updates
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5
test.py
5
test.py
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@ -126,12 +126,13 @@ def main(opt):
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print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), len(dataloader) * opt.batch_size, mean_P, mean_R, mean_mAP))
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print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), len(dataloader) * opt.batch_size, mean_P, mean_R, mean_mAP))
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# Print mAP per class
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# Print mAP per class
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')
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classes = load_classes(opt.class_path) # Extracts class labels from file
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classes = load_classes(opt.class_path) # Extracts class labels from file
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for i, c in enumerate(classes):
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for i, c in enumerate(classes):
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print('%15s: %-.4f' % (c, AP_accum[i] / AP_accum_count[i]))
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print('%15s: %-.4f' % (c, AP_accum[i] / AP_accum_count[i]))
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# Print mAP
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# Return mAP
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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return mean_mAP, mean_R, mean_P
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return mean_mAP, mean_R, mean_P
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@ -300,7 +300,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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# Filter out confidence scores below threshold
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# Filter out confidence scores below threshold
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# Get score and class with highest confidence
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# Get score and class with highest confidence
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# cross-class NMS
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# cross-class NMS (experimental)
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cross_class_nms = False
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cross_class_nms = False
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if cross_class_nms:
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if cross_class_nms:
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# thresh = 0.85
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# thresh = 0.85
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