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model = DetectMultiBackend(path, device=device) # download/load FP32 model |
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model = DetectMultiBackend(path, device=device) # download/load FP32 model |
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# model = models.experimental.attempt_load(path, map_location=device) # download/load FP32 model |
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# model = models.experimental.attempt_load(path, map_location=device) # download/load FP32 model |
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else: |
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else: |
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cfg = list((Path(__file__).parent / 'models').rglob(f'{path.name}.yaml'))[0] # model.yaml path |
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cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path |
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model = Model(cfg, channels, classes) # create model |
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model = Model(cfg, channels, classes) # create model |
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if pretrained: |
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if pretrained: |
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ckpt = torch.load(attempt_download(path), map_location=device) # load |
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ckpt = torch.load(attempt_download(path), map_location=device) # load |
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Image.open('data/images/bus.jpg'), # PIL |
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Image.open('data/images/bus.jpg'), # PIL |
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np.zeros((320, 640, 3))] # numpy |
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np.zeros((320, 640, 3))] # numpy |
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results = model(imgs) # batched inference |
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results = model(imgs, size=320) # batched inference |
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results.print() |
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results.print() |
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results.save() |
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results.save() |