civ
59 lines
2.0 KiB
Markdown
59 lines
2.0 KiB
Markdown
This example shows how to implement YOLO object detection with ggml using pretrained model.
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# YOLOv3-tiny
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Download the model weights:
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```bash
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$ wget https://pjreddie.com/media/files/yolov3-tiny.weights
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$ sha1sum yolov3-tiny.weights
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40f3c11883bef62fd850213bc14266632ed4414f yolov3-tiny.weights
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```
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Convert the weights to GGUF format:
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```bash
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$ ./convert-yolov3-tiny.py yolov3-tiny.weights
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yolov3-tiny.weights converted to yolov3-tiny.gguf
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```
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Alternatively, you can download the converted model from [HuggingFace](https://huggingface.co/rgerganov/yolo-gguf/resolve/main/yolov3-tiny.gguf)
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Object detection:
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```bash
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$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/dog.jpg
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$ ./yolov3-tiny -m yolov3-tiny.gguf -i dog.jpg
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load_model: using CUDA backend
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ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
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ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
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ggml_cuda_init: found 1 CUDA devices:
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Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
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Layer 0 output shape: 416 x 416 x 16 x 1
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Layer 1 output shape: 208 x 208 x 16 x 1
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Layer 2 output shape: 208 x 208 x 32 x 1
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Layer 3 output shape: 104 x 104 x 32 x 1
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Layer 4 output shape: 104 x 104 x 64 x 1
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Layer 5 output shape: 52 x 52 x 64 x 1
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Layer 6 output shape: 52 x 52 x 128 x 1
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Layer 7 output shape: 26 x 26 x 128 x 1
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Layer 8 output shape: 26 x 26 x 256 x 1
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Layer 9 output shape: 13 x 13 x 256 x 1
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Layer 10 output shape: 13 x 13 x 512 x 1
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Layer 11 output shape: 13 x 13 x 512 x 1
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Layer 12 output shape: 13 x 13 x 1024 x 1
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Layer 13 output shape: 13 x 13 x 256 x 1
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Layer 14 output shape: 13 x 13 x 512 x 1
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Layer 15 output shape: 13 x 13 x 255 x 1
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Layer 18 output shape: 13 x 13 x 128 x 1
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Layer 19 output shape: 26 x 26 x 128 x 1
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Layer 20 output shape: 26 x 26 x 384 x 1
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Layer 21 output shape: 26 x 26 x 256 x 1
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Layer 22 output shape: 26 x 26 x 255 x 1
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dog: 57%
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car: 52%
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truck: 56%
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car: 62%
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bicycle: 59%
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Detected objects saved in 'predictions.jpg' (time: 0.057000 sec.)
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``` |