Comments (2)
There is no support for multi-label classification. It would be fairly straightforward to add a multi-label dataset, a loss, and use the pretrained models here and the rest of the training features.
There was a Kaggle challenge a few years back https://www.kaggle.com/c/planet-understanding-the-amazon-from-space that was multi-label with a lot of contestants using PyTorch. I have some code for this one too using older ideas that this repo evolved from.
from pytorch-image-models.
Hey thanks, ill check
from pytorch-image-models.
Related Issues (20)
- [FEATURE] Support resizing rectangular pos_embed HOT 3
- [BUG] TypeError: forward_head() got an unexpected keyword argument 'pre_logits' HOT 3
- [BUG] Swin transformer HOT 1
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- [FEATURE] Please add inetern image model
- How to Replicate Official Model Accuracy HOT 1
- [FEATURE] Add Florence-2 model weights HOT 4
- [BUG] Loading state dict in a feature extraction network HOT 1
- [BUG] `return_dict` option in feature extractor breaks torch.jit.script HOT 2
- inference error HOT 2
- [BUG] Transform not properly working with batching and Grayscale Images in ViT HOT 1
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- Accuracy keeps the same for several epoches HOT 1
- [BUG] MobileNetV4 Output Shape Is Wrong HOT 3
- Question about the `use_conv` parameter in timm.layers.mlp.Mlp
- [BUG] Error when using an image dataset with grayscale images HOT 1
- [BUG] CoAtNet_0 Model different from paper HOT 1
- [BUG] MultiQueryAttention2d incorrect Upsample usage (MobileNet v4) HOT 5
- [FEATURE] Support RDNet HOT 3
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from pytorch-image-models.