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README.md

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+---
+pipeline_tag: image-classification
+license: apache-2.0
+base_model: facebook/levit-128S
+library_name: zeromodels
+tags:
+- keras
+- zeromodels
+- image-classification
+- vit
+- backbone
+- levit
+- arxiv:2104.01136
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/zeromodels/levit-6a937f8760837c24b7a51d25) for all versions of LeViT.***
+
+# Run LeViT with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-Backbones-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-LeViT%20collection-yellow)](https://huggingface.co/collections/zeromodels/levit-6a937f8760837c24b7a51d25)
+
+# zeromodels/levit-128S
+
+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)
+
+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. The smallest LeViT (hidden sizes 128/256/384, depths 2/3/4), tuned for the fastest inference.
+
+For more details on the model, please go to Meta's original [model card](https://huggingface.co/facebook/levit-128S).
+
+Pure-**Keras 3** conversion of [`facebook/levit-128S`](https://huggingface.co/facebook/levit-128S) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+## ✨ Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+import keras
+import numpy as np
+from PIL import Image
+from zeromodels.models.levit import LevitImageClassify
+
+model = LevitImageClassify.from_weights("zeromodels/levit-128S")
+
+# LeViT preprocessing: resize the shortest edge to 256, then center-crop 224.
+image = Image.open("your_image.jpg").convert("RGB")
+w, h = image.size
+short = 256
+image = image.resize((round(short * w / h), short) if h <= w else (short, round(short * h / w)))
+w, h = image.size
+left, top = (w - 224) // 2, (h - 224) // 2
+image = image.crop((left, top, left + 224, top + 224))
+
+pixels = np.asarray(image, "float32")[None]  # raw [0, 255]; normalization is inside the model
+logits = model(pixels, training=False)
+print("top-1 ImageNet class id:", int(keras.ops.convert_to_numpy(logits)[0].argmax()))
+```
+
+Load any LeViT variant the same way with `from_weights("zeromodels/<variant>")`:
+
+| Variant | Hub |
+|---|---|
+| `levit-128S` | [`zeromodels/levit-128S`](https://huggingface.co/zeromodels/levit-128S) |
+| `levit-128` | [`zeromodels/levit-128`](https://huggingface.co/zeromodels/levit-128) |
+| `levit-192` | [`zeromodels/levit-192`](https://huggingface.co/zeromodels/levit-192) |
+| `levit-256` | [`zeromodels/levit-256`](https://huggingface.co/zeromodels/levit-256) |
+| `levit-384` | [`zeromodels/levit-384`](https://huggingface.co/zeromodels/levit-384) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
+- ImageNet normalization is baked into the model, so pass raw `[0, 255]` pixels.
+- 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.
+- `LevitImageClassify` averages the two distillation heads internally; `LevitModel.from_weights(...)` gives the backbone (the final token sequence, no head).
+- See [Classification backbones](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `LevitImageClassify.from_weights("hf:facebook/levit-128S")`.
+
+## Special Thanks
+
+A huge thank you to the Meta AI LeViT authors for creating and releasing these models.
+
+License: Apache 2.0.

model.weights.h5

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+size 31776288

zm_config.json

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+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.7",
+  "model_module": "zeromodels.models.levit",
+  "model_class": "LevitImageClassify",
+  "variant": "levit-128S",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "model_type": "levit",
+  "vision_config": {
+    "image_size": 224,
+    "num_channels": 3,
+    "kernel_size": 3,
+    "stride": 2,
+    "padding": 1,
+    "patch_size": 16,
+    "hidden_sizes": [
+      128,
+      256,
+      384
+    ],
+    "num_attention_heads": [
+      4,
+      6,
+      8
+    ],
+    "depths": [
+      2,
+      3,
+      4
+    ],
+    "key_dim": [
+      16,
+      16,
+      16
+    ],
+    "mlp_ratio": [
+      2,
+      2,
+      2
+    ],
+    "attention_ratio": [
+      2,
+      2,
+      2
+    ],
+    "num_classes": 1000,
+    "use_distillation": true
+  }
+}