only_mid_control = False 24 | 25 | learning_rate = 2e-6 26 | sd_locked = False 27 | 28 | 29 | # First use cpu to load models. Pytorch Lightning will automatically move it to GPUs. 30 | model = create_model('./models/cldm_v15.yaml').cpu() 31 | model.load_state_dict(load_state_dict(resume_path, location='cpu')) 32 | model.learning_rate = learning_rate 33 | model.sd_locked = sd_locked 34 | model.only_mid_control = only_mid_control 35 | 36 | 37 | # Misc 38 | dataset = MyDataset() 39 | dataloader = DataLoader(dataset, num_workers=0, batch_size=batch_size, shuffle=True) 40 | checkpoint_callback = ModelCheckpoint(dirpath="./", filename='model-{epoch:02d}', save_top_k=-1) 41 | logger = ImageLogger(batch_frequency=logger_freq) 42 | trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger,checkpoint_callback], default_root_dir='./chkp_folder/', enable_checkpointing = True, max_epochs=55) 43 | #trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger], accumulate_grad_batches=4) # But this will be 4x slower 44 | 45 | # Train! 46 | trainer.fit(model, dataloader) 47 | 48 | for i in range(1,201): # Evaluate the model on first 200 images from test json file 49 | test_dataset = MyTestDataset(i) 50 | # Create a test dataloader 51 | test_loader = DataLoader(test_dataset, batch_size=50, shuffle=False, num_workers=0) 52 | trainer.validate(model = model, dataloaders = test_loader) 53 | 54 | --------------------------------------------------------------------------------