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48 lines
1.6 KiB
Markdown
48 lines
1.6 KiB
Markdown
# candle-yolo-v8: Object Detection and Pose Estimation
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This is a port of [Ultralytics
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YOLOv8](https://github.com/ultralytics/ultralytics). The implementation is based
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on the [tinygrad
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version](https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py)
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and on the model architecture described in this
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[issue](https://github.com/ultralytics/ultralytics/issues/189). The supported
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tasks are object detection and pose estimation.
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You can try this model online on the [Candle YOLOv8
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Space](https://huggingface.co/spaces/lmz/candle-yolo). The model then fully runs
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in your browser using WebAssembly - if you use a custom image it will never
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leave your phone/computer!
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## Running some example
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### Object Detection
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```bash
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cargo run --example yolo-v8 --release -- candle-examples/examples/yolo-v8/assets/bike.jpg
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```
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This prints details about the detected objects and generates a `bike.pp.jpg` file.
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Image source:
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[wikimedia](https://commons.wikimedia.org/wiki/File:Leading_group,_Giro_d%27Italia_2021,_Stage_15.jpg).
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### Pose Estimation
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```bash
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cargo run --example yolo-v8 --release -- \
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candle-examples/examples/yolo-v8/assets/bike.jpg --task pose
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```
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### Command-line flags
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- `--which`: select the model variant to be used, `n`, `s` , `m`, `l`, or `x` by
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increasing size and quality.
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- `--task`: `detect` for object detection and `pose` for pose estimation.
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- `--legend-size`: the size of the characters to print.
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- `--model`: use a local model file rather than downloading it from the hub.
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