Comments (2)
bowenc0221
commented on July 19, 2024
I have not systematically evaluated the variance on the COCO dataset, but the gap (+/- 0.2 AP) seems reasonable.
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kevinLee1990
commented on July 19, 2024
@bowenc0221 Thx for your replying. I re-train your model again in my side, and it achieves 43.3% AP, which is still lower than the reported number (0.4% lower). More specifically, for AP_s, the variance is even larger in my side (-0.4, -0.7, -0.6). Do u have any idea or suggestion to address this issue?
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Related Issues (20)
- Installation expects CUDA_HOME on Apple Silicon Macs HOT 1
- 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?
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