| ---
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| pipeline_tag: image-classification
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| license: apache-2.0
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| base_model: facebook/levit-256
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| library_name: zeromodels
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| tags:
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| - keras
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| - zeromodels
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| - image-classification
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| - vit
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| - backbone
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| - levit
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| - arxiv:2104.01136
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| - pytorch
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| - jax
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| - tf
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| ---
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|
|
| ## ***See [our collection](https://huggingface.co/collections/zeromodels/levit-6a937f8760837c24b7a51d25) for all versions of LeViT.***
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|
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| # Run LeViT with Keras 3: JAX, PyTorch, or TensorFlow
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|
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| [](https://github.com/IMvision12/ZeroModels) [](https://imvision12.github.io/ZeroModels/classification_backbones/) [](https://huggingface.co/collections/zeromodels/levit-6a937f8760837c24b7a51d25)
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|
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| # zeromodels/levit-256
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|
|
| Paper: [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference (arXiv:2104.01136)](https://arxiv.org/abs/2104.01136) · [HF Papers](https://huggingface.co/papers/2104.01136)
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|
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| LeViT is a hybrid convolution/transformer image classifier built for fast inference: a four-layer conv stem downsamples the image 16x, then three attention stages (each adding a learnable 2D relative-position bias) run over the tokens, with a BatchNorm fused into every linear layer and Hardswish activations. The released checkpoints are distilled - a second classification head is averaged with the first at inference. Larger LeViT (hidden sizes 256/384/512).
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|
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| For more details on the model, please go to Meta's original [model card](https://huggingface.co/facebook/levit-256).
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|
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| Pure-**Keras 3** conversion of [`facebook/levit-256`](https://huggingface.co/facebook/levit-256) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
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|
|
| ## ✨ Quick start
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|
|
| ```python
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| import os
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|
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| os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
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|
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| from PIL import Image
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| from zeromodels.models.levit import LevitImageClassify, LevitModel, LevitImageProcessor
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|
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| model = LevitImageClassify.from_weights("zeromodels/levit-256")
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| processor = LevitImageProcessor.from_weights("zeromodels/levit-256")
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|
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| image = Image.open("your_image.jpg").convert("RGB")
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| pixels = processor(image) # resize + normalize (normalization lives in the processor)
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| logits = model(pixels, training=False)
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| print(logits.shape) # (1, num_classes)
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|
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| # Feature extraction: the backbone without the classifier head
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| backbone = LevitModel.from_weights("zeromodels/levit-256")
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| features = backbone(pixels, training=False)
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| ```
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|
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| Load any LeViT variant the same way with `from_weights("zeromodels/<variant>")`:
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|
|
| | Variant | Hub |
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| |---|---|
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| | `levit-128S` | [`zeromodels/levit-128S`](https://huggingface.co/zeromodels/levit-128S) |
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| | `levit-128` | [`zeromodels/levit-128`](https://huggingface.co/zeromodels/levit-128) |
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| | `levit-192` | [`zeromodels/levit-192`](https://huggingface.co/zeromodels/levit-192) |
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| | `levit-256` | [`zeromodels/levit-256`](https://huggingface.co/zeromodels/levit-256) |
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| | `levit-384` | [`zeromodels/levit-384`](https://huggingface.co/zeromodels/levit-384) |
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|
|
| ## Tips
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|
|
| - Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
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| - ImageNet normalization is baked into the model, so pass raw `[0, 255]` pixels.
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| - Preprocess by resizing the shortest edge to 256 and center-cropping 224 (shown above) to match the reference; a plain `resize((224, 224))` is close and also works.
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| - `LevitImageClassify` averages the two distillation heads internally; `LevitModel.from_weights(...)` gives the backbone (the final token sequence, no head).
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| - See [Classification backbones](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
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| - Community / upstream safetensors still work via the `hf:` prefix, e.g. `LevitImageClassify.from_weights("hf:facebook/levit-256")`.
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|
|
| ## Special Thanks
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|
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| A huge thank you to the Meta AI LeViT authors for creating and releasing these models.
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|
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| License: Apache 2.0.
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|
