darknet-to-pytorch
Easily convert Darknet weights with cfgs to PyTorch!
Currently only support yolov3
, yolov3-spp
, and yolov3-tiny
,
since I don't think anyone would use Darknet
to implement RNNs
.
This repo is in active development. Issues are welcome.
Contribute
Currently, I don't know how Darknet C++ version
pads the feature map to
keep the size after max-pooling when stride = 1
.
Thus, loading official weights of yolov3-spp
and yolov3-tiny
may cause problem.
If you do know a bit, feel free to open an issue and tell me. Thank you.
Getting the weights and cfgs
cd src
bash get_weights.sh
Simple Guide
See main.py
net = get_net('yolov3-spp') # Currently support yolov3 / yolov3-spp / yolov3-tiny
net.summary() # will print the network layers summary
size = int(net.net_info['height'])
img, x = load_test_img('./src/dog-cycle-car.png', size) # Load the test image
raw_preds = net(x).detach() # Raw preds
preds = net.get_results(raw_preds, num_classes=80, conf_thres=0.5, nms_thres=0.4) # Transform to bboxes
print(preds)
print_preds(preds, img) # simple vis
DEMO
conf_threshold
is set to 0.5
.