Improve model card: Update GitHub link, add abstract, library_name, tags, and sample usage

README.md

@@ -1,9 +1,13 @@
 ---
+license: apache-2.0
+pipeline_tag: image-segmentation
+library_name: Pytorch
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
-license: apache-2.0
-pipeline_tag: image-segmentation
+- DINOv2
+- CLIP
+- open-vocabulary segmentation
 ---
 
 <div align="center">
@@ -22,7 +26,7 @@ Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Ope
 <a href="https://www.linkedin.com/in/rita-cucchiara-a4653a13/">Rita Cucchiara</a>
 </h3>
 
-[Project Page](https://lorebianchi98.github.io/Talk2DINO/) | [Paper](http://arxiv.org/abs/2411.19331) | [Code](https://github.com/your-repo/Talk2DINO)
+[Project Page](https://lorebianchi98.github.io/Talk2DINO/) | [Paper](http://arxiv.org/abs/2411.19331) | [Code](https://github.com/lorebianchi98/Talk2DINO)
 
 </div>
 
@@ -32,6 +36,63 @@ Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Ope
 </figure>
 </div>
 
+## About
+Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.
+
+## Sample Usage
+
+### Mapping CLIP Text Embeddings to DINOv2 space with Talk2DINO
+We can use Talk2DINO to map CLIP text embeddings into the DINOv2 patch embedding space.
+```python
+import clip
+from src.model import ProjectionLayer
+import torch
+import os
+
+# Device setup
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Configuration and weights
+proj_name = 'vitb_mlp_infonce'
+config_path = os.path.join("configs", f"{proj_name}.yaml")
+weights_path = os.path.join("weights", f"{proj_name}.pth")
+
+# Load Talk2DINO projection layer
+talk2dino = ProjectionLayer.from_config(config_path)
+talk2dino.load_state_dict(torch.load(weights_path, map_location=device))
+talk2dino.to(device)
+
+# Load CLIP model
+clip_model, clip_preprocess = clip.load("ViT-B/16", device=device, jit=False)
+tokenizer = clip.tokenize
+
+# Example: Tokenize and project text features
+texts = ["a cat"]
+text_tokens = tokenizer(texts).to(device)
+text_features = clip_model.encode_text(text_tokens)
+projected_text_features = talk2dino.project_clip_txt(text_features)
+```
+
+### Demo
+In `demo.py` we provide a simple example on how to use Talk2DINO for inference on a given image with custom textual categories. Run
+
+```bash
+python demo.py --input custom_input_image --output custom_output_seg [--with_background] --textual_categories category_1,category_2,..
+```
+
+Example:
+```bash
+python demo.py --input assets/pikachu.png --output pikachu_seg.png --textual_categories pikachu,traffic_sign,forest,route
+```
+
+Result:
+<div align="center">
+<table><tr><td><figure>
+  <img alt="" src="./assets/pikachu.png" width=300>
+</figure></td><td><figure>
+  <img alt="" src="./pikachu_seg.png" width=300>
+</figure></td></tr></table>
+</div>
 
 ## Installation
 ```bash
@@ -87,5 +148,4 @@ If you found this code useful, please cite the following paper:
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2411.19331}, 
 }
-```
-
+```