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README.md

@@ -21,6 +21,64 @@ pipeline_tag: zero-shot-image-classification
 
 This repository hosts the pretrained vision-language backbone for CLEAR (*Concept-Level Embeddings for Auditable Radiology*). The checkpoint contains a contrastive image–text encoder that maps chest X-rays and radiological text into a shared 768-dimensional embedding space. Given a chest X-ray, the image encoder produces a feature vector whose cosine similarity with each of 368,294 text-encoded radiological observations yields a concept score vector. This concept score vector is then projected through LLM-derived semantic embeddings to produce the final CLEAR image embedding used for zero-shot classification, supervised linear probing, and concept bottleneck models. The concept bank, LLM embeddings, and downstream inference code are available in the [GitHub repository](https://github.com/peterhan91/CLEAR).
 
+## Files in This Repository
+
+| File | Description |
+|------|-------------|
+| `best_model.pt` | CLEAR vision-language backbone checkpoint (DINOv2 ViT-B/14 + text encoder) |
+| `mimic_concepts.csv` | Full concept vocabulary (368,294 radiological observations extracted from reports) |
+| `concept_embeddings_368294.pt` | Precomputed SFR-Embedding-Mistral embeddings for all 368,294 concepts (4096-dim) |
+
+## Quick Start
+
+```python
+import torch
+from PIL import Image
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode
+from huggingface_hub import hf_hub_download
+
+# Download the checkpoint
+ckpt_path = hf_hub_download(repo_id="peterhan91/CLEAR", filename="best_model.pt")
+
+# Clone the CLEAR repo for model code: git clone https://github.com/peterhan91/CLEAR
+from clear.model import CLIP
+from clear import tokenize
+from examples.train import load_clip
+
+# Load the CLEAR checkpoint (DINOv2 ViT-B/14 + text encoder)
+model = load_clip(
+    model_path=ckpt_path,
+    use_dinov2=True,
+    dinov2_model_name="dinov2_vitb14",
+)
+model.eval()
+
+# Preprocess a chest X-ray
+preprocess = Compose([
+    Resize(448, interpolation=InterpolationMode.BICUBIC),
+    CenterCrop(448),
+    ToTensor(),
+    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+device = next(model.parameters()).device
+image = preprocess(Image.open("path/to/cxr.jpg").convert("RGB")).unsqueeze(0).to(device)
+
+# Encode image and text
+with torch.no_grad():
+    image_features = model.encode_image(image)
+    text_tokens = tokenize(["pleural effusion", "no pleural effusion"]).to(device)
+    text_features = model.encode_text(text_tokens)
+
+    # Cosine similarity → softmax probability
+    image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+    text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+    logits = (image_features @ text_features.T).softmax(dim=-1)
+
+print(logits)  # [[prob_positive, prob_negative]]
+```
+
+The full CLEAR pipeline projects these concept similarity scores through LLM-derived semantic embeddings (SFR-Embedding-Mistral) for auditable zero-shot classification. See the [GitHub repository](https://github.com/peterhan91/CLEAR) for benchmarking, concept bottleneck models, and model auditing scripts.
+
 ## Checkpoint Details
 
 | Attribute | Value |