fix readme.md

README.md

@@ -1,21 +1,73 @@
 ---
 pipeline_tag: depth-estimation
 license: apache-2.0
+base_model: LiheYoung/depth-anything-small-hf
 library_name: kerasformers
 tags:
 - keras
 - kerasformers
-- depth_anything_v1
-- tf
-- jax
+- depth-anything
+- depth-estimation
+- arxiv:2401.10891
 - pytorch
+- jax
+- tf
 ---
 
-# depth_anything_small (Keras 3)
+## ***See [our collection](https://huggingface.co/collections/kerasformers/depth-anything-v1-and-v2-6a6a965e3e9e4847e424dde2) for all versions of Depth Anything V1.***
+
+# Run Depth Anything V1 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-Depth--Anything--V1-blue)](https://imvision12.github.io/KerasFormers/depth_anything_v1/) [![Collection](https://img.shields.io/badge/HF-Depth--Anything--V1%20collection-yellow)](https://huggingface.co/collections/kerasformers/depth-anything-v1-and-v2-6a6a965e3e9e4847e424dde2)
+
+# kerasformers/depth_anything_small
+
+Paper: [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (arXiv:2401.10891)](https://arxiv.org/abs/2401.10891) · [HF Papers](https://huggingface.co/papers/2401.10891)
+
+Depth Anything estimates depth from a single image. A DINOv2 ViT backbone feeds a DPT-style neck and head. V1 outputs relative inverse depth (larger means closer; units are arbitrary within one image).\n
+For more details on the model, please go to the upstream [model card](https://huggingface.co/LiheYoung/depth-anything-small-hf).
+
+Pure-**Keras 3** conversion of [`LiheYoung/depth-anything-small-hf`](https://huggingface.co/LiheYoung/depth-anything-small-hf) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **monocular depth** checkpoint (`DepthAnythingV1DepthEstimation`) with relative inverse depth.
 
-Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers). License: `apache-2.0`.
+## ✨ Quick start
 
 ```python
-from kerasformers.models.depth_anything_v1 import DepthAnythingV1DepthEstimation
-model = DepthAnythingV1DepthEstimation.from_weights("depth_anything_small")
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+from {meta['import_path']} import {meta['load_cls']}, {meta['proc_cls']}
+
+model = {meta["load_cls"]}.from_weights("kerasformers/{variant}")
+processor = {meta['proc_cls']}()
+
+image = Image.open("your_image.jpg").convert("RGB")
+output = model(processor(image)["pixel_values"], training=False)
+depth = processor.post_process_depth_estimation(
+    output, original_size=(image.height, image.width)
+)
+print(depth.shape)
 ```
+
+Load any Depth Anything V1 variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Backbone |
+|---|---|---|
+| `depth_anything_small` | [`kerasformers/depth_anything_small`](https://huggingface.co/kerasformers/depth_anything_small) | ViT-S/14 |
+| `depth_anything_base` | [`kerasformers/depth_anything_base`](https://huggingface.co/kerasformers/depth_anything_base) | ViT-B/14 |
+| `depth_anything_large` | [`kerasformers/depth_anything_large`](https://huggingface.co/kerasformers/depth_anything_large) | ViT-L/14 |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- For metric (metre) depth, use Depth Anything V2 metric heads.
+- See [Depth Anything V1 docs]({DOCS_URL}) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream weights: `DepthAnythingV1DepthEstimation.from_weights("hf:LiheYoung/depth-anything-small-hf")`.
+
+## Special Thanks
+
+A huge thank you to the Depth Anything authors for creating and releasing these models.
+
+License: Apache 2.0.