update

README.md

@@ -1,3 +1,177 @@
-CheXOne official model
+---
+license: cc-by-nc-4.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: image-text-to-text
+tags:
+- Chest-Xray
+- CXR
+- Reasoning
+- VQA
+- Report
+- Grounding
+---
 
-To update
+<div align="center">
+<img src="https://github.com/YBZh/CheXOne/raw/main/asset/chexone_logo1.png" width="600" alt="CheXOne Logo">
+
+
+<p align="center">
+📝 <a href="https://huggingface.co/StanfordAIMI/CheXOne" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/StanfordAIMI/CheXOne" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/YBZh/CheXOne" target="_blank">Github</a> • 🪄 <a href="https://github.com/YBZh/CheXOne" target="_blank">Project</a>
+</p>
+</div>
+
+<!-- <div align="center">
+</div> -->
+
+## ✨  Key Features:
+* **Reasoning Capability**: Produces explicit reasoning traces alongside final answers.
+
+* **Multi-Task Support**: Supports Visual Question Answering (VQA), Report Generation, and Visual Grounding.
+
+* **Resident-Level Report Drafting**: Matches or outperforms resident-drafted reports in 50% of cases.
+
+- **Two Inference Modes**  
+  - **Reasoning Mode**: Higher performance with explicit reasoning traces.  
+  - **Instruct Mode**: Faster inference without reasoning traces.
+
+
+## 🎬 Get Started
+CheXOne is post-trained on Qwen2.5VL-3B-Instruct model, which has been in the latest Hugging face transformers and we advise you to build from source with command:
+```
+pip install git+https://github.com/huggingface/transformers accelerate
+```
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "StanfordAIMI/CheXOne", torch_dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image scenarios.
+# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+#     "StanfordAIMI/CheXOne",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processer
+processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXOne")
+
+# The default range for the number of visual tokens per image in the model is 4-16384.
+# We recommand to set max_pixels=512*512 to align with the training setting.
+# min_pixels = 256*28*28
+# max_pixels = 512*512
+# processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXOne", min_pixels=min_pixels, max_pixels=max_pixels)
+
+# Inference Mode: Reasoning
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://github.com/YBZh/CheXOne/blob/main/asset/cxr.jpg",
+            },
+            {"type": "text", "text": "Write an example findings section for the CXR. Please reason step by step, and put your final answer within \\boxed{{}}."},
+        ],
+    }
+]
+
+# Inference Mode: Instruct
+# messages = [
+#     {
+#         "role": "user",
+#         "content": [
+#             {
+#                 "type": "image",
+#                 "image": "https://github.com/YBZh/CheXOne/blob/main/asset/cxr.jpg",
+#             },
+#             {"type": "text", "text": "Write an example findings section for the CXR."},
+#         ],
+#     }
+# ]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+<details>
+<summary>Multi image inference</summary>
+
+```python
+# Messages containing multiple images and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://github.com/YBZh/CheXOne/blob/main/asset/cxr.jpg"},
+            {"type": "image", "image": "https://github.com/YBZh/CheXOne/blob/main/asset/cxr_lateral.jpg"},
+            {"type": "text", "text": "Write an example findings section for the CXR. Please reason step by step, and put your final answer within \\boxed{{}}."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+</details>
+
+## ✏️ Citation
+
+```
+@article{xx,
+  title={xx},
+  author={Cxxx},
+  journal={xx},
+  url={xx},
+  year={xx}
+}
+```