Merge remote-tracking branch 'origin/master'

pull/1/head
Glenn Jocher 5 years ago
commit 762b06de4b

@ -102,7 +102,7 @@ def detect(save_img=False):
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
@ -139,10 +139,10 @@ def detect(save_img=False):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='weights/yolov5m.pt', help='model.pt path')
parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='model.pt path')
parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=1024, help='inference size (pixels)')
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')

@ -52,7 +52,8 @@ class Model(nn.Module):
self.md = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc:
if nc and nc != self.md['nc']:
print('Overriding %s nc=%g with nc=%g' % (model_cfg, self.md['nc'], nc))
self.md['nc'] = nc # override yaml value
self.model, self.save = parse_model(self.md, ch=[ch]) # model, savelist, ch_out
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])

@ -77,8 +77,7 @@ def train(hyp):
os.remove(f)
# Create model
model = Model(opt.cfg).to(device)
assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
model = Model(opt.cfg, nc=data_dict['nc']).to(device)
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)

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