├── CITATION.cff ├── README.md ├── data.yaml ├── datasets ├── .ipynb_checkpoints │ ├── yolov8-pose-p6-checkpoint.yaml │ ├── yolov8-pose-p6_new-2-checkpoint.yaml │ ├── yolov8-pose-p6_new-3-checkpoint.yaml │ └── yolov8-pose-p6_new-checkpoint.yaml ├── yolov8-pose-p6.yaml ├── yolov8-pose-p6_new-2.yaml ├── yolov8-pose-p6_new-3.yaml ├── yolov8-pose-p6_new.yaml ├── yolov8.yaml └── yolov8_new.yaml ├── train_step1.py └── ultralytics ├── __init__.py ├── __pycache__ └── __init__.cpython-38.pyc ├── assets ├── bus.jpg └── zidane.jpg ├── cfg ├── .ipynb_checkpoints │ └── default-checkpoint.yaml ├── __init__.py ├── __pycache__ │ └── __init__.cpython-38.pyc ├── datasets │ ├── .ipynb_checkpoints │ │ ├── VOC-checkpoint.yaml │ │ ├── coco-checkpoint.yaml │ │ └── coco8-checkpoint.yaml │ ├── Argoverse.yaml │ ├── GlobalWheat2020.yaml │ ├── ImageNet.yaml │ ├── Objects365.yaml │ ├── SKU-110K.yaml │ ├── VOC.yaml │ ├── VisDrone.yaml │ ├── coco-pose.yaml │ ├── coco.yaml │ ├── coco128-seg.yaml │ ├── coco128.yaml │ ├── coco8-pose.yaml │ ├── coco8-seg.yaml │ ├── coco8.yaml │ └── xView.yaml ├── default.yaml ├── models │ ├── README.md │ ├── rt-detr │ │ ├── .ipynb_checkpoints │ │ │ ├── rtdetr-l-checkpoint.yaml │ │ │ └── rtdetr-x-checkpoint.yaml │ │ ├── rtdetr-l.yaml │ │ └── rtdetr-x.yaml │ ├── v3 │ │ ├── .ipynb_checkpoints │ │ │ ├── yolov3-checkpoint.yaml │ │ │ ├── yolov3-spp-checkpoint.yaml │ │ │ └── yolov3-tiny-checkpoint.yaml │ │ ├── yolov3-spp.yaml │ │ ├── yolov3-tiny.yaml │ │ └── yolov3.yaml │ ├── v5 │ │ ├── .ipynb_checkpoints │ │ │ └── yolov5s-checkpoint.yaml │ │ ├── yolov5-p6.yaml │ │ └── yolov5s.yaml │ ├── v6 │ │ ├── .ipynb_checkpoints │ │ │ └── yolov6s-checkpoint.yaml │ │ └── yolov6s.yaml │ └── v8 │ │ ├── .ipynb_checkpoints │ │ ├── yolov8-pose-p6-checkpoint.yaml │ │ ├── yolov8-seg-Copy1-checkpoint.yaml │ │ ├── yolov8-seg-Copy2-checkpoint.yaml │ │ ├── yolov8-seg-checkpoint.yaml │ │ ├── yolov8_new-checkpoint.yaml │ │ └── yolov8s-checkpoint.yaml │ │ ├── yolov8-cls.yaml │ │ ├── yolov8-p2.yaml │ │ ├── yolov8-p6.yaml │ │ ├── yolov8-pose-p6.yaml │ │ ├── yolov8-pose.yaml │ │ ├── yolov8-rtdetr.yaml │ │ ├── yolov8-seg-Copy1.yaml │ │ ├── yolov8-seg-Copy2.yaml │ │ ├── yolov8-seg.yaml │ │ ├── yolov8_new.yaml │ │ ├── yolov8m_mobilenetv3.yaml │ │ ├── yolov8n.yaml │ │ └── yolov8s.yaml └── trackers │ ├── botsort.yaml │ └── bytetrack.yaml ├── data ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── augment.cpython-38.pyc │ ├── base.cpython-38.pyc │ ├── build.cpython-38.pyc │ ├── dataset.cpython-38.pyc │ ├── loaders.cpython-38.pyc │ └── utils.cpython-38.pyc ├── annotator.py ├── augment.py ├── base.py ├── build.py ├── converter.py ├── dataloaders │ └── __init__.py ├── dataset.py ├── loaders.py ├── scripts │ ├── download_weights.sh │ ├── get_coco.sh │ ├── get_coco128.sh │ └── get_imagenet.sh └── utils.py ├── datasets ├── Argoverse.yaml ├── GlobalWheat2020.yaml ├── ImageNet.yaml ├── Objects365.yaml ├── SKU-110K.yaml ├── VOC.yaml ├── VisDrone.yaml ├── coco-pose.yaml ├── coco.yaml ├── coco128-seg.yaml ├── coco128.yaml ├── coco8-pose.yaml ├── coco8-seg.yaml ├── coco8.yaml ├── data.yaml └── xView.yaml ├── engine ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── exporter.cpython-38.pyc │ ├── model.cpython-38.pyc │ ├── predictor.cpython-38.pyc │ ├── results.cpython-38.pyc │ ├── trainer.cpython-38.pyc │ └── validator.cpython-38.pyc ├── exporter.py ├── model.py ├── predictor.py ├── results.py ├── trainer.py └── validator.py ├── hub ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── auth.cpython-37.pyc │ ├── auth.cpython-38.pyc │ ├── utils.cpython-37.pyc │ └── utils.cpython-38.pyc ├── auth.py ├── session.py └── utils.py ├── models ├── README.md ├── __init__.py ├── __pycache__ │ └── __init__.cpython-38.pyc ├── fastsam │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── model.cpython-38.pyc │ │ ├── predict.cpython-38.pyc │ │ ├── prompt.cpython-38.pyc │ │ ├── utils.cpython-38.pyc │ │ └── val.cpython-38.pyc │ ├── model.py │ ├── predict.py │ ├── prompt.py │ ├── utils.py │ └── val.py ├── nas │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── model.cpython-38.pyc │ │ ├── predict.cpython-38.pyc │ │ └── val.cpython-38.pyc │ ├── model.py │ ├── predict.py │ └── val.py ├── rtdetr │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── model.cpython-38.pyc │ │ ├── predict.cpython-38.pyc │ │ ├── train.cpython-38.pyc │ │ └── val.cpython-38.pyc │ ├── model.py │ ├── predict.py │ ├── train.py │ └── val.py ├── sam │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── amg.cpython-38.pyc │ │ ├── build.cpython-38.pyc │ │ ├── model.cpython-38.pyc │ │ └── predict.cpython-38.pyc │ ├── amg.py │ ├── build.py │ ├── model.py │ ├── modules │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── decoders.cpython-38.pyc │ │ │ ├── encoders.cpython-38.pyc │ │ │ ├── sam.cpython-38.pyc │ │ │ ├── tiny_encoder.cpython-38.pyc │ │ │ └── transformer.cpython-38.pyc │ │ ├── decoders.py │ │ ├── encoders.py │ │ ├── sam.py │ │ ├── tiny_encoder.py │ │ └── transformer.py │ └── predict.py ├── utils │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ └── ops.cpython-38.pyc │ ├── loss.py │ └── ops.py ├── v3 │ ├── yolov3-spp.yaml │ ├── yolov3-tiny.yaml │ └── yolov3.yaml ├── v5 │ ├── yolov5-p6.yaml │ └── yolov5.yaml ├── v8 │ ├── yolov8-cls.yaml │ ├── yolov8-p2.yaml │ ├── yolov8-p6.yaml │ ├── yolov8-pose-p6.yaml │ ├── yolov8-pose.yaml │ ├── yolov8-seg.yaml │ ├── yolov8.yaml │ ├── yolov8l.yaml │ ├── yolov8n.yaml │ └── yolov8s.yaml └── yolo │ ├── __init__.py │ ├── __pycache__ │ └── __init__.cpython-38.pyc │ ├── classify │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── predict.cpython-38.pyc │ │ ├── train.cpython-38.pyc │ │ └── val.cpython-38.pyc │ ├── predict.py │ ├── train.py │ └── val.py │ ├── detect │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── predict.cpython-38.pyc │ │ ├── train.cpython-38.pyc │ │ └── val.cpython-38.pyc │ ├── predict.py │ ├── train.py │ └── val.py │ ├── pose │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── predict.cpython-38.pyc │ │ ├── train.cpython-38.pyc │ │ └── val.cpython-38.pyc │ ├── predict.py │ ├── train.py │ └── val.py │ └── segment │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── predict.cpython-38.pyc │ ├── train.cpython-38.pyc │ └── val.cpython-38.pyc │ ├── predict.py │ ├── train.py │ └── val.py ├── nn ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── autobackend.cpython-37.pyc │ ├── autobackend.cpython-38.pyc │ ├── modules.cpython-37.pyc │ ├── modules.cpython-38.pyc │ ├── tasks.cpython-37.pyc │ └── tasks.cpython-38.pyc ├── autobackend.py ├── autoshape.py ├── backbone │ ├── MobileNetV3.py │ └── __pycache__ │ │ └── MobileNetV3.cpython-38.pyc ├── modules.py ├── modules │ ├── .ipynb_checkpoints │ │ ├── block-checkpoint.py │ │ ├── conv-checkpoint.py │ │ └── head-checkpoint.py │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── block.cpython-38.pyc │ │ ├── conv.cpython-38.pyc │ │ ├── head.cpython-38.pyc │ │ ├── transformer.cpython-38.pyc │ │ └── utils.cpython-38.pyc │ ├── block.py │ ├── conv.py │ ├── head.py │ ├── transformer.py │ └── utils.py └── tasks.py ├── tracker ├── README.md ├── __init__.py ├── cfg │ ├── botsort.yaml │ └── bytetrack.yaml ├── track.py ├── trackers │ ├── __init__.py │ ├── basetrack.py │ ├── bot_sort.py │ └── byte_tracker.py └── utils │ ├── __init__.py │ ├── gmc.py │ ├── kalman_filter.py │ └── matching.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 ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── autobatch.cpython-38.pyc │ ├── checks.cpython-38.pyc │ ├── dist.cpython-38.pyc │ ├── downloads.cpython-38.pyc │ ├── files.cpython-38.pyc │ ├── instance.cpython-38.pyc │ ├── loss.cpython-38.pyc │ ├── metrics.cpython-38.pyc │ ├── ops.cpython-38.pyc │ ├── patches.cpython-38.pyc │ ├── plotting.cpython-38.pyc │ ├── tal.cpython-38.pyc │ └── torch_utils.cpython-38.pyc ├── autobatch.py ├── benchmarks.py ├── callbacks │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── base.cpython-38.pyc │ │ ├── clearml.cpython-38.pyc │ │ ├── comet.cpython-38.pyc │ │ ├── dvc.cpython-38.pyc │ │ ├── hub.cpython-38.pyc │ │ ├── mlflow.cpython-38.pyc │ │ ├── neptune.cpython-38.pyc │ │ ├── raytune.cpython-38.pyc │ │ ├── tensorboard.cpython-38.pyc │ │ └── wb.cpython-38.pyc │ ├── base.py │ ├── clearml.py │ ├── comet.py │ ├── dvc.py │ ├── hub.py │ ├── mlflow.py │ ├── neptune.py │ ├── raytune.py │ ├── tensorboard.py │ └── wb.py ├── checks.py ├── dist.py ├── downloads.py ├── errors.py ├── files.py ├── instance.py ├── loss.py ├── metrics.py ├── ops.py ├── patches.py ├── plotting.py ├── tal.py ├── torch_utils.py └── tuner.py └── yolo ├── __init__.py ├── cfg └── __init__.py ├── data └── __init__.py ├── engine └── __init__.py ├── utils └── __init__.py └── v8 └── __init__.py /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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # yolov8模型剪枝项目 2 | 3 | ## 项目简介 4 | 本项目通过应用模型剪枝技术，旨在降低深度学习模型的复杂性和计算负载，并通过回调训练进一步提升模型的效率和性能。 5 | 6 | ## 功能特点 7 | - **模型剪枝**：去除冗余权重，精简模型结构。 8 | - **回调训练**：剪枝后对模型进行再训练，优化性能。 9 | - **性能优化**：在减小模型体积的同时，保持或提高模型的准确性和泛化能力。 10 | 11 | ## 使用技术 12 | - 深度学习框架：PyTorch 13 | - 配置和权重文件：用于模型定义和初始化。 14 | - Python脚本：自定义脚本进行模型训练和调整。 15 | 16 | ## 运行环境 17 | - Python 3.8 18 | - 深度学习库：PyTorch 19 | - CUDA环境（推荐，用于GPU加速） 20 | 21 | ## 安装指南 22 | 1. 克隆项目仓库到本地机器 23 | ```bash 24 | git clone https://github.com/jasonDasuantou/yolov8_prune.git 25 | python train_step1.py 26 | -------------------------------------------------------------------------------- /data.yaml: -------------------------------------------------------------------------------- 1 | # 数据集根目录 2 | path: datasets2 3 | 4 | # 训练集、验证集和测试集相对于数据集根目录的路径 5 | train: F:/10team/yolo_project/datasets/datasets2/images/train # 训练集图片 6 | val: F:/10team/yolo_project/datasets/datasets2/images/val # 验证集图片 7 | test: F:/10team/yolo_project/datasets/datasets2/images/test # 测试集图片 8 | 9 | # 类别数 10 | nc: 8 11 | 12 | # 类别名称映射，确保与标签文件中的ID一致 13 | names: 14 | 0: 0 15 | 1: 1 16 | 2: 2 17 | 3: 3 18 | 4: 4 19 | 5: 5 20 | 6: 6 21 | 7: 7 22 | -------------------------------------------------------------------------------- /datasets/.ipynb_checkpoints/yolov8-pose-p6-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | 43 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 44 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 45 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 46 | 47 | - [-1, 1, Conv, [256, 3, 2]] 48 | - [[-1, 17], 1, Concat, [1]] # cat head P4 49 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 50 | 51 | - [-1, 1, Conv, [512, 3, 2]] 52 | - [[-1, 14], 1, Concat, [1]] # cat head P5 53 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 54 | 55 | - [-1, 1, Conv, [768, 3, 2]] 56 | - [[-1, 11], 1, Concat, [1]] # cat head P6 57 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 58 | 59 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 60 | -------------------------------------------------------------------------------- /datasets/.ipynb_checkpoints/yolov8-pose-p6_new-2-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | - [-1, 1, SimAM, []] 43 | 44 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 45 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 46 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 47 | - [-1, 1, CoTAttention, []] 48 | 49 | - [-1, 1, Conv, [256, 3, 2]] 50 | - [[-1, 17], 1, Concat, [1]] # cat head P4 51 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 52 | 53 | - [-1, 1, Conv, [512, 3, 2]] 54 | - [[-1, 14], 1, Concat, [1]] # cat head P5 55 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 56 | 57 | - [-1, 1, Conv, [768, 3, 2]] 58 | - [[-1, 11], 1, Concat, [1]] # cat head P6 59 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 60 | 61 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 62 | -------------------------------------------------------------------------------- /datasets/.ipynb_checkpoints/yolov8-pose-p6_new-3-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | # - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | # - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | # - [-1, 1, SimAM, []] 43 | 44 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 45 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 46 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 47 | # - [-1, 1, CoTAttention, []] 48 | 49 | - [-1, 1, Conv, [256, 3, 2]] 50 | - [[-1, 17], 1, Concat, [1]] # cat head P4 51 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 52 | 53 | - [-1, 1, Conv, [512, 3, 2]] 54 | - [[-1, 14], 1, Concat, [1]] # cat head P5 55 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 56 | 57 | - [-1, 1, Conv, [768, 3, 2]] 58 | - [[-1, 11], 1, Concat, [1]] # cat head P6 59 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 60 | 61 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 62 | -------------------------------------------------------------------------------- /datasets/.ipynb_checkpoints/yolov8-pose-p6_new-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | # - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | - [-1, 1, SimAM, []] 43 | 44 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 45 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 46 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 47 | - [-1, 1, CoTAttention, []] 48 | 49 | - [-1, 1, Conv, [256, 3, 2]] 50 | - [[-1, 17], 1, Concat, [1]] # cat head P4 51 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 52 | 53 | - [-1, 1, Conv, [512, 3, 2]] 54 | - [[-1, 14], 1, Concat, [1]] # cat head P5 55 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 56 | 57 | - [-1, 1, Conv, [768, 3, 2]] 58 | - [[-1, 11], 1, Concat, [1]] # cat head P6 59 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 60 | 61 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 62 | -------------------------------------------------------------------------------- /datasets/yolov8-pose-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | 43 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 44 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 45 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 46 | 47 | - [-1, 1, Conv, [256, 3, 2]] 48 | - [[-1, 17], 1, Concat, [1]] # cat head P4 49 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 50 | 51 | - [-1, 1, Conv, [512, 3, 2]] 52 | - [[-1, 14], 1, Concat, [1]] # cat head P5 53 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 54 | 55 | - [-1, 1, Conv, [768, 3, 2]] 56 | - [[-1, 11], 1, Concat, [1]] # cat head P6 57 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 58 | 59 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 60 | -------------------------------------------------------------------------------- /datasets/yolov8-pose-p6_new-2.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | - [-1, 1, SimAM, []] 43 | 44 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 45 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 46 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 47 | - [-1, 1, CoTAttention, []] 48 | 49 | - [-1, 1, Conv, [256, 3, 2]] 50 | - [[-1, 17], 1, Concat, [1]] # cat head P4 51 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 52 | 53 | - [-1, 1, Conv, [512, 3, 2]] 54 | - [[-1, 14], 1, Concat, [1]] # cat head P5 55 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 56 | 57 | - [-1, 1, Conv, [768, 3, 2]] 58 | - [[-1, 11], 1, Concat, [1]] # cat head P6 59 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 60 | 61 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 62 | -------------------------------------------------------------------------------- /datasets/yolov8-pose-p6_new-3.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | # - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | # - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | # - [-1, 1, SimAM, []] 43 | 44 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 45 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 46 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 47 | # - [-1, 1, CoTAttention, []] 48 | 49 | - [-1, 1, Conv, [256, 3, 2]] 50 | - [[-1, 17], 1, Concat, [1]] # cat head P4 51 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 52 | 53 | - [-1, 1, Conv, [512, 3, 2]] 54 | - [[-1, 14], 1, Concat, [1]] # cat head P5 55 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 56 | 57 | - [-1, 1, Conv, [768, 3, 2]] 58 | - [[-1, 11], 1, Concat, [1]] # cat head P6 59 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 60 | 61 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 62 | -------------------------------------------------------------------------------- /datasets/yolov8-pose-p6_new.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment 3 | 4 | # Parameters 5 | nc: 3 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | # - [-1, 6, HGBlock, [ 96, 256, 3 ]] 23 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 24 | - [-1, 6, C2f, [512, True]] 25 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | - [-1, 1, ELA, []] 37 | 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 40 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 41 | - [-1, 3, C2, [512, False]] # 17 42 | - [-1, 1, SimAM, []] 43 | 44 | - [-1, 1, nn.Upsample, [None, 2, "nearest"]] 45 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 46 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 47 | - [-1, 1, CoTAttention, []] 48 | 49 | - [-1, 1, Conv, [256, 3, 2]] 50 | - [[-1, 17], 1, Concat, [1]] # cat head P4 51 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 52 | 53 | - [-1, 1, Conv, [512, 3, 2]] 54 | - [[-1, 14], 1, Concat, [1]] # cat head P5 55 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 56 | 57 | - [-1, 1, Conv, [768, 3, 2]] 58 | - [[-1, 11], 1, Concat, [1]] # cat head P6 59 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 60 | 61 | - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6) 62 | -------------------------------------------------------------------------------- /datasets/yolov8.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 1 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) -------------------------------------------------------------------------------- /datasets/yolov8_new.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 1 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | 17 | - [-1, 1, HGStem, [32, 32]]# 0-P2/4 18 | - [ -1, 1, HGStem, [ 32, 48 ] ] # 0-P2/4 19 | - [ -1, 6, HGBlock, [ 48, 128, 3 ] ] 20 | 21 | - [ -1, 1, DWConv, [ 128, 3, 2, 1, False ] ]# 2-P3/8 22 | - [ -1, 6, HGBlock, [ 96, 512, 3 ] ] 23 | 24 | - [ -1, 1, DWConv, [ 512, 3, 2, 1, False ] ] # 4-P3/16 25 | - [ -1, 6, HGBlock, [ 192, 1024, 5, True, False ] ] 26 | - [ -1, 6, HGBlock, [ 192, 1024, 5, True, True ] ] 27 | - [ -1, 6, HGBlock, [ 192, 1024, 5, True, True ] ] 28 | 29 | - [ -1, 1, DWConv, [ 1024, 3, 2, 1, False ] ] # 8-P4/32 30 | - [ -1, 6, HGBlock, [ 384, 2048, 5, True, False ] ] 31 | 32 | # YOLOv8.0n head 33 | head: 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 36 | - [-1, 3, C2f, [512]] # 12 37 | - [-1, 1, ELA, []] 38 | 39 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 40 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 41 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 42 | 43 | - [-1, 1, Conv, [256, 3, 2]] 44 | - [[-1, 12], 1, Concat, [1]] # cat head P4 45 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 46 | 47 | - [-1, 1, Conv, [512, 3, 2]] 48 | - [[-1, 9], 1, Concat, [1]] # cat head P5 49 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 50 | 51 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) -------------------------------------------------------------------------------- /train_step1.py: -------------------------------------------------------------------------------- 1 | import os 2 | from ultralytics import YOLO 3 | import torch 4 | os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' 5 | 6 | 7 | def main(): 8 | model = YOLO(r'ultralytics/cfg/models/v8/yolov8s.yaml').load('runs/detect/yolov8s/weights/best.pt') 9 | model.train(data="data.yaml", amp=False, imgsz=640, epochs=100, batch=20, device=0, workers=0) 10 | 11 | 12 | if __name__ == '__main__': 13 | main() 14 | -------------------------------------------------------------------------------- /ultralytics/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | __version__ = '8.0.142' 4 | 5 | from ultralytics.engine.model import YOLO 6 | from ultralytics.hub import start 7 | from ultralytics.models import RTDETR, SAM 8 | from ultralytics.models.fastsam import FastSAM 9 | from ultralytics.models.nas import NAS 10 | from ultralytics.utils import SETTINGS as settings 11 | from ultralytics.utils.checks import check_yolo as checks 12 | from ultralytics.utils.downloads import download 13 | 14 | __all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'start', 'settings' # allow simpler import 15 | -------------------------------------------------------------------------------- /ultralytics/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/assets/bus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/assets/bus.jpg -------------------------------------------------------------------------------- /ultralytics/assets/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/assets/zidane.jpg -------------------------------------------------------------------------------- /ultralytics/cfg/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/cfg/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/.ipynb_checkpoints/coco8-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics 3 | # Example usage: yolo train data=coco8.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco8 ← downloads here (1 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco8 # dataset root dir 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: person 19 | 1: bicycle 20 | 2: car 21 | 3: motorcycle 22 | 4: airplane 23 | 5: bus 24 | 6: train 25 | 7: truck 26 | 8: boat 27 | 9: traffic light 28 | 10: fire hydrant 29 | 11: stop sign 30 | 12: parking meter 31 | 13: bench 32 | 14: bird 33 | 15: cat 34 | 16: dog 35 | 17: horse 36 | 18: sheep 37 | 19: cow 38 | 20: elephant 39 | 21: bear 40 | 22: zebra 41 | 23: giraffe 42 | 24: backpack 43 | 25: umbrella 44 | 26: handbag 45 | 27: tie 46 | 28: suitcase 47 | 29: frisbee 48 | 30: skis 49 | 31: snowboard 50 | 32: sports ball 51 | 33: kite 52 | 34: baseball bat 53 | 35: baseball glove 54 | 36: skateboard 55 | 37: surfboard 56 | 38: tennis racket 57 | 39: bottle 58 | 40: wine glass 59 | 41: cup 60 | 42: fork 61 | 43: knife 62 | 44: spoon 63 | 45: bowl 64 | 46: banana 65 | 47: apple 66 | 48: sandwich 67 | 49: orange 68 | 50: broccoli 69 | 51: carrot 70 | 52: hot dog 71 | 53: pizza 72 | 54: donut 73 | 55: cake 74 | 56: chair 75 | 57: couch 76 | 58: potted plant 77 | 59: bed 78 | 60: dining table 79 | 61: toilet 80 | 62: tv 81 | 63: laptop 82 | 64: mouse 83 | 65: remote 84 | 66: keyboard 85 | 67: cell phone 86 | 68: microwave 87 | 69: oven 88 | 70: toaster 89 | 71: sink 90 | 72: refrigerator 91 | 73: book 92 | 74: clock 93 | 75: vase 94 | 76: scissors 95 | 77: teddy bear 96 | 78: hair drier 97 | 79: toothbrush 98 | 99 | 100 | # Download script/URL (optional) 101 | download: https://ultralytics.com/assets/coco8.zip 102 | -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/GlobalWheat2020.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan 3 | # Example usage: yolo train data=GlobalWheat2020.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── GlobalWheat2020 ← downloads here (7.0 GB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/GlobalWheat2020 # dataset root dir 12 | train: # train images (relative to 'path') 3422 images 13 | - images/arvalis_1 14 | - images/arvalis_2 15 | - images/arvalis_3 16 | - images/ethz_1 17 | - images/rres_1 18 | - images/inrae_1 19 | - images/usask_1 20 | val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) 21 | - images/ethz_1 22 | test: # test images (optional) 1276 images 23 | - images/utokyo_1 24 | - images/utokyo_2 25 | - images/nau_1 26 | - images/uq_1 27 | 28 | # Classes 29 | names: 30 | 0: wheat_head 31 | 32 | 33 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 34 | download: | 35 | from ultralytics.utils.downloads import download 36 | from pathlib import Path 37 | 38 | # Download 39 | dir = Path(yaml['path']) # dataset root dir 40 | urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', 41 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] 42 | download(urls, dir=dir) 43 | 44 | # Make Directories 45 | for p in 'annotations', 'images', 'labels': 46 | (dir / p).mkdir(parents=True, exist_ok=True) 47 | 48 | # Move 49 | for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ 50 | 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': 51 | (dir / 'global-wheat-codalab-official' / p).rename(dir / 'images' / p) # move to /images 52 | f = (dir / 'global-wheat-codalab-official' / p).with_suffix('.json') # json file 53 | if f.exists(): 54 | f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations 55 | -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/coco-pose.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO 2017 dataset http://cocodataset.org by Microsoft 3 | # Example usage: yolo train data=coco-pose.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco-pose ← downloads here (20.1 GB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco-pose # dataset root dir 12 | train: train2017.txt # train images (relative to 'path') 118287 images 13 | val: val2017.txt # val images (relative to 'path') 5000 images 14 | test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 15 | 16 | # Keypoints 17 | kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 18 | flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] 19 | 20 | # Classes 21 | names: 22 | 0: person 23 | 24 | # Download script/URL (optional) 25 | download: | 26 | from ultralytics.utils.downloads import download 27 | from pathlib import Path 28 | 29 | # Download labels 30 | dir = Path(yaml['path']) # dataset root dir 31 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 32 | urls = [url + 'coco2017labels-pose.zip'] # labels 33 | download(urls, dir=dir.parent) 34 | # Download data 35 | urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 36 | 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 37 | 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) 38 | download(urls, dir=dir / 'images', threads=3) 39 | -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/coco128.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics 3 | # Example usage: yolo train data=coco128.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco128 ← downloads here (7 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco128 # dataset root dir 12 | train: images/train2017 # train images (relative to 'path') 128 images 13 | val: images/train2017 # val images (relative to 'path') 128 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: person 19 | 1: bicycle 20 | 2: car 21 | 3: motorcycle 22 | 4: airplane 23 | 5: bus 24 | 6: train 25 | 7: truck 26 | 8: boat 27 | 9: traffic light 28 | 10: fire hydrant 29 | 11: stop sign 30 | 12: parking meter 31 | 13: bench 32 | 14: bird 33 | 15: cat 34 | 16: dog 35 | 17: horse 36 | 18: sheep 37 | 19: cow 38 | 20: elephant 39 | 21: bear 40 | 22: zebra 41 | 23: giraffe 42 | 24: backpack 43 | 25: umbrella 44 | 26: handbag 45 | 27: tie 46 | 28: suitcase 47 | 29: frisbee 48 | 30: skis 49 | 31: snowboard 50 | 32: sports ball 51 | 33: kite 52 | 34: baseball bat 53 | 35: baseball glove 54 | 36: skateboard 55 | 37: surfboard 56 | 38: tennis racket 57 | 39: bottle 58 | 40: wine glass 59 | 41: cup 60 | 42: fork 61 | 43: knife 62 | 44: spoon 63 | 45: bowl 64 | 46: banana 65 | 47: apple 66 | 48: sandwich 67 | 49: orange 68 | 50: broccoli 69 | 51: carrot 70 | 52: hot dog 71 | 53: pizza 72 | 54: donut 73 | 55: cake 74 | 56: chair 75 | 57: couch 76 | 58: potted plant 77 | 59: bed 78 | 60: dining table 79 | 61: toilet 80 | 62: tv 81 | 63: laptop 82 | 64: mouse 83 | 65: remote 84 | 66: keyboard 85 | 67: cell phone 86 | 68: microwave 87 | 69: oven 88 | 70: toaster 89 | 71: sink 90 | 72: refrigerator 91 | 73: book 92 | 74: clock 93 | 75: vase 94 | 76: scissors 95 | 77: teddy bear 96 | 78: hair drier 97 | 79: toothbrush 98 | 99 | 100 | # Download script/URL (optional) 101 | download: https://ultralytics.com/assets/coco128.zip 102 | -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/coco8-pose.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics 3 | # Example usage: yolo train data=coco8-pose.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco8-pose ← downloads here (1 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco8-pose # dataset root dir 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Keypoints 17 | kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 18 | flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] 19 | 20 | # Classes 21 | names: 22 | 0: person 23 | 24 | # Download script/URL (optional) 25 | download: https://ultralytics.com/assets/coco8-pose.zip 26 | -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/coco8-seg.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics 3 | # Example usage: yolo train data=coco8-seg.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco8-seg ← downloads here (1 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco8-seg # dataset root dir 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: person 19 | 1: bicycle 20 | 2: car 21 | 3: motorcycle 22 | 4: airplane 23 | 5: bus 24 | 6: train 25 | 7: truck 26 | 8: boat 27 | 9: traffic light 28 | 10: fire hydrant 29 | 11: stop sign 30 | 12: parking meter 31 | 13: bench 32 | 14: bird 33 | 15: cat 34 | 16: dog 35 | 17: horse 36 | 18: sheep 37 | 19: cow 38 | 20: elephant 39 | 21: bear 40 | 22: zebra 41 | 23: giraffe 42 | 24: backpack 43 | 25: umbrella 44 | 26: handbag 45 | 27: tie 46 | 28: suitcase 47 | 29: frisbee 48 | 30: skis 49 | 31: snowboard 50 | 32: sports ball 51 | 33: kite 52 | 34: baseball bat 53 | 35: baseball glove 54 | 36: skateboard 55 | 37: surfboard 56 | 38: tennis racket 57 | 39: bottle 58 | 40: wine glass 59 | 41: cup 60 | 42: fork 61 | 43: knife 62 | 44: spoon 63 | 45: bowl 64 | 46: banana 65 | 47: apple 66 | 48: sandwich 67 | 49: orange 68 | 50: broccoli 69 | 51: carrot 70 | 52: hot dog 71 | 53: pizza 72 | 54: donut 73 | 55: cake 74 | 56: chair 75 | 57: couch 76 | 58: potted plant 77 | 59: bed 78 | 60: dining table 79 | 61: toilet 80 | 62: tv 81 | 63: laptop 82 | 64: mouse 83 | 65: remote 84 | 66: keyboard 85 | 67: cell phone 86 | 68: microwave 87 | 69: oven 88 | 70: toaster 89 | 71: sink 90 | 72: refrigerator 91 | 73: book 92 | 74: clock 93 | 75: vase 94 | 76: scissors 95 | 77: teddy bear 96 | 78: hair drier 97 | 79: toothbrush 98 | 99 | 100 | # Download script/URL (optional) 101 | download: https://ultralytics.com/assets/coco8-seg.zip 102 | -------------------------------------------------------------------------------- /ultralytics/cfg/datasets/coco8.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics 3 | # Example usage: yolo train data=coco8.yaml 4 | # parent 5 | # ├── ultralytics 6 | # └── datasets 7 | # └── coco8 ← downloads here (1 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco8 # dataset root dir 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: person 19 | 1: bicycle 20 | 2: car 21 | 3: motorcycle 22 | 4: airplane 23 | 5: bus 24 | 6: train 25 | 7: truck 26 | 8: boat 27 | 9: traffic light 28 | 10: fire hydrant 29 | 11: stop sign 30 | 12: parking meter 31 | 13: bench 32 | 14: bird 33 | 15: cat 34 | 16: dog 35 | 17: horse 36 | 18: sheep 37 | 19: cow 38 | 20: elephant 39 | 21: bear 40 | 22: zebra 41 | 23: giraffe 42 | 24: backpack 43 | 25: umbrella 44 | 26: handbag 45 | 27: tie 46 | 28: suitcase 47 | 29: frisbee 48 | 30: skis 49 | 31: snowboard 50 | 32: sports ball 51 | 33: kite 52 | 34: baseball bat 53 | 35: baseball glove 54 | 36: skateboard 55 | 37: surfboard 56 | 38: tennis racket 57 | 39: bottle 58 | 40: wine glass 59 | 41: cup 60 | 42: fork 61 | 43: knife 62 | 44: spoon 63 | 45: bowl 64 | 46: banana 65 | 47: apple 66 | 48: sandwich 67 | 49: orange 68 | 50: broccoli 69 | 51: carrot 70 | 52: hot dog 71 | 53: pizza 72 | 54: donut 73 | 55: cake 74 | 56: chair 75 | 57: couch 76 | 58: potted plant 77 | 59: bed 78 | 60: dining table 79 | 61: toilet 80 | 62: tv 81 | 63: laptop 82 | 64: mouse 83 | 65: remote 84 | 66: keyboard 85 | 67: cell phone 86 | 68: microwave 87 | 69: oven 88 | 70: toaster 89 | 71: sink 90 | 72: refrigerator 91 | 73: book 92 | 74: clock 93 | 75: vase 94 | 76: scissors 95 | 77: teddy bear 96 | 78: hair drier 97 | 79: toothbrush 98 | 99 | 100 | # Download script/URL (optional) 101 | download: https://ultralytics.com/assets/coco8.zip 102 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/rt-detr/.ipynb_checkpoints/rtdetr-l-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr 3 | 4 | # Parameters 5 | nc: 80 # 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 | l: [1.00, 1.00, 1024] 9 | 10 | backbone: 11 | # [from, repeats, module, args] 12 | - [-1, 1, HGStem, [32, 48]] # 0-P2/4 13 | - [-1, 6, HGBlock, [48, 128, 3]] # stage 1 14 | 15 | - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8 16 | - [-1, 6, HGBlock, [96, 512, 3]] # stage 2 17 | 18 | - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16 19 | - [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut 20 | - [-1, 6, HGBlock, [192, 1024, 5, True, True]] 21 | - [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3 22 | 23 | - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32 24 | - [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4 25 | 26 | head: 27 | - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2 28 | - [-1, 1, AIFI, [1024, 8]] 29 | - [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0 30 | 31 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 32 | - [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1 33 | - [[-2, -1], 1, Concat, [1]] 34 | - [-1, 3, RepC3, [256]] # 16, fpn_blocks.0 35 | - [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1 36 | 37 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 38 | - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0 39 | - [[-2, -1], 1, Concat, [1]] # cat backbone P4 40 | - [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1 41 | 42 | - [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0 43 | - [[-1, 17], 1, Concat, [1]] # cat Y4 44 | - [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0 45 | 46 | - [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1 47 | - [[-1, 12], 1, Concat, [1]] # cat Y5 48 | - [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1 49 | 50 | - [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5) 51 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/rt-detr/rtdetr-l.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr 3 | 4 | # Parameters 5 | nc: 80 # 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 | l: [1.00, 1.00, 1024] 9 | 10 | backbone: 11 | # [from, repeats, module, args] 12 | - [-1, 1, HGStem, [32, 48]] # 0-P2/4 13 | - [-1, 6, HGBlock, [48, 128, 3]] # stage 1 14 | 15 | - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8 16 | - [-1, 6, HGBlock, [96, 512, 3]] # stage 2 17 | 18 | - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16 19 | - [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut 20 | - [-1, 6, HGBlock, [192, 1024, 5, True, True]] 21 | - [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3 22 | 23 | - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32 24 | - [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4 25 | 26 | head: 27 | - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2 28 | - [-1, 1, AIFI, [1024, 8]] 29 | - [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0 30 | 31 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 32 | - [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1 33 | - [[-2, -1], 1, Concat, [1]] 34 | - [-1, 3, RepC3, [256]] # 16, fpn_blocks.0 35 | - [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1 36 | 37 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 38 | - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0 39 | - [[-2, -1], 1, Concat, [1]] # cat backbone P4 40 | - [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1 41 | 42 | - [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0 43 | - [[-1, 17], 1, Concat, [1]] # cat Y4 44 | - [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0 45 | 46 | - [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1 47 | - [[-1, 12], 1, Concat, [1]] # cat Y5 48 | - [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1 49 | 50 | - [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5) 51 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v3/.ipynb_checkpoints/yolov3-checkpoint.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/cfg/models/v3/.ipynb_checkpoints/yolov3-spp-checkpoint.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/cfg/models/v3/.ipynb_checkpoints/yolov3-tiny-checkpoint.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/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/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/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/cfg/models/v5/.ipynb_checkpoints/yolov5s-checkpoint.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/cfg/models/v5/yolov5-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv5 object detection model with P3-P6 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-p6.yaml' will call yolov5-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 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, [768, 3, 2]], # 7-P5/32 25 | [-1, 3, C3, [768]], 26 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 27 | [-1, 3, C3, [1024]], 28 | [-1, 1, SPPF, [1024, 5]], # 11 29 | ] 30 | 31 | # YOLOv5 v6.0 head 32 | head: 33 | [[-1, 1, Conv, [768, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 36 | [-1, 3, C3, [768, False]], # 15 37 | 38 | [-1, 1, Conv, [512, 1, 1]], 39 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 40 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 41 | [-1, 3, C3, [512, False]], # 19 42 | 43 | [-1, 1, Conv, [256, 1, 1]], 44 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 45 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 46 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 47 | 48 | [-1, 1, Conv, [256, 3, 2]], 49 | [[-1, 20], 1, Concat, [1]], # cat head P4 50 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 51 | 52 | [-1, 1, Conv, [512, 3, 2]], 53 | [[-1, 16], 1, Concat, [1]], # cat head P5 54 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 55 | 56 | [-1, 1, Conv, [768, 3, 2]], 57 | [[-1, 12], 1, Concat, [1]], # cat head P6 58 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 59 | 60 | [[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6) 61 | ] 62 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v5/yolov5s.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/cfg/models/v6/.ipynb_checkpoints/yolov6s-checkpoint.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: 8 # 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/cfg/models/v6/yolov6s.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: 8 # 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/cfg/models/v8/.ipynb_checkpoints/yolov8-pose-p6-checkpoint.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: 3 # 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-p6.yaml' will call yolov8-p6.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 | # YOLOv8.0x6 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, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | 37 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 38 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 39 | - [-1, 3, C2, [512, False]] # 17 40 | 41 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 42 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 43 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 44 | 45 | - [-1, 1, Conv, [256, 3, 2]] 46 | - [[-1, 17], 1, Concat, [1]] # cat head P4 47 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 48 | 49 | - [-1, 1, Conv, [512, 3, 2]] 50 | - [[-1, 14], 1, Concat, [1]] # cat head P5 51 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 52 | 53 | - [-1, 1, Conv, [768, 3, 2]] 54 | - [[-1, 11], 1, Concat, [1]] # cat head P6 55 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 56 | 57 | - [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6) 58 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/.ipynb_checkpoints/yolov8-seg-Copy1-checkpoint.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: 3 # 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, 6, HGBlock, [ 96, 256, 3 ]] 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 | # - [-1, 1, ELA, []] 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | - [-1, 1, SimAM, []] 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | # - [-1, 1, CoTAttention, []] 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/.ipynb_checkpoints/yolov8-seg-Copy2-checkpoint.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: 3 # 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, 6, HGBlock, [ 96, 256, 3 ]] 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 | # - [-1, 1, ELA, []] 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | # - [-1, 1, SimAM, []] 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | - [-1, 1, CoTAttention, []] 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/.ipynb_checkpoints/yolov8-seg-checkpoint.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: 3 # 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, 6, HGBlock, [ 96, 256, 3 ]] 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 | - [-1, 1, ELA, []] 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | - [-1, 1, SimAM, []] 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | - [-1, 1, CoTAttention, []] 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/.ipynb_checkpoints/yolov8_new-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 1 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - 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 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/.ipynb_checkpoints/yolov8s-checkpoint.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /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/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/cfg/models/v8/yolov8-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [768, True]] 26 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 27 | - [-1, 3, C2f, [1024, True]] 28 | - [-1, 1, SPPF, [1024, 5]] # 11 29 | 30 | # YOLOv8.0x6 head 31 | head: 32 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 33 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 34 | - [-1, 3, C2, [768, False]] # 14 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 38 | - [-1, 3, C2, [512, False]] # 17 39 | 40 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 41 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 42 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 43 | 44 | - [-1, 1, Conv, [256, 3, 2]] 45 | - [[-1, 17], 1, Concat, [1]] # cat head P4 46 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 47 | 48 | - [-1, 1, Conv, [512, 3, 2]] 49 | - [[-1, 14], 1, Concat, [1]] # cat head P5 50 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 51 | 52 | - [-1, 1, Conv, [768, 3, 2]] 53 | - [[-1, 11], 1, Concat, [1]] # cat head P6 54 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 55 | 56 | - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6) 57 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/yolov8-pose-p6.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: 3 # 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-p6.yaml' will call yolov8-p6.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 | # YOLOv8.0x6 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, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | 37 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 38 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 39 | - [-1, 3, C2, [512, False]] # 17 40 | 41 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 42 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 43 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 44 | 45 | - [-1, 1, Conv, [256, 3, 2]] 46 | - [[-1, 17], 1, Concat, [1]] # cat head P4 47 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 48 | 49 | - [-1, 1, Conv, [512, 3, 2]] 50 | - [[-1, 14], 1, Concat, [1]] # cat head P5 51 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 52 | 53 | - [-1, 1, Conv, [768, 3, 2]] 54 | - [[-1, 11], 1, Concat, [1]] # cat head P6 55 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 56 | 57 | - [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6) 58 | -------------------------------------------------------------------------------- /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/cfg/models/v8/yolov8-rtdetr.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/yolov8-seg-Copy1.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: 3 # 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, 6, HGBlock, [ 96, 256, 3 ]] 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 | # - [-1, 1, ELA, []] 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | - [-1, 1, SimAM, []] 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | # - [-1, 1, CoTAttention, []] 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/yolov8-seg-Copy2.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: 3 # 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, 6, HGBlock, [ 96, 256, 3 ]] 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 | # - [-1, 1, ELA, []] 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | # - [-1, 1, SimAM, []] 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | - [-1, 1, CoTAttention, []] 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /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: 3 # 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, 6, HGBlock, [ 96, 256, 3 ]] 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 | - [-1, 1, ELA, []] 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | - [-1, 1, SimAM, []] 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | - [-1, 1, CoTAttention, []] 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/yolov8_new.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 1 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - 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 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 38 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 39 | 40 | - [-1, 1, Conv, [256, 3, 2]] 41 | - [[-1, 12], 1, Concat, [1]] # cat head P4 42 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 43 | 44 | - [-1, 1, Conv, [512, 3, 2]] 45 | - [[-1, 9], 1, Concat, [1]] # cat head P5 46 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 47 | 48 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 49 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/yolov8n.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 8 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /ultralytics/cfg/models/v8/yolov8s.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | 46 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 47 | -------------------------------------------------------------------------------- /ultralytics/cfg/trackers/botsort.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT 3 | 4 | tracker_type: botsort # tracker type, ['botsort', 'bytetrack'] 5 | track_high_thresh: 0.5 # threshold for the first association 6 | track_low_thresh: 0.1 # threshold for the second association 7 | new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks 8 | track_buffer: 30 # buffer to calculate the time when to remove tracks 9 | match_thresh: 0.8 # threshold for matching tracks 10 | # min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now) 11 | # mot20: False # for tracker evaluation(not used for now) 12 | 13 | # BoT-SORT settings 14 | cmc_method: sparseOptFlow # method of global motion compensation 15 | # ReID model related thresh (not supported yet) 16 | proximity_thresh: 0.5 17 | appearance_thresh: 0.25 18 | with_reid: False 19 | -------------------------------------------------------------------------------- /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/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/data/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/__pycache__/augment.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/augment.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/__pycache__/base.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/base.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/__pycache__/build.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/build.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/__pycache__/dataset.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/dataset.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/__pycache__/loaders.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/loaders.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/__pycache__/utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/__pycache__/utils.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/data/annotator.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | 3 | from ultralytics import SAM, YOLO 4 | 5 | 6 | def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None): 7 | """ 8 | Automatically annotates images using a YOLO object detection model and a SAM segmentation model. 9 | Args: 10 | data (str): Path to a folder containing images to be annotated. 11 | det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. 12 | sam_model (str, optional): Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. 13 | device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available). 14 | output_dir (str | None | optional): Directory to save the annotated results. 15 | Defaults to a 'labels' folder in the same directory as 'data'. 16 | """ 17 | det_model = YOLO(det_model) 18 | sam_model = SAM(sam_model) 19 | 20 | if not output_dir: 21 | output_dir = Path(str(data)).parent / 'labels' 22 | Path(output_dir).mkdir(exist_ok=True, parents=True) 23 | 24 | det_results = det_model(data, stream=True, device=device) 25 | 26 | for result in det_results: 27 | boxes = result.boxes.xyxy # Boxes object for bbox outputs 28 | class_ids = result.boxes.cls.int().tolist() # noqa 29 | if len(class_ids): 30 | sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device) 31 | segments = sam_results[0].masks.xyn # noqa 32 | 33 | with open(str(Path(output_dir) / Path(result.path).stem) + '.txt', 'w') as f: 34 | for i in range(len(segments)): 35 | s = segments[i] 36 | if len(s) == 0: 37 | continue 38 | segment = map(str, segments[i].reshape(-1).tolist()) 39 | f.write(f'{class_ids[i]} ' + ' '.join(segment) + '\n') 40 | -------------------------------------------------------------------------------- /ultralytics/data/dataloaders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/data/dataloaders/__init__.py -------------------------------------------------------------------------------- /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 - < w - threshold, 2] = w # x2 26 | boxes[boxes[:, 3] > h - threshold, 3] = h # y2 27 | return boxes 28 | 29 | 30 | def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False): 31 | """ 32 | Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes. 33 | 34 | Args: 35 | box1 (torch.Tensor): (4, ) 36 | boxes (torch.Tensor): (n, 4) 37 | 38 | Returns: 39 | high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres 40 | """ 41 | boxes = adjust_bboxes_to_image_border(boxes, image_shape) 42 | # obtain coordinates for intersections 43 | x1 = torch.max(box1[0], boxes[:, 0]) 44 | y1 = torch.max(box1[1], boxes[:, 1]) 45 | x2 = torch.min(box1[2], boxes[:, 2]) 46 | y2 = torch.min(box1[3], boxes[:, 3]) 47 | 48 | # compute the area of intersection 49 | intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0) 50 | 51 | # compute the area of both individual boxes 52 | box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) 53 | box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) 54 | 55 | # compute the area of union 56 | union = box1_area + box2_area - intersection 57 | 58 | # compute the IoU 59 | iou = intersection / union # Should be shape (n, ) 60 | if raw_output: 61 | return 0 if iou.numel() == 0 else iou 62 | 63 | # return indices of boxes with IoU > thres 64 | return torch.nonzero(iou > iou_thres).flatten() 65 | -------------------------------------------------------------------------------- /ultralytics/models/nas/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .model import NAS 4 | from .predict import NASPredictor 5 | from .val import NASValidator 6 | 7 | __all__ = 'NASPredictor', 'NASValidator', 'NAS' 8 | -------------------------------------------------------------------------------- /ultralytics/models/nas/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/nas/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/nas/__pycache__/model.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/nas/__pycache__/model.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/nas/__pycache__/predict.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/nas/__pycache__/predict.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/nas/__pycache__/val.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/nas/__pycache__/val.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/nas/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.engine.predictor import BasePredictor 6 | from ultralytics.engine.results import Results 7 | from ultralytics.utils import ops 8 | from ultralytics.utils.ops import xyxy2xywh 9 | 10 | 11 | class NASPredictor(BasePredictor): 12 | 13 | def postprocess(self, preds_in, img, orig_imgs): 14 | """Postprocesses predictions and returns a list of Results objects.""" 15 | 16 | # Cat boxes and class scores 17 | boxes = xyxy2xywh(preds_in[0][0]) 18 | preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1) 19 | 20 | preds = ops.non_max_suppression(preds, 21 | self.args.conf, 22 | self.args.iou, 23 | agnostic=self.args.agnostic_nms, 24 | max_det=self.args.max_det, 25 | classes=self.args.classes) 26 | 27 | results = [] 28 | for i, pred in enumerate(preds): 29 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 30 | if not isinstance(orig_imgs, torch.Tensor): 31 | pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) 32 | path = self.batch[0] 33 | img_path = path[i] if isinstance(path, list) else path 34 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) 35 | return results 36 | -------------------------------------------------------------------------------- /ultralytics/models/nas/val.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.models.yolo.detect import DetectionValidator 6 | from ultralytics.utils import ops 7 | from ultralytics.utils.ops import xyxy2xywh 8 | 9 | __all__ = ['NASValidator'] 10 | 11 | 12 | class NASValidator(DetectionValidator): 13 | 14 | def postprocess(self, preds_in): 15 | """Apply Non-maximum suppression to prediction outputs.""" 16 | boxes = xyxy2xywh(preds_in[0][0]) 17 | preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1) 18 | return ops.non_max_suppression(preds, 19 | self.args.conf, 20 | self.args.iou, 21 | labels=self.lb, 22 | multi_label=False, 23 | agnostic=self.args.single_cls, 24 | max_det=self.args.max_det, 25 | max_time_img=0.5) 26 | -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .model import RTDETR 4 | from .predict import RTDETRPredictor 5 | from .val import RTDETRValidator 6 | 7 | __all__ = 'RTDETRPredictor', 'RTDETRValidator', 'RTDETR' 8 | -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/rtdetr/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/__pycache__/model.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/rtdetr/__pycache__/model.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/__pycache__/predict.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/rtdetr/__pycache__/predict.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/__pycache__/train.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/rtdetr/__pycache__/train.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/__pycache__/val.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/rtdetr/__pycache__/val.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/rtdetr/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.data.augment import LetterBox 6 | from ultralytics.engine.predictor import BasePredictor 7 | from ultralytics.engine.results import Results 8 | from ultralytics.utils import ops 9 | 10 | 11 | class RTDETRPredictor(BasePredictor): 12 | 13 | def postprocess(self, preds, img, orig_imgs): 14 | """Postprocess predictions and returns a list of Results objects.""" 15 | nd = preds[0].shape[-1] 16 | bboxes, scores = preds[0].split((4, nd - 4), dim=-1) 17 | results = [] 18 | for i, bbox in enumerate(bboxes): # (300, 4) 19 | bbox = ops.xywh2xyxy(bbox) 20 | score, cls = scores[i].max(-1, keepdim=True) # (300, 1) 21 | idx = score.squeeze(-1) > self.args.conf # (300, ) 22 | if self.args.classes is not None: 23 | idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx 24 | pred = torch.cat([bbox, score, cls], dim=-1)[idx] # filter 25 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 26 | oh, ow = orig_img.shape[:2] 27 | if not isinstance(orig_imgs, torch.Tensor): 28 | pred[..., [0, 2]] *= ow 29 | pred[..., [1, 3]] *= oh 30 | path = self.batch[0] 31 | img_path = path[i] if isinstance(path, list) else path 32 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) 33 | return results 34 | 35 | def pre_transform(self, im): 36 | """Pre-transform input image before inference. 37 | 38 | Args: 39 | im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list. 40 | 41 | Return: A list of transformed imgs. 42 | """ 43 | # 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Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | 8 | # darknet53 backbone 9 | backbone: 10 | # [from, number, module, args] 11 | [[-1, 1, Conv, [32, 3, 1]], # 0 12 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 13 | [-1, 1, Bottleneck, [64]], 14 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 15 | [-1, 2, Bottleneck, [128]], 16 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 17 | [-1, 8, Bottleneck, [256]], 18 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 19 | [-1, 8, Bottleneck, [512]], 20 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 21 | [-1, 4, Bottleneck, [1024]], # 10 22 | ] 23 | 24 | # YOLOv3-SPP head 25 | head: 26 | [[-1, 1, Bottleneck, [1024, False]], 27 | [-1, 1, SPP, [512, [5, 9, 13]]], 28 | [-1, 1, Conv, [1024, 3, 1]], 29 | [-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 31 | 32 | [-2, 1, Conv, [256, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 35 | [-1, 1, Bottleneck, [512, False]], 36 | [-1, 1, Bottleneck, [512, False]], 37 | [-1, 1, Conv, [256, 1, 1]], 38 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 39 | 40 | [-2, 1, Conv, [128, 1, 1]], 41 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 42 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 43 | [-1, 1, Bottleneck, [256, False]], 44 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 45 | 46 | [[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5) 47 | ] 48 | -------------------------------------------------------------------------------- /ultralytics/models/v3/yolov3-tiny.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | 8 | # YOLOv3-tiny backbone 9 | backbone: 10 | # [from, number, module, args] 11 | [[-1, 1, Conv, [16, 3, 1]], # 0 12 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 13 | [-1, 1, Conv, [32, 3, 1]], 14 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 15 | [-1, 1, Conv, [64, 3, 1]], 16 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 17 | [-1, 1, Conv, [128, 3, 1]], 18 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 19 | [-1, 1, Conv, [256, 3, 1]], 20 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 21 | [-1, 1, Conv, [512, 3, 1]], 22 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 23 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 24 | ] 25 | 26 | # YOLOv3-tiny head 27 | head: 28 | [[-1, 1, Conv, [1024, 3, 1]], 29 | [-1, 1, Conv, [256, 1, 1]], 30 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 31 | 32 | [-2, 1, Conv, [128, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 35 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 36 | 37 | [[19, 15], 1, Detect, [nc]], # Detect(P4, P5) 38 | ] 39 | -------------------------------------------------------------------------------- /ultralytics/models/v3/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | 8 | # darknet53 backbone 9 | backbone: 10 | # [from, number, module, args] 11 | [[-1, 1, Conv, [32, 3, 1]], # 0 12 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 13 | [-1, 1, Bottleneck, [64]], 14 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 15 | [-1, 2, Bottleneck, [128]], 16 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 17 | [-1, 8, Bottleneck, [256]], 18 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 19 | [-1, 8, Bottleneck, [512]], 20 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 21 | [-1, 4, Bottleneck, [1024]], # 10 22 | ] 23 | 24 | # YOLOv3 head 25 | head: 26 | [[-1, 1, Bottleneck, [1024, False]], 27 | [-1, 1, Conv, [512, 1, 1]], 28 | [-1, 1, Conv, [1024, 3, 1]], 29 | [-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 31 | 32 | [-2, 1, Conv, [256, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 35 | [-1, 1, Bottleneck, [512, False]], 36 | [-1, 1, Bottleneck, [512, False]], 37 | [-1, 1, Conv, [256, 1, 1]], 38 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 39 | 40 | [-2, 1, Conv, [128, 1, 1]], 41 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 42 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 43 | [-1, 1, Bottleneck, [256, False]], 44 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 45 | 46 | [[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5) 47 | ] 48 | -------------------------------------------------------------------------------- /ultralytics/models/v5/yolov5-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n' 6 | # [depth, width, max_channels] 7 | n: [0.33, 0.25, 1024] 8 | s: [0.33, 0.50, 1024] 9 | m: [0.67, 0.75, 1024] 10 | l: [1.00, 1.00, 1024] 11 | x: [1.33, 1.25, 1024] 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /ultralytics/models/v5/yolov5.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n' 6 | # [depth, width, max_channels] 7 | n: [0.33, 0.25, 1024] 8 | s: [0.33, 0.50, 1024] 9 | m: [0.67, 0.75, 1024] 10 | l: [1.00, 1.00, 1024] 11 | x: [1.33, 1.25, 1024] 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [1024]], 25 | [-1, 1, SPPF, [1024, 5]], # 9 26 | ] 27 | 28 | # YOLOv5 v6.0 head 29 | head: 30 | [[-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 3, C3, [512, False]], # 13 34 | 35 | [-1, 1, Conv, [256, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 39 | 40 | [-1, 1, Conv, [256, 3, 2]], 41 | [[-1, 14], 1, Concat, [1]], # cat head P4 42 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 43 | 44 | [-1, 1, Conv, [512, 3, 2]], 45 | [[-1, 10], 1, Concat, [1]], # cat head P5 46 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 47 | 48 | [[17, 20, 23], 1, Detect, [nc]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /ultralytics/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/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/models/v8/yolov8-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] 9 | s: [0.33, 0.50, 1024] 10 | m: [0.67, 0.75, 768] 11 | l: [1.00, 1.00, 512] 12 | x: [1.00, 1.25, 512] 13 | 14 | # YOLOv8.0x6 backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [768, True]] 26 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 27 | - [-1, 3, C2f, [1024, True]] 28 | - [-1, 1, SPPF, [1024, 5]] # 11 29 | 30 | # YOLOv8.0x6 head 31 | head: 32 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 33 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 34 | - [-1, 3, C2, [768, False]] # 14 35 | 36 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 37 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 38 | - [-1, 3, C2, [512, False]] # 17 39 | 40 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 41 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 42 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 43 | 44 | - [-1, 1, Conv, [256, 3, 2]] 45 | - [[-1, 17], 1, Concat, [1]] # cat head P4 46 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 47 | 48 | - [-1, 1, Conv, [512, 3, 2]] 49 | - [[-1, 14], 1, Concat, [1]] # cat head P5 50 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 51 | 52 | - [-1, 1, Conv, [768, 3, 2]] 53 | - [[-1, 11], 1, Concat, [1]] # cat head P6 54 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 55 | 56 | - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6) 57 | -------------------------------------------------------------------------------- /ultralytics/models/v8/yolov8-pose-p6.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 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-p6.yaml' will call yolov8-p6.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 | # YOLOv8.0x6 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, [768, 3, 2]] # 7-P5/32 26 | - [-1, 3, C2f, [768, True]] 27 | - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64 28 | - [-1, 3, C2f, [1024, True]] 29 | - [-1, 1, SPPF, [1024, 5]] # 11 30 | 31 | # YOLOv8.0x6 head 32 | head: 33 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 34 | - [[-1, 8], 1, Concat, [1]] # cat backbone P5 35 | - [-1, 3, C2, [768, False]] # 14 36 | 37 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 38 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 39 | - [-1, 3, C2, [512, False]] # 17 40 | 41 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 42 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 43 | - [-1, 3, C2, [256, False]] # 20 (P3/8-small) 44 | 45 | - [-1, 1, Conv, [256, 3, 2]] 46 | - [[-1, 17], 1, Concat, [1]] # cat head P4 47 | - [-1, 3, C2, [512, False]] # 23 (P4/16-medium) 48 | 49 | - [-1, 1, Conv, [512, 3, 2]] 50 | - [[-1, 14], 1, Concat, [1]] # cat head P5 51 | - [-1, 3, C2, [768, False]] # 26 (P5/32-large) 52 | 53 | - [-1, 1, Conv, [768, 3, 2]] 54 | - [[-1, 11], 1, Concat, [1]] # cat head P6 55 | - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge) 56 | 57 | - [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6) 58 | -------------------------------------------------------------------------------- /ultralytics/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/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/models/v8/yolov8.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 46 | -------------------------------------------------------------------------------- /ultralytics/models/v8/yolov8n.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect 3 | 4 | # Parameters 5 | nc: 80 # number of classes 6 | scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' 7 | # [depth, width, max_channels] 8 | n: [0.33, 0.125, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs 9 | s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs 10 | m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs 11 | l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs 12 | x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs 13 | 14 | # YOLOv8.0n backbone 15 | backbone: 16 | # [from, repeats, module, args] 17 | - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 18 | - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 19 | - [-1, 3, C2f, [128, True]] 20 | - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 21 | - [-1, 6, C2f, [256, True]] 22 | - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 23 | - [-1, 6, C2f, [512, True]] 24 | - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 25 | - [-1, 3, C2f, [1024, True]] 26 | - [-1, 1, SPPF, [1024, 5]] # 9 27 | 28 | # YOLOv8.0n head 29 | head: 30 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 31 | - [[-1, 6], 1, Concat, [1]] # cat backbone P4 32 | - [-1, 3, C2f, [512]] # 12 33 | 34 | - [-1, 1, nn.Upsample, [None, 2, 'nearest']] 35 | - [[-1, 4], 1, Concat, [1]] # cat backbone P3 36 | - [-1, 3, C2f, [256]] # 15 (P3/8-small) 37 | 38 | - [-1, 1, Conv, [256, 3, 2]] 39 | - [[-1, 12], 1, Concat, [1]] # cat head P4 40 | - [-1, 3, C2f, [512]] # 18 (P4/16-medium) 41 | 42 | - [-1, 1, Conv, [512, 3, 2]] 43 | - [[-1, 9], 1, Concat, [1]] # cat head P5 44 | - [-1, 3, C2f, [1024]] # 21 (P5/32-large) 45 | - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) 46 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.models.yolo import classify, detect, pose, segment 4 | 5 | __all__ = 'classify', 'segment', 'detect', 'pose' 6 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/classify/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.models.yolo.classify.predict import ClassificationPredictor, predict 4 | from ultralytics.models.yolo.classify.train import ClassificationTrainer, train 5 | from ultralytics.models.yolo.classify.val import ClassificationValidator, val 6 | 7 | __all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val' 8 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/classify/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/classify/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/classify/__pycache__/predict.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/classify/__pycache__/predict.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/classify/__pycache__/train.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/classify/__pycache__/train.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/classify/__pycache__/val.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/classify/__pycache__/val.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/classify/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.engine.predictor import BasePredictor 6 | from ultralytics.engine.results import Results 7 | from ultralytics.utils import DEFAULT_CFG, ROOT 8 | 9 | 10 | class ClassificationPredictor(BasePredictor): 11 | 12 | def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): 13 | super().__init__(cfg, overrides, _callbacks) 14 | self.args.task = 'classify' 15 | 16 | def preprocess(self, img): 17 | """Converts input image to model-compatible data type.""" 18 | if not isinstance(img, torch.Tensor): 19 | img = torch.stack([self.transforms(im) for im in img], dim=0) 20 | img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) 21 | return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 22 | 23 | def postprocess(self, preds, img, orig_imgs): 24 | """Postprocesses predictions to return Results objects.""" 25 | results = [] 26 | for i, pred in enumerate(preds): 27 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 28 | path = self.batch[0] 29 | img_path = path[i] if isinstance(path, list) else path 30 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred)) 31 | 32 | return results 33 | 34 | 35 | def predict(cfg=DEFAULT_CFG, use_python=False): 36 | """Run YOLO model predictions on input images/videos.""" 37 | model = cfg.model or 'yolov8n-cls.pt' # or "resnet18" 38 | source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ 39 | else 'https://ultralytics.com/images/bus.jpg' 40 | 41 | args = dict(model=model, source=source) 42 | if use_python: 43 | from ultralytics import YOLO 44 | YOLO(model)(**args) 45 | else: 46 | predictor = ClassificationPredictor(overrides=args) 47 | predictor.predict_cli() 48 | 49 | 50 | if __name__ == '__main__': 51 | predict() 52 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/detect/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .predict import DetectionPredictor, predict 4 | from .train import DetectionTrainer, train 5 | from .val import DetectionValidator, val 6 | 7 | __all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val' 8 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/detect/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/detect/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/detect/__pycache__/predict.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/detect/__pycache__/predict.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/detect/__pycache__/train.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/detect/__pycache__/train.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/detect/__pycache__/val.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/detect/__pycache__/val.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/detect/predict.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | import torch 4 | 5 | from ultralytics.engine.predictor import BasePredictor 6 | from ultralytics.engine.results import Results 7 | from ultralytics.utils import DEFAULT_CFG, ROOT, ops 8 | 9 | 10 | class DetectionPredictor(BasePredictor): 11 | 12 | def postprocess(self, preds, img, orig_imgs): 13 | """Postprocesses predictions and returns a list of Results objects.""" 14 | preds = ops.non_max_suppression(preds, 15 | self.args.conf, 16 | self.args.iou, 17 | agnostic=self.args.agnostic_nms, 18 | max_det=self.args.max_det, 19 | classes=self.args.classes) 20 | 21 | results = [] 22 | for i, pred in enumerate(preds): 23 | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs 24 | if not isinstance(orig_imgs, torch.Tensor): 25 | pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) 26 | path = self.batch[0] 27 | img_path = path[i] if isinstance(path, list) else path 28 | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) 29 | return results 30 | 31 | 32 | def predict(cfg=DEFAULT_CFG, use_python=False): 33 | """Runs YOLO model inference on input image(s).""" 34 | model = cfg.model or 'yolov8n.pt' 35 | source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ 36 | else 'https://ultralytics.com/images/bus.jpg' 37 | 38 | args = dict(model=model, source=source) 39 | if use_python: 40 | from ultralytics import YOLO 41 | YOLO(model)(**args) 42 | else: 43 | predictor = DetectionPredictor(overrides=args) 44 | predictor.predict_cli() 45 | 46 | 47 | if __name__ == '__main__': 48 | predict() 49 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/pose/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .predict import PosePredictor, predict 4 | from .train import PoseTrainer, train 5 | from .val import PoseValidator, val 6 | 7 | __all__ = 'PoseTrainer', 'train', 'PoseValidator', 'val', 'PosePredictor', 'predict' 8 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/pose/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/pose/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/pose/__pycache__/predict.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/pose/__pycache__/predict.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/pose/__pycache__/train.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/pose/__pycache__/train.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/pose/__pycache__/val.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/pose/__pycache__/val.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/segment/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .predict import SegmentationPredictor, predict 4 | from .train import SegmentationTrainer, train 5 | from .val import SegmentationValidator, val 6 | 7 | __all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val' 8 | -------------------------------------------------------------------------------- /ultralytics/models/yolo/segment/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/segment/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/segment/__pycache__/predict.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/segment/__pycache__/predict.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/segment/__pycache__/train.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/segment/__pycache__/train.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/models/yolo/segment/__pycache__/val.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/models/yolo/segment/__pycache__/val.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .tasks import (BaseModel, ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight, 4 | attempt_load_weights, guess_model_scale, guess_model_task, parse_model, torch_safe_load, 5 | yaml_model_load) 6 | 7 | __all__ = ('attempt_load_one_weight', 'attempt_load_weights', 'parse_model', 'yaml_model_load', 'guess_model_task', 8 | 'guess_model_scale', 'torch_safe_load', 'DetectionModel', 'SegmentationModel', 'ClassificationModel', 9 | 'BaseModel') 10 | -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/autobackend.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/autobackend.cpython-37.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/autobackend.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/autobackend.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/modules.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/modules.cpython-37.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/modules.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/modules.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/tasks.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/tasks.cpython-37.pyc -------------------------------------------------------------------------------- /ultralytics/nn/__pycache__/tasks.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/__pycache__/tasks.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/backbone/__pycache__/MobileNetV3.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/backbone/__pycache__/MobileNetV3.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | """ 3 | Ultralytics modules. Visualize with: 4 | 5 | from ultralytics.nn.modules import * 6 | import torch 7 | import os 8 | 9 | x = torch.ones(1, 128, 40, 40) 10 | m = Conv(128, 128) 11 | f = f'{m._get_name()}.onnx' 12 | torch.onnx.export(m, x, f) 13 | os.system(f'onnxsim {f} {f} && open {f}') 14 | """ 15 | 16 | from .block import (C1, C2, C3, C3TR, DFL, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, GhostBottleneck, 17 | HGBlock, HGStem, Proto, RepC3) 18 | from .conv import (CBAM, ChannelAttention, Concat, Conv, Conv2, ConvTranspose, DWConv, DWConvTranspose2d, Focus, 19 | GhostConv, LightConv, RepConv, SpatialAttention, ELA, SimAM, CoTAttention) 20 | from .head import Classify, Detect, Pose, RTDETRDecoder, Segment 21 | from .transformer import (AIFI, MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer, LayerNorm2d, 22 | MLPBlock, MSDeformAttn, TransformerBlock, TransformerEncoderLayer, TransformerLayer) 23 | 24 | __all__ = ('Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 25 | 'GhostConv', 'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'TransformerLayer', 26 | 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 27 | 'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect', 28 | 'Segment', 'Pose', 'Classify', 'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI', 29 | 'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP', 'ELA', 'SimAM', 'CoTAttention') 30 | -------------------------------------------------------------------------------- /ultralytics/nn/modules/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/modules/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/modules/__pycache__/block.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/modules/__pycache__/block.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/modules/__pycache__/conv.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/modules/__pycache__/conv.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/modules/__pycache__/head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/modules/__pycache__/head.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/modules/__pycache__/transformer.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/modules/__pycache__/transformer.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/nn/modules/__pycache__/utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/nn/modules/__pycache__/utils.cpython-38.pyc -------------------------------------------------------------------------------- /ultralytics/tracker/README.md: -------------------------------------------------------------------------------- 1 | ## Tracker 2 | 3 | ### Trackers 4 | 5 | - [x] ByteTracker 6 | - [x] BoT-SORT 7 | 8 | ### Usage 9 | 10 | python interface: 11 | 12 | ```python 13 | from ultralytics import YOLO 14 | 15 | model = YOLO("yolov8n.pt") # or a segmentation model .i.e yolov8n-seg.pt 16 | model.track( 17 | source="video/streams", 18 | stream=True, 19 | tracker="botsort.yaml", # or 'bytetrack.yaml' 20 | ..., 21 | ) 22 | ``` 23 | 24 | cli: 25 | 26 | ```bash 27 | yolo detect track source=... tracker=... 28 | yolo segment track source=... tracker=... 29 | ``` 30 | 31 | By default, trackers will use the configuration in `ultralytics/tracker/cfg`. 32 | We also support using a modified tracker config file. Please refer to the tracker config files 33 | in `ultralytics/tracker/cfg`. 34 | -------------------------------------------------------------------------------- /ultralytics/tracker/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .track import register_tracker 4 | from .trackers import BOTSORT, BYTETracker 5 | 6 | __all__ = 'register_tracker', 'BOTSORT', 'BYTETracker' # allow simpler import 7 | -------------------------------------------------------------------------------- /ultralytics/tracker/cfg/botsort.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | # Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT 3 | 4 | tracker_type: botsort # tracker type, ['botsort', 'bytetrack'] 5 | track_high_thresh: 0.5 # threshold for the first association 6 | track_low_thresh: 0.1 # threshold for the second association 7 | new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks 8 | track_buffer: 30 # buffer to calculate the time when to remove tracks 9 | match_thresh: 0.8 # threshold for matching tracks 10 | # min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now) 11 | # mot20: False # for tracker evaluation(not used for now) 12 | 13 | # BoT-SORT settings 14 | cmc_method: sparseOptFlow # method of global motion compensation 15 | # ReID model related thresh (not supported yet) 16 | proximity_thresh: 0.5 17 | appearance_thresh: 0.25 18 | with_reid: False 19 | -------------------------------------------------------------------------------- /ultralytics/tracker/cfg/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/tracker/trackers/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .bot_sort import BOTSORT 4 | from .byte_tracker import BYTETracker 5 | 6 | __all__ = 'BOTSORT', 'BYTETracker' # allow simpler import 7 | -------------------------------------------------------------------------------- /ultralytics/tracker/trackers/basetrack.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from collections import OrderedDict 4 | 5 | import numpy as np 6 | 7 | 8 | class TrackState: 9 | """Enumeration of possible object tracking states.""" 10 | 11 | New = 0 12 | Tracked = 1 13 | Lost = 2 14 | Removed = 3 15 | 16 | 17 | class BaseTrack: 18 | """Base class for object tracking, handling basic track attributes and operations.""" 19 | 20 | _count = 0 21 | 22 | track_id = 0 23 | is_activated = False 24 | state = TrackState.New 25 | 26 | history = OrderedDict() 27 | features = [] 28 | curr_feature = None 29 | score = 0 30 | start_frame = 0 31 | frame_id = 0 32 | time_since_update = 0 33 | 34 | # Multi-camera 35 | location = (np.inf, np.inf) 36 | 37 | @property 38 | def end_frame(self): 39 | """Return the last frame ID of the track.""" 40 | return self.frame_id 41 | 42 | @staticmethod 43 | def next_id(): 44 | """Increment and return the global track ID counter.""" 45 | BaseTrack._count += 1 46 | return BaseTrack._count 47 | 48 | def activate(self, *args): 49 | """Activate the track with the provided arguments.""" 50 | raise NotImplementedError 51 | 52 | def predict(self): 53 | """Predict the next state of the track.""" 54 | raise NotImplementedError 55 | 56 | def update(self, *args, **kwargs): 57 | """Update the track with new observations.""" 58 | raise NotImplementedError 59 | 60 | def mark_lost(self): 61 | """Mark the track as lost.""" 62 | self.state = TrackState.Lost 63 | 64 | def mark_removed(self): 65 | """Mark the track as removed.""" 66 | self.state = TrackState.Removed 67 | 68 | @staticmethod 69 | def reset_id(): 70 | """Reset the global track ID counter.""" 71 | BaseTrack._count = 0 72 | -------------------------------------------------------------------------------- /ultralytics/tracker/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/garlic-byte/yolov8_prune/b4f5c67d4505223d01e6a725c92505f45a126271/ultralytics/tracker/utils/__init__.py -------------------------------------------------------------------------------- /ultralytics/trackers/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from .bot_sort import BOTSORT 4 | from .byte_tracker import BYTETracker 5 | from .track import register_tracker 6 | 7 | __all__ = 'register_tracker', 'BOTSORT', 'BYTETracker' # allow simpler import 8 | -------------------------------------------------------------------------------- /ultralytics/trackers/basetrack.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from collections import OrderedDict 4 | 5 | import numpy as np 6 | 7 | 8 | class TrackState: 9 | """Enumeration of possible object tracking states.""" 10 | 11 | New = 0 12 | Tracked = 1 13 | Lost = 2 14 | Removed = 3 15 | 16 | 17 | class BaseTrack: 18 | """Base class for object tracking, handling basic track attributes and operations.""" 19 | 20 | _count = 0 21 | 22 | track_id = 0 23 | is_activated = False 24 | state = TrackState.New 25 | 26 | history = OrderedDict() 27 | features = [] 28 | curr_feature = None 29 | score = 0 30 | start_frame = 0 31 | frame_id = 0 32 | time_since_update = 0 33 | 34 | # Multi-camera 35 | location = (np.inf, np.inf) 36 | 37 | @property 38 | def end_frame(self): 39 | """Return the last frame ID of the track.""" 40 | return self.frame_id 41 | 42 | @staticmethod 43 | def next_id(): 44 | """Increment and return the global track ID counter.""" 45 | BaseTrack._count += 1 46 | return BaseTrack._count 47 | 48 | def activate(self, *args): 49 | """Activate the track with the provided arguments.""" 50 | raise NotImplementedError 51 | 52 | def predict(self): 53 | """Predict the next state of the track.""" 54 | raise NotImplementedError 55 | 56 | def update(self, *args, **kwargs): 57 | """Update the track with new observations.""" 58 | raise NotImplementedError 59 | 60 | def mark_lost(self): 61 | """Mark the track as lost.""" 62 | self.state = TrackState.Lost 63 | 64 | def mark_removed(self): 65 | """Mark the track as removed.""" 66 | self.state = TrackState.Removed 67 | 68 | @staticmethod 69 | def reset_id(): 70 | """Reset the global track ID counter.""" 71 | BaseTrack._count = 0 72 | -------------------------------------------------------------------------------- /ultralytics/trackers/utils/__init__.py: -------------------------------------------------------------------------------- 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session 9 | 10 | assert SETTINGS['raytune'] is True # verify integration is enabled 11 | except (ImportError, AssertionError): 12 | tune = None 13 | 14 | 15 | def on_fit_epoch_end(trainer): 16 | """Sends training metrics to Ray Tune at end of each epoch.""" 17 | if ray.tune.is_session_enabled(): 18 | metrics = trainer.metrics 19 | metrics['epoch'] = trainer.epoch 20 | session.report(metrics) 21 | 22 | 23 | callbacks = { 24 | 'on_fit_epoch_end': on_fit_epoch_end, } if tune else {} 25 | -------------------------------------------------------------------------------- /ultralytics/utils/callbacks/tensorboard.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr 4 | 5 | try: 6 | from torch.utils.tensorboard import SummaryWriter 7 | 8 | assert not TESTS_RUNNING # do not log pytest 9 | assert SETTINGS['tensorboard'] is True # verify integration is enabled 10 | 11 | # TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows 12 | except (ImportError, AssertionError, TypeError): 13 | SummaryWriter = None 14 | 15 | writer = None # TensorBoard SummaryWriter instance 16 | 17 | 18 | def _log_scalars(scalars, step=0): 19 | """Logs scalar values to TensorBoard.""" 20 | if writer: 21 | for k, v in scalars.items(): 22 | writer.add_scalar(k, v, step) 23 | 24 | 25 | def on_pretrain_routine_start(trainer): 26 | """Initialize TensorBoard logging with SummaryWriter.""" 27 | if SummaryWriter: 28 | try: 29 | global writer 30 | writer = SummaryWriter(str(trainer.save_dir)) 31 | prefix = colorstr('TensorBoard: ') 32 | LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/") 33 | except Exception as e: 34 | LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}') 35 | 36 | 37 | def on_batch_end(trainer): 38 | """Logs scalar statistics at the end of a training batch.""" 39 | _log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1) 40 | 41 | 42 | def on_fit_epoch_end(trainer): 43 | """Logs epoch metrics at end of training epoch.""" 44 | _log_scalars(trainer.metrics, trainer.epoch + 1) 45 | 46 | 47 | callbacks = { 48 | 'on_pretrain_routine_start': on_pretrain_routine_start, 49 | 'on_fit_epoch_end': on_fit_epoch_end, 50 | 'on_batch_end': on_batch_end} 51 | -------------------------------------------------------------------------------- /ultralytics/utils/errors.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from ultralytics.utils import emojis 4 | 5 | 6 | class HUBModelError(Exception): 7 | 8 | def __init__(self, message='Model not found. Please check model URL and try again.'): 9 | """Create an exception for when a model is not found.""" 10 | super().__init__(emojis(message)) 11 | -------------------------------------------------------------------------------- /ultralytics/utils/patches.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | """ 3 | Monkey patches to update/extend functionality of existing functions 4 | """ 5 | 6 | from pathlib import Path 7 | 8 | import cv2 9 | import numpy as np 10 | import torch 11 | 12 | # OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------ 13 | _imshow = cv2.imshow # copy to avoid recursion errors 14 | 15 | 16 | def imread(filename, flags=cv2.IMREAD_COLOR): 17 | return cv2.imdecode(np.fromfile(filename, np.uint8), flags) 18 | 19 | 20 | def imwrite(filename, img): 21 | try: 22 | cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) 23 | return True 24 | except Exception: 25 | return False 26 | 27 | 28 | def imshow(path, im): 29 | _imshow(path.encode('unicode_escape').decode(), im) 30 | 31 | 32 | # PyTorch functions ---------------------------------------------------------------------------------------------------- 33 | _torch_save = torch.save # copy to avoid recursion errors 34 | 35 | 36 | def torch_save(*args, **kwargs): 37 | """Use dill (if exists) to serialize the lambda functions where pickle does not do this.""" 38 | try: 39 | import dill as pickle 40 | except ImportError: 41 | import pickle 42 | 43 | if 'pickle_module' not in kwargs: 44 | kwargs['pickle_module'] = pickle 45 | return _torch_save(*args, **kwargs) 46 | -------------------------------------------------------------------------------- /ultralytics/yolo/__init__.py: -------------------------------------------------------------------------------- 1 | # Ultralytics YOLO 🚀, AGPL-3.0 license 2 | 3 | from . import v8 4 | 5 | __all__ = 'v8', # tuple or list 6 | -------------------------------------------------------------------------------- /ultralytics/yolo/cfg/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.cfg'] = importlib.import_module('ultralytics.cfg') 8 | 9 | LOGGER.warning("WARNING ⚠️ 'ultralytics.yolo.cfg' is deprecated since '8.0.136' and will be removed in '8.1.0'. " 10 | "Please use 'ultralytics.cfg' instead.") 11 | -------------------------------------------------------------------------------- /ultralytics/yolo/data/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.data'] = importlib.import_module('ultralytics.data') 8 | # This is for updating old cls models, or the way in following warning won't work. 9 | sys.modules['ultralytics.yolo.data.augment'] = importlib.import_module('ultralytics.data.augment') 10 | 11 | DATA_WARNING = """WARNING ⚠️ 'ultralytics.yolo.data' is deprecated since '8.0.136' and will be removed in '8.1.0'. Please use 'ultralytics.data' instead. 12 | Note this warning may be related to loading older models. You can update your model to current structure with: 13 | import torch 14 | ckpt = torch.load("model.pt") # applies to both official and custom models 15 | torch.save(ckpt, "updated-model.pt") 16 | """ 17 | LOGGER.warning(DATA_WARNING) 18 | -------------------------------------------------------------------------------- /ultralytics/yolo/engine/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.engine'] = importlib.import_module('ultralytics.engine') 8 | 9 | LOGGER.warning("WARNING ⚠️ 'ultralytics.yolo.engine' is deprecated since '8.0.136' and will be removed in '8.1.0'. " 10 | "Please use 'ultralytics.engine' instead.") 11 | -------------------------------------------------------------------------------- /ultralytics/yolo/utils/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.utils'] = importlib.import_module('ultralytics.utils') 8 | 9 | UTILS_WARNING = """WARNING ⚠️ 'ultralytics.yolo.utils' is deprecated since '8.0.136' and will be removed in '8.1.0'. Please use 'ultralytics.utils' instead. 10 | Note this warning may be related to loading older models. You can update your model to current structure with: 11 | import torch 12 | ckpt = torch.load("model.pt") # applies to both official and custom models 13 | torch.save(ckpt, "updated-model.pt") 14 | """ 15 | LOGGER.warning(UTILS_WARNING) 16 | -------------------------------------------------------------------------------- /ultralytics/yolo/v8/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import sys 3 | 4 | from ultralytics.utils import LOGGER 5 | 6 | # Set modules in sys.modules under their old name 7 | sys.modules['ultralytics.yolo.v8'] = importlib.import_module('ultralytics.models.yolo') 8 | 9 | LOGGER.warning("WARNING ⚠️ 'ultralytics.yolo.v8' is deprecated since '8.0.136' and will be removed in '8.1.0'. " 10 | "Please use 'ultralytics.models.yolo' instead.") 11 | --------------------------------------------------------------------------------