Comments (2)
bowenc0221
commented on July 19, 2024
Thank you for your interest in our work. Evaluating box AP is already supported in this code, please follow my comment to generate boxes from masks:
Mask2Former/mask2former/maskformer_model.py
Lines 372 to 374 in 46af009
Unfortunately, the box AP is worse than Mask R-CNN, because the generated boxes are very sensitive to outliers: imaging the model predicts a single pixel that is very far from the mask, it won't affect mask AP much but it is catastrophic to box AP.
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VGrondin
commented on July 19, 2024
Got it, thanks for the information.
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Related Issues (20)
- How to understand the output of different tasks
- Using ground truth masks instead of the predicted ones
- No module named 'MultiScaleDeformableAttention', Please compile MultiScaleDeformableAttention CUDA op HOT 2
- As for training, how long does it take?
- HAVE ANYONE MEET SUCH ERROR WHEN TRAINING ON OWN DATASET HOT 1
- batch_size doesn't affect evaluation
- how use custom pre-trained backbone in mask2former HOT 1
- why swin accept different input size
- loading swintransformer
- Ambiguous checkpoint key error when running train_net.py HOT 1
- difference among different mode
- Prebuilt wheels provided via 3rd party repository
- Using COCO for the dataset, what is the appropriate adjustment for learning rate if using a single GPU
- Run in colab seems that there's a ModuleNotFoundError related to the MultiScaleDeformableAttention module.
- Poor Output image quality
- Mask loss with soft labels
- Custom dataset registration to use a model trained on Cityscapes for semantic segmentation.
- How should I fix the input size during testing? HOT 3
- Could you please let me know if anyone has successfully trained using the YouTube VIS 2021 dataset? How should the dataset be formatted?
- How to train only the semantic segmentation weights of ade20k?
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