Comments (2)
Hello,
Yes due to the larger model size it works slow on cpu.
The model used here is u2net. You can retrain u2net-p or have a look into this issue
from cloth-segmentation.
Hello, Yes due to the larger model size it works slow on cpu. The model used here is u2net. You can retrain u2net-p or have a look into this issue
Thank you very much
from cloth-segmentation.
Related Issues (19)
- RuntimeError: CUDA out of memory. HOT 1
- Possible to adjust sensitivity threshold?
- [bug] labels should start from 1
- [question] Segmentation mask dimensions
- AttributeError: 'NoneType' object has no attribute 'to'; Issue with colab HOT 3
- Prorosal: Pack model for Huggingface inference HOT 2
- Is it possible to segregate the final output into each seperate label and have its original colour from the input image? HOT 2
- Inference error on trained checkpoints HOT 14
- final_label = first_channel + second_channel * 2 + third_channel * 3 conflict_mask = (final_label <= 3).astype("uint8") final_label = (conflict_mask) * final_label + (1 - conflict_mask) * 1 target_tensor = torch.as_tensor(final_label, dtype=torch.int64)
- 您好!如何将分割结果输出为RGB彩色图?
- Pretrained model file is not found using google drive link HOT 12
- Model isn't accurate HOT 1
- 多人图片的识别问题 HOT 15
- IsADirectoryError: [Errno 21] Is a directory: 'input_images/.ipynb_checkpoints' HOT 1
- TypeError: Caught TypeError in DataLoader worker process 0. and TypeError: 'float' object cannot be interpreted as an integer HOT 4
- How can upper body and Lower body segmentation can be separated out? HOT 1
- Getting the predictions HOT 6
- Can pretrain model to 11 class ?
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