fix readme.md

README.md

@@ -1,22 +1,92 @@
 ---
 pipeline_tag: zero-shot-image-classification
 license: apache-2.0
+base_model: google/siglip-base-patch16-384
 library_name: kerasformers
 tags:
 - keras
 - kerasformers
 - siglip
-- tf
-- jax
+- zero-shot-image-classification
+- vision
+- arxiv:2303.15343
 - pytorch
+- jax
+- tf
 ---
 
-# siglip_base_p16_384 (Keras 3)
+## ***See [our collection](https://huggingface.co/collections/kerasformers/siglip-6a6ab1b9bb61206cd508dccd) for all versions of SigLIP.***
+
+# Run SigLIP 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-SigLIP-blue)](https://imvision12.github.io/KerasFormers/siglip/) [![Collection](https://img.shields.io/badge/HF-SigLIP%20collection-yellow)](https://huggingface.co/collections/kerasformers/siglip-6a6ab1b9bb61206cd508dccd)
+
+# kerasformers/siglip_base_p16_384
+
+Paper: [Sigmoid Loss for Language Image Pre-Training (arXiv:2303.15343)](https://arxiv.org/abs/2303.15343) · [HF Papers](https://huggingface.co/papers/2303.15343)
+
+SigLIP is a vision + text dual encoder trained with a pairwise sigmoid loss instead of CLIP's softmax contrastive loss. Per-pair training scales to large batches and often improves zero-shot accuracy at the same model size.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/google/siglip-base-patch16-384).
+
+Pure-**Keras 3** conversion of [`google/siglip-base-patch16-384`](https://huggingface.co/google/siglip-base-patch16-384) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
 
-Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers), mirrored from the GitHub release. License: `apache-2.0`.
+This is a **zero-shot image-text** checkpoint (`SigLIPZeroShotClassify`): pass image(s) and text prompts at inference time.
+
+## ✨ Quick start
 
 ```python
-from kerasformers.models.siglip import SigLIPModel, SigLIPTokenizer
-model = SigLIPModel.from_weights("siglip_base_p16_384")
-tokenizer = SigLIPTokenizer.from_weights("siglip_base_p16_384")
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.siglip import (
+    SigLIPProcessor,
+    SigLIPZeroShotClassify,
+)
+
+processor = SigLIPProcessor.from_weights("kerasformers/siglip_base_p16_384")
+model = SigLIPZeroShotClassify.from_weights("kerasformers/siglip_base_p16_384")
+
+labels = [
+    "a photo of a cat",
+    "a photo of a dog",
+    "a photo of a car",
+    "a photo of a living room",
+]
+inputs = processor(text=labels, image_paths="your_image.jpg")
+output = model(
+    {
+        "images": inputs["images"],
+        "token_ids": inputs["input_ids"],
+    }
+)
+print(output["image_logits"].shape)
 ```
+
+Load any SigLIP variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub |
+|---|---|
+| `siglip_base_p16_224` | [`kerasformers/siglip_base_p16_224`](https://huggingface.co/kerasformers/siglip_base_p16_224) |
+| `siglip_base_p16_256` | [`kerasformers/siglip_base_p16_256`](https://huggingface.co/kerasformers/siglip_base_p16_256) |
+| `siglip_base_p16_multilingual_256` | [`kerasformers/siglip_base_p16_multilingual_256`](https://huggingface.co/kerasformers/siglip_base_p16_multilingual_256) |
+| `siglip_base_p16_384` | [`kerasformers/siglip_base_p16_384`](https://huggingface.co/kerasformers/siglip_base_p16_384) |
+| `siglip_base_p16_512` | [`kerasformers/siglip_base_p16_512`](https://huggingface.co/kerasformers/siglip_base_p16_512) |
+| `siglip_large_p16_256` | [`kerasformers/siglip_large_p16_256`](https://huggingface.co/kerasformers/siglip_large_p16_256) |
+| `siglip_large_p16_384` | [`kerasformers/siglip_large_p16_384`](https://huggingface.co/kerasformers/siglip_large_p16_384) |
+| `siglip_so400m_p14_224` | [`kerasformers/siglip_so400m_p14_224`](https://huggingface.co/kerasformers/siglip_so400m_p14_224) |
+| `siglip_so400m_p14_384` | [`kerasformers/siglip_so400m_p14_384`](https://huggingface.co/kerasformers/siglip_so400m_p14_384) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Prefer `Processor.from_weights(...)` so image size and tokenizer match the variant.
+- Map processor `input_ids` to model `token_ids`. No padding mask is required.
+- See [SigLIP docs](https://imvision12.github.io/KerasFormers/siglip/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `SigLIPZeroShotClassify.from_weights("hf:google/siglip-base-patch16-384")`.
+
+## Special Thanks
+
+A huge thank you to the Google SigLIP authors for creating and releasing these models.
+
+License: Apache 2.0.