This example shows how to implement YOLO object detection with ggml using pretrained model.
Download the model weights:
$ wget https://pjreddie.com/media/files/yolov3-tiny.weights
$ sha1sum yolov3-tiny.weights
40f3c11883bef62fd850213bc14266632ed4414f yolov3-tiny.weightsConvert the weights to GGUF format:
$ ./convert-yolov3-tiny.py yolov3-tiny.weights
yolov3-tiny.weights converted to yolov3-tiny.ggufObject detection:
$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/dog.jpg
$ ./yolov3-tiny -m yolov3-tiny.gguf -i dog.jpg
Layer 0 output shape: 416 x 416 x 16 x 1
Layer 1 output shape: 208 x 208 x 16 x 1
Layer 2 output shape: 208 x 208 x 32 x 1
Layer 3 output shape: 104 x 104 x 32 x 1
Layer 4 output shape: 104 x 104 x 64 x 1
Layer 5 output shape: 52 x 52 x 64 x 1
Layer 6 output shape: 52 x 52 x 128 x 1
Layer 7 output shape: 26 x 26 x 128 x 1
Layer 8 output shape: 26 x 26 x 256 x 1
Layer 9 output shape: 13 x 13 x 256 x 1
Layer 10 output shape: 13 x 13 x 512 x 1
Layer 11 output shape: 13 x 13 x 512 x 1
Layer 12 output shape: 13 x 13 x 1024 x 1
Layer 13 output shape: 13 x 13 x 256 x 1
Layer 14 output shape: 13 x 13 x 512 x 1
Layer 15 output shape: 13 x 13 x 255 x 1
Layer 18 output shape: 13 x 13 x 128 x 1
Layer 19 output shape: 26 x 26 x 128 x 1
Layer 20 output shape: 26 x 26 x 384 x 1
Layer 21 output shape: 26 x 26 x 256 x 1
Layer 22 output shape: 26 x 26 x 255 x 1
dog: 57%
car: 52%
truck: 56%
car: 62%
bicycle: 59%
Detected objects saved in 'predictions.jpg' (time: 0.357000 sec.)