Comments (2)
Thank you for your attention. Currently, our work only supports category-agnostic instance segmentation data augmentation, as reflected in the experimental results on COCO, where metrics are category-agnostic. We have provided details in the Implementation section. Experiments involving category-specific augmentation seem to yield unsatisfactory results.
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Thank you for your attention. Currently, our work only supports category-agnostic instance segmentation data augmentation, as reflected in the experimental results on COCO, where metrics are category-agnostic. We have provided details in the Implementation section. Experiments involving category-specific augmentation seem to yield unsatisfactory results.
Thank you for your attention. Currently, our work only supports category-agnostic instance segmentation data augmentation, as reflected in the experimental results on COCO, where metrics are category-agnostic. We have provided details in the Implementation section. Experiments involving category-specific augmentation seem to yield unsatisfactory results.
thanks for your reply and your open-source work.
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Related Issues (20)
- Training not working. HOT 4
- gt_classes in VOC2012.py HOT 4
- ./DataDiffusion/COCO_Train_5_images_t1_10layers_NoClass/Image/ HOT 1
- prompt txt files HOT 1
- About prompt in NYU dataset
- No module named 'torch.distributed.algorithms.join
- Accelerate training via training on multiple gpus HOT 1
- Weights about Depth Estimation
- CUDA out of memory - on 12 GB GPU
- Can't reproduce the results in other COCO-format instance segmentation dataset HOT 3
- exception when trying to generate data HOT 5
- generate_instance_coco HOT 2
- doing data augmentation with coco2017 dataset but no image or mask generate
- prepare NYU dataset
- adapt the synthetic data to Mask2Fomer model HOT 5
- dataset mode not defined while training HOT 1
- Error in init_latent in ptp_utils.py during training
- 在VOC2012.py中gt_classes为什么是1而不是select_class?
- Why is mapper_classes = [1] in VOC2012.py?
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