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car-detection-bayes
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mAP corrected to per-class

This commit is contained in:
Glenn Jocher 2018-09-10 15:23:04 +02:00
parent e7dab5a42f
commit cd753d23f7
1 changed files with 3 additions and 4 deletions
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7
test.py
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@ -8,7 +8,7 @@ parser = argparse.ArgumentParser()
parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch') parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file') parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file') parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.pt', help='path to weights file') parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.weights', help='path to weights file')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file') parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected') parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold') parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')
@ -117,11 +117,10 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
# Compute mean AP for this image # Compute mean AP for this image
mAP = AP.mean() mAP = AP.mean()
# Append image mAP to list of validation mAPs # Append image mAP to list
mAPs.append(mAP)
# Print image mAP and running mean mAP # Print image mAP and running mean mAP
print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, AP, np.mean(mAPs))) print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, np.mean(mAPs)))
print('Mean Average Precision: %.4f' % np.mean(mAPs)) print('Mean Average Precision: %.4f' % np.mean(mAPs))