fix readme.md

README.md

@@ -1,30 +1,81 @@
 ---
 pipeline_tag: image-classification
 license: other
-license_name: nvidia-segformer-license
-license_link: https://github.com/NVlabs/SegFormer/blob/master/LICENSE
 base_model: nvidia/mit-b3
 library_name: kerasformers
 tags:
-  - keras
-  - kerasformers
-  - image-classification
-  - mit
-  - pytorch
-  - jax
-  - tf
+- keras
+- kerasformers
+- image-classification
+- mit
+- backbone
+- arxiv:2105.15203
+- pytorch
+- jax
+- tf
 ---
 
-# mit_b3_in1k
+## ***See [our collection](https://huggingface.co/collections/kerasformers/mit-segformer-encoder-6a6e81367fda42bf79b426e8) for all versions of MiT.***
 
-Pure-Keras 3 image-classification weight for [kerasformers](https://github.com/IMvision12/KerasFormers), converted from [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3).
+# Run MiT with Keras 3: JAX, PyTorch, or TensorFlow
 
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-MiT-blue)](https://imvision12.github.io/KerasFormers/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-MiT%20collection-yellow)](https://huggingface.co/collections/kerasformers/mit-segformer-encoder-6a6e81367fda42bf79b426e8)
 
-## Usage
+# kerasformers/mit_b3_in1k
+
+Paper: [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers (arXiv:2105.15203)](https://arxiv.org/abs/2105.15203) · [HF Papers](https://huggingface.co/papers/2105.15203)
+
+MiT is the hierarchical Mix Transformer encoder from SegFormer, also usable for ImageNet classification. For full SegFormer segmentation heads, see the SegFormer collection.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/nvidia/mit-b3).
+
+Pure-**Keras 3** conversion of [`nvidia/mit-b3`](https://huggingface.co/nvidia/mit-b3) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is an **image-classification / backbone** checkpoint (`MiTImageClassify` / `MiTModel`).
+
+## ✨ Quick start
 
 ```python
-from kerasformers.models.mit import MiTImageClassify
-model = MiTImageClassify.from_weights("mit_b3_in1k")
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+import numpy as np
+from kerasformers.models.mit import MiTImageClassify, MiTModel
+
+model = MiTImageClassify.from_weights("kerasformers/mit_b3_in1k")
+backbone = MiTModel.from_weights(
+    "kerasformers/mit_b3_in1k", as_backbone=True
+)
+
+image = Image.open("your_image.jpg").convert("RGB")
+image = image.resize((224, 224))
+x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
+print(model(x).shape)  # (1, num_classes)
+feats = backbone(x)
+print(len(feats), [tuple(f.shape) for f in feats])
 ```
 
-License: **other** (nvidia-segformer-license), inherited from the upstream source.
+Load any MiT variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub |
+|---|---|
+| `mit_b0_in1k` | [`kerasformers/mit_b0_in1k`](https://huggingface.co/kerasformers/mit_b0_in1k) |
+| `mit_b1_in1k` | [`kerasformers/mit_b1_in1k`](https://huggingface.co/kerasformers/mit_b1_in1k) |
+| `mit_b2_in1k` | [`kerasformers/mit_b2_in1k`](https://huggingface.co/kerasformers/mit_b2_in1k) |
+| `mit_b3_in1k` | [`kerasformers/mit_b3_in1k`](https://huggingface.co/kerasformers/mit_b3_in1k) |
+| `mit_b4_in1k` | [`kerasformers/mit_b4_in1k`](https://huggingface.co/kerasformers/mit_b4_in1k) |
+| `mit_b5_in1k` | [`kerasformers/mit_b5_in1k`](https://huggingface.co/kerasformers/mit_b5_in1k) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- `MiTImageClassify` returns class logits; `MiTModel` returns features (`as_backbone=True` for multi-scale stages).
+- See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Upstream / timm checkpoints: `MiTImageClassify.from_weights("hf:nvidia/mit-b3")`.
+
+## Special Thanks
+
+A huge thank you to the MiT authors and the timm / Hub communities for creating and releasing these models.
+
+License: see YAML `license` (usually matches the upstream checkpoint).