Image Classification
LiteRT
LiteRT
ONNX
English
vision
botany
western-australia
dinov3
mixture-of-experts
adaround
fp8
int8
android
biodiversity
flora
Instructions to use thenukegun10x/PLantDetect-WA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use thenukegun10x/PLantDetect-WA with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Update src\models\moe_vit.py
Browse files- src/models/moe_vit.py +1 -1
src/models/moe_vit.py
CHANGED
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@@ -413,7 +413,7 @@ class MoEViT(nn.Module):
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self, x: torch.Tensor, pool: str = "patchmean"
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) -> torch.Tensor:
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"""Per-slice features (no head): [S, D] from [S, 3, H, W] slices."""
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-
tokens = self.stem.forward_features(x)
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return self.features_from_tokens(tokens, pool)
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def features_from_tokens(
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self, x: torch.Tensor, pool: str = "patchmean"
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) -> torch.Tensor:
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"""Per-slice features (no head): [S, D] from [S, 3, H, W] slices."""
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+
tokens = self.stem.forward_features(x) # [S, T, D]
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return self.features_from_tokens(tokens, pool)
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def features_from_tokens(
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