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ultralytics 8.0.93 HUB docs and JSON2YOLO converter (#2431)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: 李际朝 <tubkninght@gmail.com>
Co-authored-by: Danny Kim <imbird0312@gmail.com>
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docs/modes/predict.md
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@ -68,29 +68,29 @@ whether each source can be used in streaming mode with `stream=True` ✅ and an
All supported arguments:
| Key | Value | Description |
|------------------|------------------------|----------------------------------------------------------|
| `source` | `'ultralytics/assets'` | source directory for images or videos |
| `conf` | `0.25` | object confidence threshold for detection |
| `iou` | `0.7` | intersection over union (IoU) threshold for NMS |
| `half` | `False` | use half precision (FP16) |
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| `show` | `False` | show results if possible |
| `save` | `False` | save images with results |
| `save_txt` | `False` | save results as .txt file |
| `save_conf` | `False` | save results with confidence scores |
| `save_crop` | `False` | save cropped images with results |
| `hide_labels` | `False` | hide labels |
| `hide_conf` | `False` | hide confidence scores |
| `max_det` | `300` | maximum number of detections per image |
| `vid_stride` | `False` | video frame-rate stride |
| `line_thickness` | `3` | bounding box thickness (pixels) |
| `visualize` | `False` | visualize model features |
| `augment` | `False` | apply image augmentation to prediction sources |
| `agnostic_nms` | `False` | class-agnostic NMS |
| `retina_masks` | `False` | use high-resolution segmentation masks |
| `classes` | `None` | filter results by class, i.e. class=0, or class=[0,2,3] |
| `boxes` | `True` | Show boxes in segmentation predictions |
| Key | Value | Description |
|----------------|------------------------|--------------------------------------------------------------------------------|
| `source` | `'ultralytics/assets'` | source directory for images or videos |
| `conf` | `0.25` | object confidence threshold for detection |
| `iou` | `0.7` | intersection over union (IoU) threshold for NMS |
| `half` | `False` | use half precision (FP16) |
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| `show` | `False` | show results if possible |
| `save` | `False` | save images with results |
| `save_txt` | `False` | save results as .txt file |
| `save_conf` | `False` | save results with confidence scores |
| `save_crop` | `False` | save cropped images with results |
| `hide_labels` | `False` | hide labels |
| `hide_conf` | `False` | hide confidence scores |
| `max_det` | `300` | maximum number of detections per image |
| `vid_stride` | `False` | video frame-rate stride |
| `line_width` | `None` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `visualize` | `False` | visualize model features |
| `augment` | `False` | apply image augmentation to prediction sources |
| `agnostic_nms` | `False` | class-agnostic NMS |
| `retina_masks` | `False` | use high-resolution segmentation masks |
| `classes` | `None` | filter results by class, i.e. class=0, or class=[0,2,3] |
| `boxes` | `True` | Show boxes in segmentation predictions |
## Image and Video Formats
@ -220,19 +220,19 @@ masks, classification logits, etc.) found in the results object
res_plotted = res[0].plot()
cv2.imshow("result", res_plotted)
```
| Argument | Description |
|--------------------------------|----------------------------------------------------------------------------------------|
| `conf (bool)` | Whether to plot the detection confidence score. |
| `line_width (float, optional)` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `font_size (float, optional)` | The font size of the text. If None, it is scaled to the image size. |
| `font (str)` | The font to use for the text. |
| `pil (bool)` | Whether to use PIL for image plotting. |
| `example (str)` | An example string to display. Useful for indicating the expected format of the output. |
| `img (numpy.ndarray)` | Plot to another image. if not, plot to original image. |
| `labels (bool)` | Whether to plot the label of bounding boxes. |
| `boxes (bool)` | Whether to plot the bounding boxes. |
| `masks (bool)` | Whether to plot the masks. |
| `probs (bool)` | Whether to plot classification probability. |
| Argument | Description |
|-------------------------------|----------------------------------------------------------------------------------------|
| `conf (bool)` | Whether to plot the detection confidence score. |
| `line_width (int, optional)` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `font_size (float, optional)` | The font size of the text. If None, it is scaled to the image size. |
| `font (str)` | The font to use for the text. |
| `pil (bool)` | Whether to use PIL for image plotting. |
| `example (str)` | An example string to display. Useful for indicating the expected format of the output. |
| `img (numpy.ndarray)` | Plot to another image. if not, plot to original image. |
| `labels (bool)` | Whether to plot the label of bounding boxes. |
| `boxes (bool)` | Whether to plot the bounding boxes. |
| `masks (bool)` | Whether to plot the masks. |
| `probs (bool)` | Whether to plot classification probability. |
## Streaming Source `for`-loop