Comments (1)
The error is caused by the fact that you load a checkpoint for a model trained on Microsoft COCO that has 80 + 1 categories, but you test it on the VOC07 dataset that has 20 + 1 categories. Specify another run_name when testing on VOC, e.g. --run_name=BlitzNet512_COCO+VOC07+12 or --run_name=BlitzNet512_VOC07+12.
Note that in this run the flag --segment will not have any effect since there are no segmentation annotations for VOC07 and this command will only evaluate the detection metrics. However, you can drop the estimated segmentations in the results folder by specifying the flag --save_first_n=K, where K defines first K images in the evaluation list. If K is greater than the dataset size, all the images will be dropped with their visualizations.
Don't hesitate to contact if something else goes wrong.
from blitznet.
Related Issues (20)
- python demo.py --run_name=BlitzNet300_COCO+VOC07+12 --x4 --detect --segment --eval_min_conf=0.5 --ckpt=1 HOT 7
- files = glob(osp.join(self.folder, '*{}'.format(self.data_format))) HOT 1
- tensorflow.python.framework.errors_impl.NotFoundError: /home/cbl/PycharmProjects/blitznet-master/Datasets/voc07-trainval-segmentation; No such file or directory HOT 1
- python training.py --run_name=BlitzNet300_x4_VOC0712_detseg --dataset=voc07+12-segmentation --trunk=resnet50 --x4 --batch_size=32 --optimizer=adam --detect --segment --max_iterations=65000 --lr_decay 40000 50000 HOT 1
- without Preparation3 ,can I train ? HOT 2
- ModuleNotFoundError: No module named 'progressbar' HOT 1
- 1a
- true_number_of_negatives calculation HOT 4
- About the arguments in class feed_forward HOT 1
- Model download HOT 2
- Demo results HOT 2
- Test on MS COCO dataset based on trained checkpoint HOT 3
- Some illogical problems occurred during the detection process.
- How to train on cityscapes? HOT 2
- How to calculate each class of IoU? HOT 4
- About MIoU result HOT 1
- Where is the deconvolution layers?
- Is there any other hidden settings or tricks for training HOT 4
- Pre-trained model and the interface HOT 1
- Download the pre-trained model
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