├── .DS_Store ├── LICENSE ├── README.md ├── carla ├── .gitignore ├── README.md ├── bounding_boxes.py ├── camera_utils.py ├── config │ ├── sensors-cordinates-fixed-perception.json │ ├── sensors-fixed-perception.json │ ├── sensors.json │ └── wheel.ini ├── configuration.py ├── datadescriptor.py ├── ego_vehicle.py ├── fixed_perception.py ├── main.py ├── npc_spawning.py ├── save_sensors.py └── utils │ ├── arg_parser.py │ ├── g29_steering_wheel.py │ └── weather.py ├── images ├── ego-perception-baselines.png ├── fixed-perception-baselines.png ├── related-datasets.png └── teaser.png ├── rvt ├── .gitignore ├── LICENSE ├── README.md ├── callbacks │ ├── custom.py │ ├── detection.py │ ├── gradflow.py │ ├── utils │ │ └── visualization.py │ └── viz_base.py ├── config │ ├── dataset │ │ ├── base.yaml │ │ ├── etrap.yaml │ │ ├── gen1.yaml │ │ └── gen4.yaml │ ├── experiment │ │ ├── gen1 │ │ │ ├── base.yaml │ │ │ ├── default.yaml │ │ │ ├── small.yaml │ │ │ └── tiny.yaml │ │ └── gen4 │ │ │ ├── base.yaml │ │ │ ├── default.yaml │ │ │ ├── small.yaml │ │ │ └── tiny.yaml │ ├── general.yaml │ ├── model │ │ ├── base.yaml │ │ ├── maxvit_yolox │ │ │ └── default.yaml │ │ └── rnndet.yaml │ ├── modifier.py │ ├── train.yaml │ └── val.yaml ├── data │ ├── genx_utils │ │ ├── collate.py │ │ ├── collate_from_pytorch.py │ │ ├── dataset_rnd.py │ │ ├── dataset_streaming.py │ │ ├── labels.py │ │ ├── sequence_base.py │ │ ├── sequence_for_streaming.py │ │ └── sequence_rnd.py │ └── utils │ │ ├── augmentor.py │ │ ├── representations.py │ │ ├── spatial.py │ │ ├── stream_concat_datapipe.py │ │ ├── stream_sharded_datapipe.py │ │ └── types.py ├── environment.yaml ├── finetune.py ├── gen4_class_labels.py ├── loggers │ ├── utils.py │ └── wandb_logger.py ├── models │ ├── detection │ │ ├── __init_.py │ │ ├── recurrent_backbone │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ └── maxvit_rnn.py │ │ ├── yolox │ │ │ ├── models │ │ │ │ ├── __init__.py │ │ │ │ ├── losses.py │ │ │ │ ├── network_blocks.py │ │ │ │ └── yolo_head.py │ │ │ └── utils │ │ │ │ ├── __init__.py │ │ │ │ ├── boxes.py │ │ │ │ └── compat.py │ │ └── yolox_extension │ │ │ └── models │ │ │ ├── __init__.py │ │ │ ├── build.py │ │ │ ├── detector.py │ │ │ └── yolo_pafpn.py │ └── layers │ │ ├── maxvit │ │ ├── __init__.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── activations.py │ │ │ ├── activations_jit.py │ │ │ ├── activations_me.py │ │ │ ├── adaptive_avgmax_pool.py │ │ │ ├── attention_pool2d.py │ │ │ ├── blur_pool.py │ │ │ ├── bottleneck_attn.py │ │ │ ├── cbam.py │ │ │ ├── classifier.py │ │ │ ├── cond_conv2d.py │ │ │ ├── config.py │ │ │ ├── conv2d_same.py │ │ │ ├── conv_bn_act.py │ │ │ ├── create_act.py │ │ │ ├── create_attn.py │ │ │ ├── create_conv2d.py │ │ │ ├── create_norm.py │ │ │ ├── create_norm_act.py │ │ │ ├── drop.py │ │ │ ├── eca.py │ │ │ ├── evo_norm.py │ │ │ ├── fast_norm.py │ │ │ ├── filter_response_norm.py │ │ │ ├── gather_excite.py │ │ │ ├── global_context.py │ │ │ ├── halo_attn.py │ │ │ ├── helpers.py │ │ │ ├── inplace_abn.py │ │ │ ├── lambda_layer.py │ │ │ ├── linear.py │ │ │ ├── median_pool.py │ │ │ ├── mixed_conv2d.py │ │ │ ├── ml_decoder.py │ │ │ ├── mlp.py │ │ │ ├── non_local_attn.py │ │ │ ├── norm.py │ │ │ ├── norm_act.py │ │ │ ├── padding.py │ │ │ ├── patch_embed.py │ │ │ ├── pool2d_same.py │ │ │ ├── pos_embed.py │ │ │ ├── selective_kernel.py │ │ │ ├── separable_conv.py │ │ │ ├── space_to_depth.py │ │ │ ├── split_attn.py │ │ │ ├── split_batchnorm.py │ │ │ ├── squeeze_excite.py │ │ │ ├── std_conv.py │ │ │ ├── test_time_pool.py │ │ │ ├── trace_utils.py │ │ │ └── weight_init.py │ │ └── maxvit.py │ │ └── rnn.py ├── modules │ ├── __init__.py │ ├── data │ │ └── genx.py │ ├── detection.py │ └── utils │ │ ├── detection.py │ │ └── fetch.py ├── scripts │ └── genx │ │ ├── README.md │ │ ├── conf_preprocess │ │ ├── extraction │ │ │ ├── const_count.yaml │ │ │ └── const_duration.yaml │ │ ├── filter_gen1.yaml │ │ ├── filter_gen4.yaml │ │ └── representation │ │ │ ├── mixeddensity_stack.yaml │ │ │ └── stacked_hist.yaml │ │ ├── preprocess_dataset.py │ │ └── preprocess_dataset_gen4.py ├── train.py ├── utils │ ├── evaluation │ │ └── prophesee │ │ │ ├── __init__.py │ │ │ ├── evaluation.py │ │ │ ├── evaluator.py │ │ │ ├── io │ │ │ ├── __init__.py │ │ │ ├── box_filtering.py │ │ │ ├── box_loading.py │ │ │ ├── dat_events_tools.py │ │ │ ├── npy_events_tools.py │ │ │ └── psee_loader.py │ │ │ ├── metrics │ │ │ ├── __init__.py │ │ │ └── coco_eval.py │ │ │ └── visualize │ │ │ ├── __init__.py │ │ │ └── vis_utils.py │ ├── helpers.py │ ├── padding.py │ ├── preprocessing.py │ └── timers.py └── validation.py └── ultralytics ├── .github ├── ISSUE_TEMPLATE │ ├── bug-report.yml │ ├── config.yml │ ├── feature-request.yml │ └── question.yml ├── dependabot.yml ├── translate-readme.yml └── workflows │ ├── ci.yaml │ ├── cla.yml │ ├── codeql.yaml │ ├── docker.yaml │ ├── greetings.yml │ ├── links.yml │ ├── publish.yml │ └── stale.yml ├── .gitignore ├── .pre-commit-config.yaml ├── CITATION.cff ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── README.zh-CN.md ├── docker ├── Dockerfile ├── Dockerfile-arm64 ├── Dockerfile-conda ├── Dockerfile-cpu ├── Dockerfile-jetson ├── Dockerfile-python └── Dockerfile-runner ├── docs ├── CNAME ├── README.md ├── SECURITY.md ├── assets │ └── favicon.ico ├── build_reference.py ├── guides │ ├── azureml-quickstart.md │ ├── conda-quickstart.md │ ├── docker-quickstart.md │ ├── hyperparameter-tuning.md │ ├── index.md │ ├── kfold-cross-validation.md │ ├── raspberry-pi.md │ ├── sahi-tiled-inference.md │ ├── triton-inference-server.md │ └── yolo-common-issues.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 │ ├── mlflow.md │ ├── openvino.md │ ├── ray-tune.md │ └── roboflow.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 │ │ ├── tuner.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 │ │ ├── triton.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 │ │ ├── azureml_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 └── zh │ └── index.md ├── environment.yaml ├── examples ├── README.md ├── YOLOv8-CPP-Inference │ ├── CMakeLists.txt │ ├── README.md │ ├── inference.cpp │ ├── inference.h │ └── main.cpp ├── YOLOv8-ONNXRuntime-CPP │ ├── CMakeLists.txt │ ├── README.md │ ├── inference.cpp │ ├── inference.h │ └── main.cpp ├── YOLOv8-ONNXRuntime │ ├── README.md │ └── main.py ├── YOLOv8-OpenCV-ONNX-Python │ ├── README.md │ └── main.py ├── YOLOv8-Region-Counter │ ├── readme.md │ └── yolov8_region_counter.py ├── YOLOv8-SAHI-Inference-Video │ ├── readme.md │ └── yolov8_sahi.py ├── hub.ipynb ├── tutorial.ipynb ├── yolov8.yaml └── zidane.jpg ├── mkdocs.yml ├── requirements.txt ├── setup.cfg ├── setup.py ├── ultralytics ├── __init__.py ├── cfg │ ├── __init__.py │ ├── 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-p6.yaml │ │ │ ├── yolov8-seg.yaml │ │ │ └── yolov8.yaml │ └── trackers │ │ ├── botsort.yaml │ │ └── bytetrack.yaml ├── data │ ├── __init__.py │ ├── annotator.py │ ├── augment.py │ ├── base.py │ ├── build.py │ ├── converter.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 │ ├── tuner.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 │ ├── triton.py │ └── tuner.py └── yolo ├── data.yaml ├── main.py └── train.sh /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/.DS_Store -------------------------------------------------------------------------------- /carla/config/sensors-fixed-perception.json: -------------------------------------------------------------------------------- 1 | { 2 | "sensors": [ 3 | { 4 | "type": "sensor.camera.semantic_segmentation", 5 | "role_name": "semantic_segmentation_camera", 6 | "fov": 90.0, 7 | "image_size_x": 1280, 8 | "image_size_y": 960 9 | }, 10 | { 11 | "type": "sensor.camera.instance_segmentation", 12 | "role_name": "instance_segmentation_camera", 13 | "fov": 90.0, 14 | "image_size_x": 1280, 15 | "image_size_y": 960 16 | }, 17 | { 18 | "type": "sensor.camera.optical_flow", 19 | "role_name": "optical_flow_camera", 20 | "fov": 90.0, 21 | "image_size_x": 1280, 22 | "image_size_y": 960 23 | }, 24 | { 25 | "type": "sensor.camera.depth", 26 | "role_name": "depth_camera", 27 | "fov": 90.0, 28 | "image_size_x": 1280, 29 | "image_size_y": 960 30 | }, 31 | { 32 | "type": "sensor.camera.dvs", 33 | "role_name": "dvs_camera", 34 | "fov": 90.0, 35 | "image_size_x": 1280, 36 | "image_size_y": 960 37 | }, 38 | { 39 | "type": "sensor.camera.rgb", 40 | "role_name": "rgb_camera", 41 | "fov": 90.0, 42 | "image_size_x": 1280, 43 | "image_size_y": 960 44 | } 45 | ] 46 | } -------------------------------------------------------------------------------- /carla/config/wheel.ini: -------------------------------------------------------------------------------- 1 | [G29 Racing Wheel] 2 | steering_wheel = 0 3 | throttle = 2 4 | brake = 3 5 | reverse = 5 6 | handbrake = 4 -------------------------------------------------------------------------------- /images/ego-perception-baselines.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/images/ego-perception-baselines.png -------------------------------------------------------------------------------- /images/fixed-perception-baselines.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/images/fixed-perception-baselines.png -------------------------------------------------------------------------------- /images/related-datasets.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/images/related-datasets.png -------------------------------------------------------------------------------- /images/teaser.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/images/teaser.png -------------------------------------------------------------------------------- /rvt/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Mathias Gehrig 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /rvt/callbacks/custom.py: -------------------------------------------------------------------------------- 1 | from omegaconf import DictConfig 2 | from pytorch_lightning.callbacks import Callback 3 | from pytorch_lightning.callbacks import ModelCheckpoint 4 | 5 | from callbacks.detection import DetectionVizCallback 6 | 7 | 8 | def get_ckpt_callback(config: DictConfig) -> ModelCheckpoint: 9 | model_name = config.model.name 10 | 11 | prefix = 'val' 12 | if model_name == 'rnndet': 13 | metric = 'AP' 14 | mode = 'max' 15 | else: 16 | raise NotImplementedError 17 | ckpt_callback_monitor = prefix + '/' + metric 18 | filename_monitor_str = prefix + '_' + metric 19 | 20 | ckpt_filename = 'epoch={epoch:03d}-step={step}-' + filename_monitor_str + '={' + ckpt_callback_monitor + ':.2f}' 21 | 22 | 23 | 24 | cktp_callback = ModelCheckpoint( 25 | monitor=ckpt_callback_monitor, 26 | filename=ckpt_filename, 27 | auto_insert_metric_name=False, # because backslash would create a directory 28 | save_top_k=20, 29 | mode=mode, 30 | every_n_epochs=config.logging.ckpt_every_n_epochs, 31 | # save_on_train_epoch_end=True, 32 | save_last=True, 33 | verbose=True) 34 | 35 | cktp_callback.CHECKPOINT_NAME_LAST = 'last_epoch={epoch:03d}-step={step}' 36 | return cktp_callback 37 | 38 | 39 | def get_viz_callback(config: DictConfig) -> Callback: 40 | model_name = config.model.name 41 | 42 | if model_name == 'rnndet': 43 | return DetectionVizCallback(config=config) 44 | raise NotImplementedError 45 | -------------------------------------------------------------------------------- /rvt/callbacks/gradflow.py: -------------------------------------------------------------------------------- 1 | from typing import Any 2 | 3 | import pytorch_lightning as pl 4 | from pytorch_lightning.callbacks import Callback 5 | from pytorch_lightning.utilities.rank_zero import rank_zero_only 6 | 7 | from callbacks.utils.visualization import get_grad_flow_figure 8 | 9 | 10 | class GradFlowLogCallback(Callback): 11 | def __init__(self, log_every_n_train_steps: int): 12 | super().__init__() 13 | assert log_every_n_train_steps > 0 14 | self.log_every_n_train_steps = log_every_n_train_steps 15 | 16 | @rank_zero_only 17 | def on_before_zero_grad(self, trainer: pl.Trainer, pl_module: pl.LightningModule, optimizer: Any) -> None: 18 | # NOTE: before we had this in the on_after_backward callback. 19 | # This was fine for fp32 but showed unscaled gradients for fp16. 20 | # That is why we move it to on_before_zero_grad where gradients are scaled. 21 | global_step = trainer.global_step 22 | if global_step % self.log_every_n_train_steps != 0: 23 | return 24 | named_parameters = pl_module.named_parameters() 25 | figure = get_grad_flow_figure(named_parameters) 26 | trainer.logger.log_metrics({'train/gradients': figure}, step=global_step) 27 | -------------------------------------------------------------------------------- /rvt/callbacks/utils/visualization.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import plotly.express as px 3 | 4 | 5 | def get_grad_flow_figure(named_params): 6 | """Creates figure to visualize gradients flowing through different layers in the net during training. 7 | Can be used for checking for possible gradient vanishing / exploding problems. 8 | Usage: Use this function after loss.backwards() 9 | """ 10 | data_dict = { 11 | 'name': list(), 12 | 'grad_abs': list(), 13 | } 14 | for name, param in named_params: 15 | if param.requires_grad and param.grad is not None: 16 | grad_abs = param.grad.abs() 17 | data_dict['name'].append(name) 18 | data_dict['grad_abs'].append(grad_abs.mean().cpu().item()) 19 | 20 | data_frame = pd.DataFrame.from_dict(data_dict) 21 | 22 | fig = px.bar(data_frame, x='name', y='grad_abs') 23 | return fig 24 | -------------------------------------------------------------------------------- /rvt/config/dataset/base.yaml: -------------------------------------------------------------------------------- 1 | name: ??? 2 | path: ??? 3 | train: 4 | sampling: 'mixed' # ('random', 'stream', 'mixed') 5 | random: 6 | weighted_sampling: False 7 | mixed: 8 | w_stream: 1 9 | w_random: 1 10 | eval: 11 | sampling: 'stream' 12 | data_augmentation: 13 | random: 14 | prob_hflip: 0.5 15 | rotate: 16 | prob: 0 17 | min_angle_deg: 2 18 | max_angle_deg: 6 19 | zoom: 20 | prob: 0.8 21 | zoom_in: 22 | weight: 8 23 | factor: 24 | min: 1 25 | max: 1.5 26 | zoom_out: 27 | weight: 2 28 | factor: 29 | min: 1 30 | max: 1.2 31 | stream: 32 | prob_hflip: 0.5 33 | rotate: 34 | prob: 0 35 | min_angle_deg: 2 36 | max_angle_deg: 6 37 | zoom: 38 | prob: 0.5 39 | zoom_out: 40 | factor: 41 | min: 1 42 | max: 1.2 -------------------------------------------------------------------------------- /rvt/config/dataset/etrap.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base 3 | 4 | name: etrap 5 | ev_repr_name: 'stacked_histogram_dt=50_nbins=10' 6 | sequence_length: 5 7 | resolution_hw: [720, 1280] 8 | downsample_by_factor_2: True 9 | only_load_end_labels: False -------------------------------------------------------------------------------- /rvt/config/dataset/gen1.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base 3 | 4 | name: gen1 5 | ev_repr_name: 'stacked_histogram_dt=50_nbins=10' 6 | sequence_length: 11 7 | resolution_hw: [240, 304] 8 | downsample_by_factor_2: False 9 | only_load_end_labels: False -------------------------------------------------------------------------------- /rvt/config/dataset/gen4.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base 3 | 4 | name: gen4 5 | ev_repr_name: 'stacked_histogram_dt=50_nbins=10' 6 | sequence_length: 5 7 | resolution_hw: [720, 1280] 8 | downsample_by_factor_2: True 9 | only_load_end_labels: False -------------------------------------------------------------------------------- /rvt/config/experiment/gen1/base.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - default 4 | 5 | model: 6 | backbone: 7 | embed_dim: 64 8 | fpn: 9 | depth: 0.67 10 | -------------------------------------------------------------------------------- /rvt/config/experiment/gen1/default.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - /model/maxvit_yolox: default 4 | 5 | training: 6 | precision: 16 7 | max_epochs: 10000 8 | max_steps: 400000 9 | learning_rate: 0.0002 10 | lr_scheduler: 11 | use: True 12 | total_steps: ${..max_steps} 13 | pct_start: 0.005 14 | div_factor: 20 15 | final_div_factor: 10000 16 | validation: 17 | val_check_interval: null 18 | check_val_every_n_epoch: 1 19 | batch_size: 20 | train: 8 21 | eval: 8 22 | hardware: 23 | num_workers: 24 | train: 6 25 | eval: 2 26 | dataset: 27 | train: 28 | sampling: 'mixed' 29 | random: 30 | weighted_sampling: False 31 | mixed: 32 | w_stream: 1 33 | w_random: 1 34 | eval: 35 | sampling: 'stream' 36 | ev_repr_name: 'stacked_histogram_dt=50_nbins=10' 37 | sequence_length: 21 38 | downsample_by_factor_2: False 39 | only_load_end_labels: False 40 | model: 41 | backbone: 42 | partition_split_32: 1 -------------------------------------------------------------------------------- /rvt/config/experiment/gen1/small.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - default 4 | 5 | model: 6 | backbone: 7 | embed_dim: 48 8 | stage: 9 | attention: 10 | dim_head: 24 11 | fpn: 12 | depth: 0.33 -------------------------------------------------------------------------------- /rvt/config/experiment/gen1/tiny.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - default 4 | 5 | model: 6 | backbone: 7 | embed_dim: 32 8 | fpn: 9 | depth: 0.33 -------------------------------------------------------------------------------- /rvt/config/experiment/gen4/base.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - default 4 | 5 | model: 6 | backbone: 7 | embed_dim: 64 8 | fpn: 9 | depth: 0.67 10 | -------------------------------------------------------------------------------- /rvt/config/experiment/gen4/default.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - /model/maxvit_yolox: default 4 | 5 | training: 6 | precision: 16 7 | max_epochs: 10000 8 | max_steps: 400000000 9 | learning_rate: 0.000035 # 0.000346 10 | lr_scheduler: 11 | use: True 12 | total_steps: ${..max_steps} 13 | pct_start: 0.005 14 | div_factor: 20 15 | final_div_factor: 10000 16 | validation: 17 | val_check_interval: null 18 | check_val_every_n_epoch: 1 19 | batch_size: 20 | train: 12 21 | eval: 12 22 | hardware: 23 | num_workers: 24 | train: 1 25 | eval: 1 26 | dataset: 27 | train: 28 | sampling: 'mixed' 29 | random: 30 | weighted_sampling: False 31 | mixed: 32 | w_stream: 1 33 | w_random: 1 34 | eval: 35 | sampling: 'stream' 36 | ev_repr_name: 'stacked_histogram_dt=50_nbins=10' 37 | sequence_length: 5 38 | downsample_by_factor_2: True 39 | only_load_end_labels: False 40 | -------------------------------------------------------------------------------- /rvt/config/experiment/gen4/small.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - default 4 | 5 | model: 6 | backbone: 7 | embed_dim: 48 8 | stage: 9 | attention: 10 | dim_head: 24 11 | fpn: 12 | depth: 0.33 -------------------------------------------------------------------------------- /rvt/config/experiment/gen4/tiny.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - default 4 | 5 | model: 6 | backbone: 7 | embed_dim: 32 8 | fpn: 9 | depth: 0.33 -------------------------------------------------------------------------------- /rvt/config/model/base.yaml: -------------------------------------------------------------------------------- 1 | name: ??? -------------------------------------------------------------------------------- /rvt/config/model/maxvit_yolox/default.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - override /model: rnndet 4 | 5 | model: 6 | backbone: 7 | name: MaxViTRNN 8 | compile: 9 | enable: False 10 | args: 11 | mode: reduce-overhead 12 | input_channels: 20 13 | enable_masking: False 14 | partition_split_32: 2 15 | embed_dim: 64 16 | dim_multiplier: [1, 2, 4, 8] 17 | num_blocks: [1, 1, 1, 1] 18 | T_max_chrono_init: [4, 8, 16, 32] 19 | stem: 20 | patch_size: 4 21 | stage: 22 | downsample: 23 | type: patch 24 | overlap: True 25 | norm_affine: True 26 | attention: 27 | use_torch_mha: False 28 | partition_size: ??? 29 | dim_head: 32 30 | attention_bias: True 31 | mlp_activation: gelu 32 | mlp_gated: False 33 | mlp_bias: True 34 | mlp_ratio: 4 35 | drop_mlp: 0 36 | drop_path: 0 37 | ls_init_value: 1e-5 38 | lstm: 39 | dws_conv: False 40 | dws_conv_only_hidden: True 41 | dws_conv_kernel_size: 3 42 | drop_cell_update: 0 43 | fpn: 44 | name: PAFPN 45 | compile: 46 | enable: False 47 | args: 48 | mode: reduce-overhead 49 | depth: 0.67 # round(depth * 3) == num bottleneck blocks 50 | # stage 1 is the first and len(num_layers) is the last 51 | in_stages: [2, 3, 4] 52 | depthwise: False 53 | act: "silu" 54 | head: 55 | name: YoloX 56 | compile: 57 | enable: False 58 | args: 59 | mode: reduce-overhead 60 | depthwise: False 61 | act: "silu" 62 | postprocess: 63 | confidence_threshold: 0.1 64 | nms_threshold: 0.45 65 | -------------------------------------------------------------------------------- /rvt/config/model/rnndet.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base 3 | 4 | name: rnndet 5 | backbone: 6 | name: ??? 7 | fpn: 8 | name: ??? 9 | head: 10 | name: ??? 11 | postprocess: 12 | confidence_threshold: 0.1 13 | nms_threshold: 0.45 -------------------------------------------------------------------------------- /rvt/config/train.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - general 3 | - dataset: ??? 4 | - model: rnndet 5 | - optional model/dataset: ${model}_${dataset} -------------------------------------------------------------------------------- /rvt/config/val.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - dataset: ??? 3 | - model: rnndet 4 | - _self_ 5 | 6 | checkpoint: ??? 7 | use_test_set: False 8 | hardware: 9 | num_workers: 10 | eval: 4 11 | gpus: 0 # GPU idx (multi-gpu not supported for validation) 12 | batch_size: 13 | eval: 8 14 | training: 15 | precision: 16 16 | -------------------------------------------------------------------------------- /rvt/data/genx_utils/collate.py: -------------------------------------------------------------------------------- 1 | from copy import deepcopy 2 | from typing import Any, Callable, Dict, Optional, Type, Tuple, Union 3 | 4 | import torch 5 | 6 | from data.genx_utils.collate_from_pytorch import collate, default_collate_fn_map 7 | from data.genx_utils.labels import ObjectLabels, SparselyBatchedObjectLabels 8 | 9 | 10 | def collate_object_labels(batch, *, collate_fn_map: Optional[Dict[Union[Type, Tuple[Type, ...]], Callable]] = None): 11 | return batch 12 | 13 | 14 | def collate_sparsely_batched_object_labels(batch, *, collate_fn_map: Optional[ 15 | Dict[Union[Type, Tuple[Type, ...]], Callable]] = None): 16 | return SparselyBatchedObjectLabels.transpose_list(batch) 17 | 18 | 19 | custom_collate_fn_map = deepcopy(default_collate_fn_map) 20 | custom_collate_fn_map[ObjectLabels] = collate_object_labels 21 | custom_collate_fn_map[SparselyBatchedObjectLabels] = collate_sparsely_batched_object_labels 22 | 23 | 24 | def custom_collate(batch: Any): 25 | return collate(batch, collate_fn_map=custom_collate_fn_map) 26 | 27 | 28 | def custom_collate_rnd(batch: Any): 29 | samples = batch 30 | # NOTE: We do not really need the worker id for map style datasets (rnd) but we still provide the id for consistency 31 | worker_info = torch.utils.data.get_worker_info() 32 | local_worker_id = 0 if worker_info is None else worker_info.id 33 | return { 34 | 'data': custom_collate(samples), 35 | 'worker_id': local_worker_id, 36 | } 37 | 38 | 39 | def custom_collate_streaming(batch: Any): 40 | """We assume that we receive a batch collected by a worker of our streaming datapipe 41 | """ 42 | samples = batch[0] 43 | worker_id = batch[1] 44 | assert isinstance(worker_id, int) 45 | return { 46 | 'data': custom_collate(samples), 47 | 'worker_id': worker_id, 48 | } 49 | -------------------------------------------------------------------------------- /rvt/data/utils/spatial.py: -------------------------------------------------------------------------------- 1 | from omegaconf import DictConfig 2 | 3 | from data.utils.types import DatasetType 4 | 5 | _type_2_hw = { 6 | DatasetType.GEN1: (240, 304), 7 | DatasetType.GEN4: (720, 1280), 8 | } 9 | 10 | _str_2_type = { 11 | 'gen1': DatasetType.GEN1, 12 | 'gen4': DatasetType.GEN4, 13 | } 14 | 15 | 16 | def get_original_hw(dataset_type: DatasetType): 17 | return _type_2_hw[dataset_type] 18 | 19 | 20 | def get_dataloading_hw(dataset_config: DictConfig): 21 | dataset_name = dataset_config.name 22 | hw = get_original_hw(dataset_type=_str_2_type[dataset_name]) 23 | downsample_by_factor_2 = dataset_config.downsample_by_factor_2 24 | if downsample_by_factor_2: 25 | hw = tuple(x // 2 for x in hw) 26 | return hw 27 | -------------------------------------------------------------------------------- /rvt/data/utils/types.py: -------------------------------------------------------------------------------- 1 | from enum import auto, Enum 2 | 3 | try: 4 | from enum import StrEnum 5 | except ImportError: 6 | from strenum import StrEnum 7 | from typing import Dict, List, Optional, Tuple, Union 8 | 9 | import torch as th 10 | 11 | from data.genx_utils.labels import ObjectLabels, SparselyBatchedObjectLabels 12 | 13 | 14 | class DataType(Enum): 15 | EV_REPR = auto() 16 | FLOW = auto() 17 | IMAGE = auto() 18 | OBJLABELS = auto() 19 | OBJLABELS_SEQ = auto() 20 | IS_PADDED_MASK = auto() 21 | IS_FIRST_SAMPLE = auto() 22 | TOKEN_MASK = auto() 23 | 24 | 25 | class DatasetType(Enum): 26 | GEN1 = auto() 27 | GEN4 = auto() 28 | 29 | 30 | class DatasetMode(Enum): 31 | TRAIN = auto() 32 | VALIDATION = auto() 33 | TESTING = auto() 34 | 35 | 36 | class DatasetSamplingMode(StrEnum): 37 | RANDOM = 'random' 38 | STREAM = 'stream' 39 | MIXED = 'mixed' 40 | 41 | 42 | class ObjDetOutput(Enum): 43 | LABELS_PROPH = auto() 44 | PRED_PROPH = auto() 45 | EV_REPR = auto() 46 | SKIP_VIZ = auto() 47 | 48 | 49 | LoaderDataDictGenX = Dict[DataType, Union[List[th.Tensor], ObjectLabels, SparselyBatchedObjectLabels, List[bool]]] 50 | 51 | LstmState = Optional[Tuple[th.Tensor, th.Tensor]] 52 | LstmStates = List[LstmState] 53 | 54 | FeatureMap = th.Tensor 55 | BackboneFeatures = Dict[int, th.Tensor] 56 | -------------------------------------------------------------------------------- /rvt/gen4_class_labels.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import glob 4 | 5 | """ 6 | eTraP annotations 7 | { 8 | "0": "pedestrian", 9 | "1": "car", 10 | "2": "bicycle", 11 | "3": "bus", 12 | "4": "motorbike", 13 | "5": "truck", 14 | "6": "tram", 15 | "7": "wheelchair" 16 | } 17 | ​ 18 | 1 Megapixel annotations 19 | pedestrian, two wheeler, car, truck, bus, traffic sign, traffic light 20 | """ 21 | CONVERT_LABELS_TO_1MP_ORDER = { 22 | 0: 0, 23 | 1: 2, 24 | 2: 1, 25 | 3: 4, 26 | 4: 1, 27 | 5: 3, 28 | 6: 5, 29 | 7: 6 30 | } 31 | 32 | def mapping_labels(npy_file): 33 | original_npy = np.load(npy_file) 34 | converted_npy = np.copy(original_npy) 35 | 36 | # print(converted_npy[0:10]) 37 | save_path = '/home/arpit/caram_air_ws/datasets/etrap_gen4/gen4_train_labels/' + npy_file.split('/')[-1] 38 | 39 | new_labels = [CONVERT_LABELS_TO_1MP_ORDER[x] for x in original_npy['class_id']] 40 | converted_npy['class_id'] = new_labels 41 | print(np.bincount(original_npy['class_id'])) 42 | print(np.bincount(converted_npy['class_id'])) 43 | 44 | print(converted_npy[0:10]) 45 | print('NEW') 46 | np.save(save_path, converted_npy) 47 | 48 | if __name__ == "__main__": 49 | annotations_folder = "/home/arpit/caram_air_ws/datasets/etrap_gen4/train_labels" 50 | npy_files = glob.glob(os.path.join(annotations_folder, '*.npy')) 51 | 52 | # npy_files = ['/media/exx/data/aayush/RED/pretrained/eval/recording_2023-08-24_16-03-57_bbox.npy'] 53 | for file in npy_files: 54 | mapping_labels(file) -------------------------------------------------------------------------------- /rvt/loggers/utils.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | from typing import Union 3 | 4 | import wandb 5 | from omegaconf import DictConfig, OmegaConf 6 | 7 | from loggers.wandb_logger import WandbLogger 8 | 9 | 10 | def get_wandb_logger(full_config: DictConfig) -> WandbLogger: 11 | wandb_config = full_config.wandb 12 | wandb_runpath = wandb_config.wandb_runpath 13 | 14 | if wandb_runpath is None: 15 | wandb_id = wandb.util.generate_id() 16 | print(f'new run: generating id {wandb_id}') 17 | else: 18 | wandb_id = Path(wandb_runpath).name 19 | print(f'using provided id {wandb_id}') 20 | 21 | full_config_dict = OmegaConf.to_container(full_config, resolve=True, throw_on_missing=True) 22 | logger = WandbLogger( 23 | project=wandb_config.project_name, 24 | group=wandb_config.group_name, 25 | wandb_id=wandb_id, 26 | log_model=True, 27 | save_last_only_final=False, 28 | save_code=True, 29 | config_args=full_config_dict, 30 | ) 31 | 32 | return logger 33 | 34 | 35 | def get_ckpt_path(logger: WandbLogger, wandb_config: DictConfig) -> Union[Path, None]: 36 | cfg = wandb_config 37 | artifact_name = cfg.artifact_name 38 | assert artifact_name is not None, 'Artifact name is required to resume from checkpoint.' 39 | print(f'resuming checkpoint from artifact {artifact_name}') 40 | artifact_local_file = cfg.artifact_local_file 41 | if artifact_local_file is not None: 42 | artifact_local_file = Path(artifact_local_file) 43 | if isinstance(logger, WandbLogger): 44 | resume_path = logger.get_checkpoint( 45 | artifact_name=artifact_name, 46 | artifact_filepath=artifact_local_file) 47 | else: 48 | resume_path = artifact_local_file 49 | assert resume_path.exists() 50 | assert resume_path.suffix == '.ckpt', resume_path.suffix 51 | return resume_path 52 | -------------------------------------------------------------------------------- /rvt/models/detection/__init_.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/models/detection/__init_.py -------------------------------------------------------------------------------- /rvt/models/detection/recurrent_backbone/__init__.py: -------------------------------------------------------------------------------- 1 | from omegaconf import DictConfig 2 | 3 | from .maxvit_rnn import RNNDetector as MaxViTRNNDetector 4 | 5 | 6 | def build_recurrent_backbone(backbone_cfg: DictConfig): 7 | name = backbone_cfg.name 8 | if name == 'MaxViTRNN': 9 | return MaxViTRNNDetector(backbone_cfg) 10 | else: 11 | raise NotImplementedError 12 | -------------------------------------------------------------------------------- /rvt/models/detection/recurrent_backbone/base.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple 2 | 3 | import torch.nn as nn 4 | 5 | 6 | class BaseDetector(nn.Module): 7 | def get_stage_dims(self, stages: Tuple[int, ...]) -> Tuple[int, ...]: 8 | raise NotImplementedError 9 | 10 | def get_strides(self, stages: Tuple[int, ...]) -> Tuple[int, ...]: 11 | raise NotImplementedError 12 | -------------------------------------------------------------------------------- /rvt/models/detection/yolox/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/models/detection/yolox/models/__init__.py -------------------------------------------------------------------------------- /rvt/models/detection/yolox/models/losses.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- encoding: utf-8 -*- 3 | # Copyright (c) Megvii Inc. All rights reserved. 4 | 5 | import torch 6 | import torch.nn as nn 7 | 8 | 9 | class IOUloss(nn.Module): 10 | def __init__(self, reduction="none", loss_type="iou"): 11 | super(IOUloss, self).__init__() 12 | self.reduction = reduction 13 | self.loss_type = loss_type 14 | 15 | def forward(self, pred, target): 16 | assert pred.shape[0] == target.shape[0] 17 | 18 | pred = pred.view(-1, 4) 19 | target = target.view(-1, 4) 20 | tl = torch.max( 21 | (pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2) 22 | ) 23 | br = torch.min( 24 | (pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2) 25 | ) 26 | 27 | area_p = torch.prod(pred[:, 2:], 1) 28 | area_g = torch.prod(target[:, 2:], 1) 29 | 30 | en = (tl < br).type(tl.type()).prod(dim=1) 31 | area_i = torch.prod(br - tl, 1) * en 32 | area_u = area_p + area_g - area_i 33 | iou = (area_i) / (area_u + 1e-16) 34 | 35 | if self.loss_type == "iou": 36 | loss = 1 - iou ** 2 37 | elif self.loss_type == "giou": 38 | c_tl = torch.min( 39 | (pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2) 40 | ) 41 | c_br = torch.max( 42 | (pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2) 43 | ) 44 | area_c = torch.prod(c_br - c_tl, 1) 45 | giou = iou - (area_c - area_u) / area_c.clamp(1e-16) 46 | loss = 1 - giou.clamp(min=-1.0, max=1.0) 47 | else: 48 | raise NotImplementedError 49 | 50 | if self.reduction == "mean": 51 | loss = loss.mean() 52 | elif self.reduction == "sum": 53 | loss = loss.sum() 54 | 55 | return loss 56 | -------------------------------------------------------------------------------- /rvt/models/detection/yolox/utils/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding:utf-8 -*- 3 | # Copyright (c) Megvii Inc. All rights reserved. 4 | 5 | from .boxes import * 6 | from .compat import meshgrid 7 | -------------------------------------------------------------------------------- /rvt/models/detection/yolox/utils/compat.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding:utf-8 -*- 3 | 4 | import torch 5 | 6 | _TORCH_VER = [int(x) for x in torch.__version__.split(".")[:2]] 7 | 8 | __all__ = ["meshgrid"] 9 | 10 | 11 | def meshgrid(*tensors): 12 | if _TORCH_VER >= [1, 10]: 13 | return torch.meshgrid(*tensors, indexing="ij") 14 | else: 15 | return torch.meshgrid(*tensors) 16 | -------------------------------------------------------------------------------- /rvt/models/detection/yolox_extension/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/models/detection/yolox_extension/models/__init__.py -------------------------------------------------------------------------------- /rvt/models/detection/yolox_extension/models/build.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple 2 | 3 | from omegaconf import OmegaConf, DictConfig 4 | 5 | from .yolo_pafpn import YOLOPAFPN 6 | from ...yolox.models.yolo_head import YOLOXHead 7 | 8 | 9 | def build_yolox_head(head_cfg: DictConfig, in_channels: Tuple[int, ...], strides: Tuple[int, ...]): 10 | head_cfg_dict = OmegaConf.to_container(head_cfg, resolve=True, throw_on_missing=True) 11 | head_cfg_dict.pop('name') 12 | head_cfg_dict.pop('version', None) 13 | head_cfg_dict.update({"in_channels": in_channels}) 14 | head_cfg_dict.update({"strides": strides}) 15 | compile_cfg = head_cfg_dict.pop('compile', None) 16 | head_cfg_dict.update({"compile_cfg": compile_cfg}) 17 | return YOLOXHead(**head_cfg_dict) 18 | 19 | 20 | def build_yolox_fpn(fpn_cfg: DictConfig, in_channels: Tuple[int, ...]): 21 | fpn_cfg_dict = OmegaConf.to_container(fpn_cfg, resolve=True, throw_on_missing=True) 22 | fpn_name = fpn_cfg_dict.pop('name') 23 | fpn_cfg_dict.update({"in_channels": in_channels}) 24 | if fpn_name in {'PAFPN', 'pafpn'}: 25 | compile_cfg = fpn_cfg_dict.pop('compile', None) 26 | fpn_cfg_dict.update({"compile_cfg": compile_cfg}) 27 | return YOLOPAFPN(**fpn_cfg_dict) 28 | raise NotImplementedError 29 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/models/layers/maxvit/__init__.py -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/blur_pool.py: -------------------------------------------------------------------------------- 1 | """ 2 | BlurPool layer inspired by 3 | - Kornia's Max_BlurPool2d 4 | - Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar` 5 | 6 | Hacked together by Chris Ha and Ross Wightman 7 | """ 8 | 9 | import torch 10 | import torch.nn as nn 11 | import torch.nn.functional as F 12 | import numpy as np 13 | from .padding import get_padding 14 | 15 | 16 | class BlurPool2d(nn.Module): 17 | r"""Creates a module that computes blurs and downsample a given feature map. 18 | See :cite:`zhang2019shiftinvar` for more details. 19 | Corresponds to the Downsample class, which does blurring and subsampling 20 | 21 | Args: 22 | channels = Number of input channels 23 | filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5. 24 | stride (int): downsampling filter stride 25 | 26 | Returns: 27 | torch.Tensor: the transformed tensor. 28 | """ 29 | def __init__(self, channels, filt_size=3, stride=2) -> None: 30 | super(BlurPool2d, self).__init__() 31 | assert filt_size > 1 32 | self.channels = channels 33 | self.filt_size = filt_size 34 | self.stride = stride 35 | self.padding = [get_padding(filt_size, stride, dilation=1)] * 4 36 | coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32)) 37 | blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1) 38 | self.register_buffer('filt', blur_filter, persistent=False) 39 | 40 | def forward(self, x: torch.Tensor) -> torch.Tensor: 41 | x = F.pad(x, self.padding, 'reflect') 42 | return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels) 43 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/conv2d_same.py: -------------------------------------------------------------------------------- 1 | """ Conv2d w/ Same Padding 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.functional as F 8 | from typing import Tuple, Optional 9 | 10 | from .padding import pad_same, get_padding_value 11 | 12 | 13 | def conv2d_same( 14 | x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1), 15 | padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1): 16 | x = pad_same(x, weight.shape[-2:], stride, dilation) 17 | return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups) 18 | 19 | 20 | class Conv2dSame(nn.Conv2d): 21 | """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions 22 | """ 23 | 24 | def __init__(self, in_channels, out_channels, kernel_size, stride=1, 25 | padding=0, dilation=1, groups=1, bias=True): 26 | super(Conv2dSame, self).__init__( 27 | in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) 28 | 29 | def forward(self, x): 30 | return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) 31 | 32 | 33 | def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs): 34 | padding = kwargs.pop('padding', '') 35 | kwargs.setdefault('bias', False) 36 | padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs) 37 | if is_dynamic: 38 | return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs) 39 | else: 40 | return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs) 41 | 42 | 43 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/create_conv2d.py: -------------------------------------------------------------------------------- 1 | """ Create Conv2d Factory Method 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | 6 | from .mixed_conv2d import MixedConv2d 7 | from .cond_conv2d import CondConv2d 8 | from .conv2d_same import create_conv2d_pad 9 | 10 | 11 | def create_conv2d(in_channels, out_channels, kernel_size, **kwargs): 12 | """ Select a 2d convolution implementation based on arguments 13 | Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d. 14 | 15 | Used extensively by EfficientNet, MobileNetv3 and related networks. 16 | """ 17 | if isinstance(kernel_size, list): 18 | assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently 19 | if 'groups' in kwargs: 20 | groups = kwargs.pop('groups') 21 | if groups == in_channels: 22 | kwargs['depthwise'] = True 23 | else: 24 | assert groups == 1 25 | # We're going to use only lists for defining the MixedConv2d kernel groups, 26 | # ints, tuples, other iterables will continue to pass to normal conv and specify h, w. 27 | m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs) 28 | else: 29 | depthwise = kwargs.pop('depthwise', False) 30 | # for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0 31 | groups = in_channels if depthwise else kwargs.pop('groups', 1) 32 | if 'num_experts' in kwargs and kwargs['num_experts'] > 0: 33 | m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs) 34 | else: 35 | m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs) 36 | return m 37 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/helpers.py: -------------------------------------------------------------------------------- 1 | """ Layer/Module Helpers 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | from itertools import repeat 6 | import collections.abc 7 | 8 | 9 | # From PyTorch internals 10 | def _ntuple(n): 11 | def parse(x): 12 | if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): 13 | return x 14 | return tuple(repeat(x, n)) 15 | return parse 16 | 17 | 18 | to_1tuple = _ntuple(1) 19 | to_2tuple = _ntuple(2) 20 | to_3tuple = _ntuple(3) 21 | to_4tuple = _ntuple(4) 22 | to_ntuple = _ntuple 23 | 24 | 25 | def make_divisible(v, divisor=8, min_value=None, round_limit=.9): 26 | min_value = min_value or divisor 27 | new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) 28 | # Make sure that round down does not go down by more than 10%. 29 | if new_v < round_limit * v: 30 | new_v += divisor 31 | return new_v 32 | 33 | 34 | def extend_tuple(x, n): 35 | # pdas a tuple to specified n by padding with last value 36 | if not isinstance(x, (tuple, list)): 37 | x = (x,) 38 | else: 39 | x = tuple(x) 40 | pad_n = n - len(x) 41 | if pad_n <= 0: 42 | return x[:n] 43 | return x + (x[-1],) * pad_n 44 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/linear.py: -------------------------------------------------------------------------------- 1 | """ Linear layer (alternate definition) 2 | """ 3 | import torch 4 | import torch.nn.functional as F 5 | from torch import nn as nn 6 | 7 | 8 | class Linear(nn.Linear): 9 | r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` 10 | 11 | Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting 12 | weight & bias to input.dtype to work around an issue w/ torch.addmm in this use case. 13 | """ 14 | def forward(self, input: torch.Tensor) -> torch.Tensor: 15 | if torch.jit.is_scripting(): 16 | bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None 17 | return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias) 18 | else: 19 | return F.linear(input, self.weight, self.bias) 20 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/median_pool.py: -------------------------------------------------------------------------------- 1 | """ Median Pool 2 | Hacked together by / Copyright 2020 Ross Wightman 3 | """ 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | from .helpers import to_2tuple, to_4tuple 7 | 8 | 9 | class MedianPool2d(nn.Module): 10 | """ Median pool (usable as median filter when stride=1) module. 11 | 12 | Args: 13 | kernel_size: size of pooling kernel, int or 2-tuple 14 | stride: pool stride, int or 2-tuple 15 | padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad 16 | same: override padding and enforce same padding, boolean 17 | """ 18 | def __init__(self, kernel_size=3, stride=1, padding=0, same=False): 19 | super(MedianPool2d, self).__init__() 20 | self.k = to_2tuple(kernel_size) 21 | self.stride = to_2tuple(stride) 22 | self.padding = to_4tuple(padding) # convert to l, r, t, b 23 | self.same = same 24 | 25 | def _padding(self, x): 26 | if self.same: 27 | ih, iw = x.size()[2:] 28 | if ih % self.stride[0] == 0: 29 | ph = max(self.k[0] - self.stride[0], 0) 30 | else: 31 | ph = max(self.k[0] - (ih % self.stride[0]), 0) 32 | if iw % self.stride[1] == 0: 33 | pw = max(self.k[1] - self.stride[1], 0) 34 | else: 35 | pw = max(self.k[1] - (iw % self.stride[1]), 0) 36 | pl = pw // 2 37 | pr = pw - pl 38 | pt = ph // 2 39 | pb = ph - pt 40 | padding = (pl, pr, pt, pb) 41 | else: 42 | padding = self.padding 43 | return padding 44 | 45 | def forward(self, x): 46 | x = F.pad(x, self._padding(x), mode='reflect') 47 | x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1]) 48 | x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] 49 | return x 50 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/patch_embed.py: -------------------------------------------------------------------------------- 1 | """ Image to Patch Embedding using Conv2d 2 | 3 | A convolution based approach to patchifying a 2D image w/ embedding projection. 4 | 5 | Based on the impl in https://github.com/google-research/vision_transformer 6 | 7 | Hacked together by / Copyright 2020 Ross Wightman 8 | """ 9 | from torch import nn as nn 10 | 11 | from .helpers import to_2tuple 12 | from .trace_utils import _assert 13 | 14 | 15 | class PatchEmbed(nn.Module): 16 | """ 2D Image to Patch Embedding 17 | """ 18 | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): 19 | super().__init__() 20 | img_size = to_2tuple(img_size) 21 | patch_size = to_2tuple(patch_size) 22 | self.img_size = img_size 23 | self.patch_size = patch_size 24 | self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) 25 | self.num_patches = self.grid_size[0] * self.grid_size[1] 26 | self.flatten = flatten 27 | 28 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) 29 | self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() 30 | 31 | def forward(self, x): 32 | B, C, H, W = x.shape 33 | _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") 34 | _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") 35 | x = self.proj(x) 36 | if self.flatten: 37 | x = x.flatten(2).transpose(1, 2) # BCHW -> BNC 38 | x = self.norm(x) 39 | return x 40 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/space_to_depth.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class SpaceToDepth(nn.Module): 6 | def __init__(self, block_size=4): 7 | super().__init__() 8 | assert block_size == 4 9 | self.bs = block_size 10 | 11 | def forward(self, x): 12 | N, C, H, W = x.size() 13 | x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs) # (N, C, H//bs, bs, W//bs, bs) 14 | x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs) 15 | x = x.view(N, C * (self.bs ** 2), H // self.bs, W // self.bs) # (N, C*bs^2, H//bs, W//bs) 16 | return x 17 | 18 | 19 | @torch.jit.script 20 | class SpaceToDepthJit(object): 21 | def __call__(self, x: torch.Tensor): 22 | # assuming hard-coded that block_size==4 for acceleration 23 | N, C, H, W = x.size() 24 | x = x.view(N, C, H // 4, 4, W // 4, 4) # (N, C, H//bs, bs, W//bs, bs) 25 | x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs) 26 | x = x.view(N, C * 16, H // 4, W // 4) # (N, C*bs^2, H//bs, W//bs) 27 | return x 28 | 29 | 30 | class SpaceToDepthModule(nn.Module): 31 | def __init__(self, no_jit=False): 32 | super().__init__() 33 | if not no_jit: 34 | self.op = SpaceToDepthJit() 35 | else: 36 | self.op = SpaceToDepth() 37 | 38 | def forward(self, x): 39 | return self.op(x) 40 | 41 | 42 | class DepthToSpace(nn.Module): 43 | 44 | def __init__(self, block_size): 45 | super().__init__() 46 | self.bs = block_size 47 | 48 | def forward(self, x): 49 | N, C, H, W = x.size() 50 | x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W) # (N, bs, bs, C//bs^2, H, W) 51 | x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # (N, C//bs^2, H, bs, W, bs) 52 | x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs) # (N, C//bs^2, H * bs, W * bs) 53 | return x 54 | -------------------------------------------------------------------------------- /rvt/models/layers/maxvit/layers/trace_utils.py: -------------------------------------------------------------------------------- 1 | try: 2 | from torch import _assert 3 | except ImportError: 4 | def _assert(condition: bool, message: str): 5 | assert condition, message 6 | 7 | 8 | def _float_to_int(x: float) -> int: 9 | """ 10 | Symbolic tracing helper to substitute for inbuilt `int`. 11 | Hint: Inbuilt `int` can't accept an argument of type `Proxy` 12 | """ 13 | return int(x) 14 | -------------------------------------------------------------------------------- /rvt/modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/modules/__init__.py -------------------------------------------------------------------------------- /rvt/modules/utils/fetch.py: -------------------------------------------------------------------------------- 1 | import pytorch_lightning as pl 2 | from omegaconf import DictConfig 3 | 4 | from modules.data.genx import DataModule as genx_data_module 5 | from modules.detection import Module as rnn_det_module 6 | 7 | 8 | def fetch_model_module(config: DictConfig) -> pl.LightningModule: 9 | model_str = config.model.name 10 | if model_str == 'rnndet': 11 | return rnn_det_module(config) 12 | raise NotImplementedError 13 | 14 | 15 | def fetch_data_module(config: DictConfig) -> pl.LightningDataModule: 16 | batch_size_train = config.batch_size.train 17 | batch_size_eval = config.batch_size.eval 18 | num_workers_generic = config.hardware.get('num_workers', None) 19 | num_workers_train = config.hardware.num_workers.get('train', num_workers_generic) 20 | num_workers_eval = config.hardware.num_workers.get('eval', num_workers_generic) 21 | dataset_str = config.dataset.name 22 | if dataset_str in {'gen1', 'gen4'}: 23 | return genx_data_module(config.dataset, 24 | num_workers_train=num_workers_train, 25 | num_workers_eval=num_workers_eval, 26 | batch_size_train=batch_size_train, 27 | batch_size_eval=batch_size_eval) 28 | raise NotImplementedError 29 | -------------------------------------------------------------------------------- /rvt/scripts/genx/conf_preprocess/extraction/const_count.yaml: -------------------------------------------------------------------------------- 1 | method: COUNT 2 | value: 50000 -------------------------------------------------------------------------------- /rvt/scripts/genx/conf_preprocess/extraction/const_duration.yaml: -------------------------------------------------------------------------------- 1 | method: DURATION 2 | # value is in milliseconds! 3 | value: 50 4 | -------------------------------------------------------------------------------- /rvt/scripts/genx/conf_preprocess/filter_gen1.yaml: -------------------------------------------------------------------------------- 1 | apply_psee_bbox_filter: True 2 | apply_faulty_bbox_filter: True -------------------------------------------------------------------------------- /rvt/scripts/genx/conf_preprocess/filter_gen4.yaml: -------------------------------------------------------------------------------- 1 | apply_psee_bbox_filter: False 2 | apply_faulty_bbox_filter: True -------------------------------------------------------------------------------- /rvt/scripts/genx/conf_preprocess/representation/mixeddensity_stack.yaml: -------------------------------------------------------------------------------- 1 | name: "mixeddensity_stack" 2 | nbins: 10 3 | count_cutoff: 32 4 | -------------------------------------------------------------------------------- /rvt/scripts/genx/conf_preprocess/representation/stacked_hist.yaml: -------------------------------------------------------------------------------- 1 | name: "stacked_histogram" 2 | nbins: 10 3 | count_cutoff: 10 4 | -------------------------------------------------------------------------------- /rvt/utils/evaluation/prophesee/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/utils/evaluation/prophesee/__init__.py -------------------------------------------------------------------------------- /rvt/utils/evaluation/prophesee/evaluation.py: -------------------------------------------------------------------------------- 1 | from .io.box_filtering import filter_boxes 2 | from .metrics.coco_eval import evaluate_detection 3 | 4 | 5 | def evaluate_list(result_boxes_list, 6 | gt_boxes_list, 7 | height: int, 8 | width: int, 9 | camera: str = 'gen1', 10 | apply_bbox_filters: bool = True, 11 | downsampled_by_2: bool = False, 12 | return_aps: bool = True): 13 | assert camera in {'gen1', 'gen4'} 14 | 15 | if camera == 'gen1': 16 | classes = ("car", "pedestrian") 17 | elif camera == 'gen4': 18 | classes = ("pedestrian", "two-wheeler", "car") 19 | elif camera == 'etrap': 20 | classes = ('pedestrian', 'car', 'bicycle') 21 | else: 22 | raise NotImplementedError 23 | 24 | if apply_bbox_filters: 25 | # Default values taken from: https://github.com/prophesee-ai/prophesee-automotive-dataset-toolbox/blob/0393adea2bf22d833893c8cb1d986fcbe4e6f82d/src/psee_evaluator.py#L23-L24 26 | min_box_diag = 60 if camera == 'gen4' else 30 27 | # In the supplementary mat, they say that min_box_side is 20 for gen4. 28 | min_box_side = 20 if camera == 'gen4' else 10 29 | if downsampled_by_2: 30 | assert min_box_diag % 2 == 0 31 | min_box_diag //= 2 32 | assert min_box_side % 2 == 0 33 | min_box_side //= 2 34 | 35 | half_sec_us = int(5e5) 36 | filter_boxes_fn = lambda x: filter_boxes(x, half_sec_us, min_box_diag, min_box_side) 37 | 38 | gt_boxes_list = map(filter_boxes_fn, gt_boxes_list) 39 | # NOTE: We also filter the prediction to follow the prophesee protocol of evaluation. 40 | result_boxes_list = map(filter_boxes_fn, result_boxes_list) 41 | 42 | return evaluate_detection(gt_boxes_list, result_boxes_list, 43 | height=height, width=width, 44 | classes=classes, return_aps=return_aps) 45 | -------------------------------------------------------------------------------- /rvt/utils/evaluation/prophesee/io/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/utils/evaluation/prophesee/io/__init__.py -------------------------------------------------------------------------------- /rvt/utils/evaluation/prophesee/io/box_filtering.py: -------------------------------------------------------------------------------- 1 | """ 2 | Define same filtering that we apply in: 3 | "Learning to detect objects on a 1 Megapixel Event Camera" by Etienne Perot et al. 4 | 5 | Namely we apply 2 different filters: 6 | 1. skip all boxes before 0.5s (before we assume it is unlikely you have sufficient historic) 7 | 2. filter all boxes whose diagonal <= min_box_diag**2 and whose side <= min_box_side 8 | 9 | 10 | 11 | Copyright: (c) 2019-2020 Prophesee 12 | """ 13 | from __future__ import print_function 14 | 15 | import numpy as np 16 | 17 | 18 | def filter_boxes(boxes, skip_ts=int(5e5), min_box_diag=60, min_box_side=20): 19 | """Filters boxes according to the paper rule. 20 | 21 | To note: the default represents our threshold when evaluating GEN4 resolution (1280x720) 22 | To note: we assume the initial time of the video is always 0 23 | 24 | Args: 25 | boxes (np.ndarray): structured box array with fields ['t','x','y','w','h','class_id','track_id','class_confidence'] 26 | (example BBOX_DTYPE is provided in src/box_loading.py) 27 | 28 | Returns: 29 | boxes: filtered boxes 30 | """ 31 | ts = boxes['t'] 32 | width = boxes['w'] 33 | height = boxes['h'] 34 | diag_square = width ** 2 + height ** 2 35 | mask = (ts > skip_ts) * (diag_square >= min_box_diag ** 2) * (width >= min_box_side) * (height >= min_box_side) 36 | return boxes[mask] 37 | -------------------------------------------------------------------------------- /rvt/utils/evaluation/prophesee/metrics/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/utils/evaluation/prophesee/metrics/__init__.py -------------------------------------------------------------------------------- /rvt/utils/evaluation/prophesee/visualize/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/rvt/utils/evaluation/prophesee/visualize/__init__.py -------------------------------------------------------------------------------- /rvt/utils/helpers.py: -------------------------------------------------------------------------------- 1 | from typing import Union 2 | 3 | import torch as th 4 | 5 | 6 | def torch_uniform_sample_scalar(min_value: float, max_value: float): 7 | assert max_value >= min_value, f'{max_value=} is smaller than {min_value=}' 8 | if max_value == min_value: 9 | return min_value 10 | return min_value + (max_value - min_value) * th.rand(1).item() 11 | 12 | 13 | def clamp(value: Union[int, float], smallest: Union[int, float], largest: Union[int, float]): 14 | return max(smallest, min(value, largest)) 15 | -------------------------------------------------------------------------------- /rvt/utils/preprocessing.py: -------------------------------------------------------------------------------- 1 | def _blosc_opts(complevel=1, complib='blosc:zstd', shuffle='byte'): 2 | shuffle = 2 if shuffle == 'bit' else 1 if shuffle == 'byte' else 0 3 | compressors = ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd'] 4 | complib = ['blosc:' + c for c in compressors].index(complib) 5 | args = { 6 | 'compression': 32001, 7 | 'compression_opts': (0, 0, 0, 0, complevel, shuffle, complib), 8 | } 9 | if shuffle > 0: 10 | # Do not use h5py shuffle if blosc shuffle is enabled. 11 | args['shuffle'] = False 12 | return args 13 | -------------------------------------------------------------------------------- /ultralytics/.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | blank_issues_enabled: true 2 | contact_links: 3 | - name: 📄 Docs 4 | url: https://docs.ultralytics.com/ 5 | about: Full Ultralytics YOLOv8 Documentation 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[issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) and found no similar questions. 19 | required: true 20 | 21 | - type: textarea 22 | attributes: 23 | label: Question 24 | description: What is your question? 25 | placeholder: | 26 | 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. 27 | validations: 28 | required: true 29 | 30 | - type: textarea 31 | attributes: 32 | label: Additional 33 | description: Anything else you would like to share? 34 | -------------------------------------------------------------------------------- /ultralytics/.github/dependabot.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Dependabot for package version updates 3 | # https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates 4 | 5 | version: 2 6 | updates: 7 | - package-ecosystem: pip 8 | directory: "/" 9 | schedule: 10 | interval: weekly 11 | time: "04:00" 12 | open-pull-requests-limit: 10 13 | reviewers: 14 | - glenn-jocher 15 | labels: 16 | - dependencies 17 | 18 | - package-ecosystem: github-actions 19 | directory: "/" 20 | schedule: 21 | interval: weekly 22 | time: "04:00" 23 | open-pull-requests-limit: 5 24 | reviewers: 25 | - glenn-jocher 26 | labels: 27 | - dependencies 28 | -------------------------------------------------------------------------------- /ultralytics/.github/translate-readme.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md 3 | 4 | name: Translate README 5 | 6 | on: 7 | push: 8 | branches: 9 | - translate_readme # replace with 'main' to enable action 10 | paths: 11 | - README.md 12 | 13 | jobs: 14 | Translate: 15 | runs-on: ubuntu-latest 16 | steps: 17 | - uses: actions/checkout@v3 18 | - name: Setup Node.js 19 | uses: actions/setup-node@v3 20 | with: 21 | node-version: 16 22 | # ISO Language Codes: https://cloud.google.com/translate/docs/languages 23 | - name: Adding README - Chinese Simplified 24 | uses: dephraiim/translate-readme@main 25 | with: 26 | LANG: zh-CN 27 | -------------------------------------------------------------------------------- /ultralytics/.github/workflows/cla.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | name: CLA Assistant 4 | on: 5 | issue_comment: 6 | types: 7 | - created 8 | pull_request_target: 9 | types: 10 | - reopened 11 | - opened 12 | - synchronize 13 | 14 | jobs: 15 | CLA: 16 | if: github.repository == 'ultralytics/ultralytics' 17 | runs-on: ubuntu-latest 18 | steps: 19 | - name: CLA Assistant 20 | if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target' 21 | uses: contributor-assistant/github-action@v2.3.1 22 | env: 23 | GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} 24 | # must be repository secret token 25 | PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }} 26 | with: 27 | path-to-signatures: 'signatures/version1/cla.json' 28 | path-to-document: 'https://docs.ultralytics.com/help/CLA' # CLA document 29 | # branch should not be protected 30 | branch: 'main' 31 | allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot* 32 | 33 | remote-organization-name: ultralytics 34 | remote-repository-name: cla 35 | custom-pr-sign-comment: 'I have read the CLA Document and I sign the CLA' 36 | custom-allsigned-prcomment: All Contributors have signed the CLA. ✅ 37 | #custom-notsigned-prcomment: 'pull request comment with Introductory message to ask new contributors to sign' 38 | -------------------------------------------------------------------------------- /ultralytics/.github/workflows/codeql.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | name: "CodeQL" 4 | 5 | on: 6 | schedule: 7 | - cron: '0 0 1 * *' 8 | workflow_dispatch: 9 | 10 | jobs: 11 | analyze: 12 | name: Analyze 13 | runs-on: ${{ 'ubuntu-latest' }} 14 | permissions: 15 | actions: read 16 | contents: read 17 | security-events: write 18 | 19 | strategy: 20 | fail-fast: false 21 | matrix: 22 | language: [ 'python' ] 23 | # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ] 24 | 25 | steps: 26 | - name: Checkout repository 27 | uses: actions/checkout@v4 28 | 29 | # Initializes the CodeQL tools for scanning. 30 | - name: Initialize CodeQL 31 | uses: github/codeql-action/init@v2 32 | with: 33 | languages: ${{ matrix.language }} 34 | # If you wish to specify custom queries, you can do so here or in a config file. 35 | # By default, queries listed here will override any specified in a config file. 36 | # Prefix the list here with "+" to use these queries and those in the config file. 37 | # queries: security-extended,security-and-quality 38 | 39 | - name: Perform CodeQL Analysis 40 | uses: github/codeql-action/analyze@v2 41 | with: 42 | category: "/language:${{matrix.language}}" 43 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | include tests/*.py 8 | recursive-include ultralytics *.yaml 9 | -------------------------------------------------------------------------------- /ultralytics/README.md: -------------------------------------------------------------------------------- 1 | # YOLOv8: You Only Look Once (v8.1) 2 | 3 | This is the modified yolov8 version for the CVPR 2024 paper [SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception](https://arxiv.org/abs/2404.10540). 4 | 5 | ## Whats Different? 6 | 7 | The ultralytics library was updated to consider only 2 channels from the image as the events and discard the 3rd channel. Similarly a zero last channel is added before visualization. 8 | 9 | The updated configs and training script utilizing this library can be found in `./yolo/`. 10 | 11 | ## Installation 12 | 13 | Pip install modified ultralytics in a Python>=3.8 environment with PyTorch>=1.8. 14 | 15 | ```Bash 16 | pip install -e . 17 | ``` 18 | 19 | ## Required Data 20 | 21 | To evaluate or train RVT on SEVD you will need to download the SEVD dataset from [Link to Dataset](https://docs.google.com/forms/d/e/1FAIpQLSdOhlegSlpzW78DsPSqNCDdfg7IVXsbcKD-BgBnbj_YdjojQg/viewform). 22 | 23 | 24 | 25 | 26 | 27 | 28 | ## Training 29 | 30 | ```bash 31 | python main.py --data data.yaml --batch_size 128 --max_epoch 20 32 | ``` 33 | 34 | ## Code Acknowledgments 35 | 36 | This project has used code from the following projects: 37 | 38 | - [ultralytics](https://github.com/ultralytics/ultralytics) for the official YOLOv8 implementation in Pytorch 39 | -------------------------------------------------------------------------------- /ultralytics/docker/Dockerfile-conda: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics 3 | # Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage 4 | 5 | # Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3 6 | FROM continuumio/miniconda3:latest 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 | RUN apt update \ 13 | && apt install --no-install-recommends -y libgl1-mesa-glx 14 | 15 | # Copy contents 16 | ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt . 17 | 18 | # Install conda packages 19 | # mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory' 20 | RUN conda config --set solver libmamba && \ 21 | conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia && \ 22 | conda install -c conda-forge ultralytics mkl 23 | # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics mkl 24 | 25 | 26 | # Usage Examples ------------------------------------------------------------------------------------------------------- 27 | 28 | # Build and Push 29 | # t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t 30 | 31 | # Run 32 | # t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t 33 | 34 | # Pull and Run 35 | # t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t 36 | 37 | # Pull and Run with local volume mounted 38 | # t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t 39 | -------------------------------------------------------------------------------- /ultralytics/docker/Dockerfile-runner: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics 3 | # Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference tests 4 | 5 | # Start FROM Ultralytics GPU image 6 | FROM ultralytics/ultralytics:latest 7 | 8 | # Set the working directory 9 | WORKDIR /actions-runner 10 | 11 | # Download and unpack the latest runner from https://github.com/actions/runner 12 | RUN FILENAME=actions-runner-linux-x64-2.309.0.tar.gz && \ 13 | curl -o $FILENAME -L https://github.com/actions/runner/releases/download/v2.309.0/$FILENAME && \ 14 | tar xzf $FILENAME && \ 15 | rm $FILENAME 16 | 17 | # Install runner dependencies 18 | ENV RUNNER_ALLOW_RUNASROOT=1 19 | ENV DEBIAN_FRONTEND=noninteractive 20 | RUN ./bin/installdependencies.sh && \ 21 | apt-get -y install libicu-dev 22 | 23 | # Inline ENTRYPOINT command to configure and start runner with default TOKEN and NAME 24 | ENTRYPOINT sh -c './config.sh --url https://github.com/ultralytics/ultralytics \ 25 | --token ${GITHUB_RUNNER_TOKEN:-TOKEN} \ 26 | --name ${GITHUB_RUNNER_NAME:-NAME} \ 27 | --labels gpu-latest \ 28 | --replace && \ 29 | ./run.sh' 30 | 31 | 32 | # Usage Examples ------------------------------------------------------------------------------------------------------- 33 | 34 | # Build and Push 35 | # t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t 36 | 37 | # Pull and Run in detached mode with access to GPUs 0 and 1 38 | # t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t 39 | -------------------------------------------------------------------------------- /ultralytics/docs/CNAME: -------------------------------------------------------------------------------- 1 | docs.ultralytics.com 2 | -------------------------------------------------------------------------------- /ultralytics/docs/assets/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/ultralytics/docs/assets/favicon.ico -------------------------------------------------------------------------------- /ultralytics/docs/overrides/partials/comments.html: -------------------------------------------------------------------------------- 1 | {% if page.meta.comments %} 2 |

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

3 | 4 | 5 | 20 | 21 | 22 | 50 | {% endif %} 51 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.get_save_dir 22 |

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

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

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

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

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

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

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

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

55 | 56 | --- 57 | ## ::: ultralytics.cfg.copy_default_cfg 58 |

59 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.coco80_to_coco91_class 18 |

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

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

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

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

35 | -------------------------------------------------------------------------------- /ultralytics/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 | 24 | --- 25 | ## ::: ultralytics.data.dataset.load_dataset_cache_file 26 |

27 | 28 | --- 29 | ## ::: ultralytics.data.dataset.save_dataset_cache_file 30 |

31 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 30 |

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

35 | 36 | --- 37 | ## ::: ultralytics.data.utils.polygon2mask 38 |

39 | 40 | --- 41 | ## ::: ultralytics.data.utils.polygons2masks 42 |

43 | 44 | --- 45 | ## ::: ultralytics.data.utils.polygons2masks_overlap 46 |

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

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

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

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

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

67 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/docs/reference/engine/tuner.md: -------------------------------------------------------------------------------- 1 | --- 2 | description: Explore the Ultralytics Tuner, a powerful tool designed for hyperparameter tuning of YOLO models to optimize performance across various tasks like object detection, image classification, and more. 3 | keywords: Ultralytics, Tuner, YOLO, hyperparameter tuning, optimization, object detection, image classification, instance segmentation, pose estimation, multi-object tracking 4 | --- 5 | 6 | # Reference for `ultralytics/engine/tuner.py` 7 | 8 | !!! note 9 | 10 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/tuner.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/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.engine.tuner.Tuner 14 |

15 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.reset_model 22 |

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

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

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

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

39 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.is_box_near_crop_edge 14 |

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

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

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

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

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

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

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

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

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

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

55 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.linear_assignment 14 |

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

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

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

27 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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._log_images 14 |

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

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

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

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

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

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

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

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

47 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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._log_tensorboard_graph 18 |

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

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

27 | 28 | --- 29 | ## ::: ultralytics.utils.callbacks.tensorboard.on_batch_end 30 |

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

35 | -------------------------------------------------------------------------------- /ultralytics/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._custom_table 14 |

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

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

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

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

31 | 32 | --- 33 | ## ::: ultralytics.utils.callbacks.wb.on_train_epoch_end 34 |

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

39 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.delete_dsstore 18 |

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

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

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

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

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

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

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

47 | 48 | --- 49 | ## ::: ultralytics.utils.downloads.download 50 |

51 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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.plt_color_scatter 38 |

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

43 | 44 | --- 45 | ## ::: ultralytics.utils.plotting.output_to_target 46 |

47 | 48 | --- 49 | ## ::: ultralytics.utils.plotting.feature_visualization 50 |

51 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/docs/reference/utils/triton.md: -------------------------------------------------------------------------------- 1 | # Reference for `ultralytics/utils/triton.py` 2 | 3 | !!! note 4 | 5 | Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/triton.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/triton.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.utils.triton.TritonRemoteModel 9 |

10 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/docs/robots.txt: -------------------------------------------------------------------------------- 1 | User-agent: * 2 | -------------------------------------------------------------------------------- /ultralytics/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 | 41 | /* Set content width */ 42 | .md-grid { 43 | max-width: 1440px; 44 | } 45 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/examples/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eventbasedvision/SEVD/5a187503f04092b7eb34683bbcb37933dde73d61/ultralytics/examples/zidane.jpg -------------------------------------------------------------------------------- /ultralytics/requirements.txt: -------------------------------------------------------------------------------- 1 | # Ultralytics requirements 2 | # Example: pip install -r requirements.txt 3 | 4 | # Base ---------------------------------------- 5 | matplotlib>=3.3.0 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>=1.8.0 13 | torchvision>=0.9.0 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>=7.0 # 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 | -------------------------------------------------------------------------------- /ultralytics/ultralytics/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | __version__ = '8.0.200' 4 | 5 | from ultralytics.models import RTDETR, SAM, YOLO 6 | from ultralytics.models.fastsam import FastSAM 7 | from ultralytics.models.nas import NAS 8 | from ultralytics.utils import SETTINGS as settings 9 | from ultralytics.utils.checks import check_yolo as checks 10 | from ultralytics.utils.downloads import download 11 | 12 | __all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'settings' 13 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | gmc_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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 | -------------------------------------------------------------------------------- /ultralytics/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 - <