fix readme.md

README.md

@@ -24,7 +24,8 @@ tags:
 
 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
+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).
+
 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**.
@@ -38,10 +39,10 @@ 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']}
+from kerasformers.models.depth_anything_v1 import DepthAnythingV1DepthEstimation, DepthAnythingV1ImageProcessor
 
-model = {meta["load_cls"]}.from_weights("kerasformers/{variant}")
-processor = {meta['proc_cls']}()
+model = DepthAnythingV1DepthEstimation.from_weights("kerasformers/depth_anything_small")
+processor = DepthAnythingV1ImageProcessor()
 
 image = Image.open("your_image.jpg").convert("RGB")
 output = model(processor(image)["pixel_values"], training=False)