Fix gitignore to format Docs datasets (#16071)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
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41 changed files with 767 additions and 744 deletions
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@ -13,7 +13,7 @@ The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialize
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## COCO-Pose Pretrained Models
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| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|------------------------------------------------------------------------------------------------------|-----------------------|-----------------------|--------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
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@ -91,7 +91,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -103,7 +103,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
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```
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=== "CLI"
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```bash
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yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
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```
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@ -42,18 +42,18 @@ The Ultralytics framework uses a YAML file format to define the dataset and mode
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```yaml
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# 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, ..]
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path: ../datasets/coco8-pose # dataset root dir
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train: images/train # train images (relative to 'path') 4 images
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val: images/val # val images (relative to 'path') 4 images
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test: # test images (optional)
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path: ../datasets/coco8-pose # dataset root dir
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train: images/train # train images (relative to 'path') 4 images
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val: images/val # val images (relative to 'path') 4 images
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test: # test images (optional)
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# Keypoints
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kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
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# Classes dictionary
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names:
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0: person
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0: person
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```
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The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
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@ -142,7 +142,7 @@ This conversion tool can be used to convert the COCO dataset or any dataset in t
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### What is the Ultralytics YOLO format for pose estimation?
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The Ultralytics YOLO format for pose estimation datasets involves labeling each image with a corresponding text file. Each row of the text file stores information about an object instance:
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The Ultralytics YOLO format for pose estimation datasets involves labeling each image with a corresponding text file. Each row of the text file stores information about an object instance:
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- Object class index
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- Object center coordinates (normalized x and y)
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@ -154,6 +154,7 @@ For 2D poses, keypoints include pixel coordinates. For 3D, each keypoint also ha
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### How do I use the COCO-Pose dataset with Ultralytics YOLO?
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To use the COCO-Pose dataset with Ultralytics YOLO:
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1. Download the dataset and prepare your label files in the YOLO format.
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2. Create a YAML configuration file specifying paths to training and validation images, keypoint shape, and class names.
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3. Use the configuration file for training:
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@ -164,12 +165,13 @@ To use the COCO-Pose dataset with Ultralytics YOLO:
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model = YOLO("yolov8n-pose.pt") # load pretrained model
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results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
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```
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For more information, visit [COCO-Pose](coco.md) and [train](../../modes/train.md) sections.
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### How can I add my own dataset for pose estimation in Ultralytics YOLO?
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To add your dataset:
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1. Convert your annotations to the Ultralytics YOLO format.
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2. Create a YAML configuration file specifying the dataset paths, number of classes, and class names.
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3. Use the configuration file to train your model:
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@ -180,7 +182,7 @@ To add your dataset:
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model = YOLO("yolov8n-pose.pt")
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results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
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```
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For complete steps, check the [Adding your own dataset](#adding-your-own-dataset) section.
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### What is the purpose of the dataset YAML file in Ultralytics YOLO?
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@ -192,7 +194,7 @@ path: ../datasets/coco8-pose
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train: images/train
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val: images/val
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names:
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0: person
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0: person
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```
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Read more about creating YAML configuration files in [Dataset YAML format](#dataset-yaml-format).
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@ -110,7 +110,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -120,10 +120,10 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
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# Train the model
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results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
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@ -140,7 +140,7 @@ To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you
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!!! Example "Inference Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -150,10 +150,10 @@ To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you
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# Run inference
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results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
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```
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=== "CLI"
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```bash
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# Run inference using a tiger-pose trained model
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yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
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