ultralytics 8.0.65 YOLOv8 Pose models (ultralytics#1347)

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: Glenn Jocher <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mert Can Demir <[email protected]>
Co-authored-by: Laughing <[email protected]>
Co-authored-by: Fabian Greavu <[email protected]>
Co-authored-by: Yonghye Kwon <[email protected]>
Co-authored-by: Eric Pedley <[email protected]>
Co-authored-by: JustasBart <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Aarni Koskela <[email protected]>
Co-authored-by: Sergio Sanchez <[email protected]>
Co-authored-by: Bogdan Gheorghe <[email protected]>
Co-authored-by: Jaap van de Loosdrecht <[email protected]>
Co-authored-by: Noobtoss <[email protected]>
Co-authored-by: nerdyespresso <[email protected]>
Co-authored-by: Farid Inawan <[email protected]>
Co-authored-by: Laughing-q <[email protected]>
Co-authored-by: Alexander Duda <[email protected]>
Co-authored-by: Mehran Ghandehari <[email protected]>
Co-authored-by: Snyk bot <[email protected]>
Co-authored-by: majid nasiri <[email protected]>

.github/workflows/ci.yaml

@@ -43,21 +43,7 @@ jobs:
           python --version
           pip --version
           pip list
-      - name: Test HUB training (Python Usage 1)
-        shell: python
-        env:
-          APIKEY: ${{ secrets.ULTRALYTICS_HUB_APIKEY }}
-        run: |
-          import os
-          from pathlib import Path
-          from ultralytics import YOLO, hub
-          from ultralytics.yolo.utils import USER_CONFIG_DIR
-          Path(USER_CONFIG_DIR / 'settings.yaml').unlink()
-          key = os.environ['APIKEY']
-          hub.reset_model(key)
-          model = YOLO('https://hub.ultralytics.com/models/' + key)
-          model.train()
-      - name: Test HUB training (Python Usage 2)
+      - name: Test HUB training
         shell: python
         env:
           APIKEY: ${{ secrets.ULTRALYTICS_HUB_APIKEY }}
@@ -73,36 +59,6 @@ jobs:
           hub.login(key)
           model = YOLO('https://hub.ultralytics.com/models/' + model_id)
           model.train()
-      - name: Test HUB training (Python Usage 3)
-        shell: python
-        env:
-          APIKEY: ${{ secrets.ULTRALYTICS_HUB_APIKEY }}
-        run: |
-          import os
-          from pathlib import Path
-          from ultralytics import YOLO, hub
-          from ultralytics.yolo.utils import USER_CONFIG_DIR
-          Path(USER_CONFIG_DIR / 'settings.yaml').unlink()
-          key = os.environ['APIKEY']
-          hub.reset_model(key)
-          model = YOLO(key)
-          model.train()
-      - name: Test HUB training (Python Usage 4)
-        shell: python
-        env:
-          APIKEY: ${{ secrets.ULTRALYTICS_HUB_APIKEY }}
-        run: |
-          import os
-          from pathlib import Path
-          from ultralytics import YOLO, hub
-          from ultralytics.yolo.utils import USER_CONFIG_DIR
-          Path(USER_CONFIG_DIR / 'settings.yaml').unlink()
-          key = os.environ['APIKEY']
-          hub.reset_model(key)
-          key, model_id = key.split('_')
-          hub.login(key)
-          model = YOLO(model_id)
-          model.train()
 
   Benchmarks:
     runs-on: ${{ matrix.os }}
@@ -154,6 +110,11 @@ jobs:
         run: |
           from ultralytics.yolo.utils.benchmarks import benchmark
           benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.61)
+      - name: Benchmark PoseModel
+        shell: python
+        run: |
+          from ultralytics.yolo.utils.benchmarks import benchmark
+          benchmark(model='${{ matrix.model }}-pose.pt', imgsz=160, half=False, hard_fail=0.0)
       - name: Benchmark Summary
         run: |
           cat benchmarks.log
@@ -200,30 +161,38 @@ jobs:
           python --version
           pip --version
           pip list
-      - name: Test detection
+      - name: Test Detect
+        shell: bash  # for Windows compatibility
+        run: |
+          yolo detect train data=coco8.yaml model=yolov8n.yaml epochs=1 imgsz=32
+          yolo detect train data=coco8.yaml model=yolov8n.pt epochs=1 imgsz=32
+          yolo detect val data=coco8.yaml model=runs/detect/train/weights/last.pt imgsz=32
+          yolo detect predict model=runs/detect/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
+          yolo export model=runs/detect/train/weights/last.pt imgsz=32 format=torchscript
+      - name: Test Segment
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=detect mode=train data=coco8.yaml model=yolov8n.yaml epochs=1 imgsz=32
-          yolo task=detect mode=train data=coco8.yaml model=yolov8n.pt epochs=1 imgsz=32
-          yolo task=detect mode=val data=coco8.yaml model=runs/detect/train/weights/last.pt imgsz=32
-          yolo task=detect mode=predict model=runs/detect/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo mode=export model=runs/detect/train/weights/last.pt imgsz=32 format=torchscript
-      - name: Test segmentation
+          yolo segment train data=coco8-seg.yaml model=yolov8n-seg.yaml epochs=1 imgsz=32
+          yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=1 imgsz=32
+          yolo segment val data=coco8-seg.yaml model=runs/segment/train/weights/last.pt imgsz=32
+          yolo segment predict model=runs/segment/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
+          yolo export model=runs/segment/train/weights/last.pt imgsz=32 format=torchscript
+      - name: Test Classify
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=segment mode=train data=coco8-seg.yaml model=yolov8n-seg.yaml epochs=1 imgsz=32
-          yolo task=segment mode=train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=1 imgsz=32
-          yolo task=segment mode=val data=coco8-seg.yaml model=runs/segment/train/weights/last.pt imgsz=32
-          yolo task=segment mode=predict model=runs/segment/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo mode=export model=runs/segment/train/weights/last.pt imgsz=32 format=torchscript
-      - name: Test classification
+          yolo classify train data=imagenet10 model=yolov8n-cls.yaml epochs=1 imgsz=32
+          yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=1 imgsz=32
+          yolo classify val data=imagenet10 model=runs/classify/train/weights/last.pt imgsz=32
+          yolo classify predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
+          yolo export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
+      - name: Test Pose
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=classify mode=train data=imagenet10 model=yolov8n-cls.yaml epochs=1 imgsz=32
-          yolo task=classify mode=train data=imagenet10 model=yolov8n-cls.pt epochs=1 imgsz=32
-          yolo task=classify mode=val data=imagenet10 model=runs/classify/train/weights/last.pt imgsz=32
-          yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo mode=export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
+          yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=1 imgsz=32
+          yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=1 imgsz=32
+          yolo pose val data=coco8-pose.yaml model=runs/pose/train/weights/last.pt imgsz=32
+          yolo pose predict model=runs/pose/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
+          yolo export model=runs/pose/train/weights/last.pt imgsz=32 format=torchscript
       - name: Pytest tests
         shell: bash  # for Windows compatibility
         run: pytest tests

README.md

@@ -109,7 +109,10 @@ YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more example
 
 ## <div align="center">Models</div>
 
-All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
 Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
@@ -174,6 +177,28 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
 
 </details>
 
+<details><summary>Pose</summary>
+
+See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with these models.
+
+| Model                                                                                                | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt)       | 640                   | -                    | 49.7                  | -                              | -                                   | 3.3                | 9.2               |
+| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt)       | 640                   | -                    | 59.2                  | -                              | -                                   | 11.6               | 30.2              |
+| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640                   | -                    | 63.6                  | -                              | -                                   | 26.4               | 81.0              |
+| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640                   | -                    | 67.0                  | -                              | -                                   | 44.4               | 168.6             |
+| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | -                    | 68.9                  | -                              | -                                   | 69.4               | 263.2             |
+| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | -                    | 71.5                  | -                              | -                                   | 99.1               | 1066.4            |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
+  dataset.
+  <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
+  instance.
+  <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
+
+</details>
+
 ## <div align="center">Integrations</div>
 
 <br>

README.zh-CN.md

@@ -163,6 +163,28 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
 
 </details>
 
+<details><summary>Pose</summary>
+
+See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with these models.
+
+| Model                                                                                                | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt)       | 640                   | -                    | 49.7                  | -                              | -                                   | 3.3                | 9.2               |
+| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt)       | 640                   | -                    | 59.2                  | -                              | -                                   | 11.6               | 30.2              |
+| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640                   | -                    | 63.6                  | -                              | -                                   | 26.4               | 81.0              |
+| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640                   | -                    | 67.0                  | -                              | -                                   | 44.4               | 168.6             |
+| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | -                    | 68.9                  | -                              | -                                   | 69.4               | 263.2             |
+| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | -                    | 71.5                  | -                              | -                                   | 99.1               | 1066.4            |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
+  dataset.
+  <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
+  instance.
+  <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
+
+</details>
+
 ## <div align="center">模块集成</div>
 
 <br>

docker/Dockerfile

@@ -2,7 +2,7 @@
 # Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
 # Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
 
-# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
 FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
 
 # Downloads to user config dir

docs/index.md

@@ -42,4 +42,4 @@ Since its launch YOLO has been employed in various applications, including auton
 
 YOLOv8 is designed with a strong focus on speed, size, and accuracy, making it a compelling choice for various vision AI tasks. It outperforms previous versions by incorporating innovations like a new backbone network, a new anchor-free split head, and new loss functions. These improvements enable YOLOv8 to deliver superior results, while maintaining a compact size and exceptional speed.
 
-Additionally, YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/keypoints.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
+Additionally, YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.

docs/modes/benchmark.md

@@ -1,7 +1,7 @@
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
 
 **Benchmark mode** is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks
-provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation)
+provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose)
 or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export
 formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
 their specific use case based on their requirements for speed and accuracy.

docs/modes/index.md

@@ -54,7 +54,7 @@ for applications such as surveillance systems or self-driving cars.
 ## [Benchmark](benchmark.md)
 
 Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide
-information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation)
+information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose)
 or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export
 formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
 their specific use case based on their requirements for speed and accuracy.

docs/modes/train.md

@@ -88,6 +88,8 @@ task.
 | `box`             | `7.5`    | box loss gain                                                               |
 | `cls`             | `0.5`    | cls loss gain (scale with pixels)                                           |
 | `dfl`             | `1.5`    | dfl loss gain                                                               |
+| `pose`            | `12.0`   | pose loss gain (pose-only)                                                  |
+| `kobj`            | `2.0`    | keypoint obj loss gain (pose-only)                                          |
 | `fl_gamma`        | `0.0`    | focal loss gamma (efficientDet default gamma=1.5)                           |
 | `label_smoothing` | `0.0`    | label smoothing (fraction)                                                  |
 | `nbs`             | `64`     | nominal batch size                                                          |

docs/reference/ops.md

@@ -175,9 +175,9 @@ show_source: false
 show_root_toc_entry: false
 ---
 
-## scale_segments
+## scale_coords
 
-:::ultralytics.yolo.utils.ops.scale_segments
+:::ultralytics.yolo.utils.ops.scale_coords
 handler: python
 options:
 show_source: false
@@ -193,9 +193,9 @@ show_source: false
 show_root_toc_entry: false
 ---
 
-## clip_segments
+## clip_coords
 
-:::ultralytics.yolo.utils.ops.clip_segments
+:::ultralytics.yolo.utils.ops.clip_coords
 handler: python
 options:
 show_source: false

docs/tasks/classify.md

@@ -122,7 +122,7 @@ Use a trained YOLOv8n-cls model to run predictions on images.
         yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'  # predict with custom model
         ```
 
-Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
+See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
 
 ## Export
 
@@ -150,7 +150,7 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
         ```
 
 Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models,
-i.e. `yolo predict model=yolov8n-cls.onnx`.
+i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes.
 
 | Format                                                             | `format` Argument | Model                         | Metadata |
 |--------------------------------------------------------------------|-------------------|-------------------------------|----------|
@@ -167,3 +167,4 @@ i.e. `yolo predict model=yolov8n-cls.onnx`.
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-cls_web_model/`      | ✅        |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-cls_paddle_model/`   | ✅        |
 
+See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.