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README.md

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-# OpenMM-Medical
-
-## Introduction
-
-OpenMM-Medical is a comprehensive large-scale medical evaluation dataset that spans multiple domains, including Magnetic Resonance Imaging (MRI), CT scans, X-rays, microscopy images, endoscopy, fundus imaging, and dermoscopy. 
-OpenMM-Medical is an integration of existing datasets, comprising a total of 88,996 entries. It is designed to advance the development of multimodal medical large language models within the research community.
-
-Components | Content | Type | Number | Metrics
-| :----: | :----: |:----: | :----: |:----: |
-ACRIMA | Fundus Photography | Multiple Choice Question Answering | 159 | Acc
-Adam Challenge | Endoscopy | Multiple Choice Question Answering | 87 | Acc
-ALL Challenge | Microscopy Images | Multiple Choice Question Answering | 342 | Acc
-BioMediTech | Microscopy Images | Multiple Choice Question Answering | 511 | Acc
-Blood Cell | Microscopy Images | Multiple Choice Question Answering | 1175 | Acc
-BreakHis | Magnetic Resonance Imaging | Multiple Choice Question Answering | 735 | Acc
-Chest CT Scan | CT Imaging | Multiple Choice Question Answering | 871 | Acc
-Chest X-Ray PA | X-Ray | Multiple Choice Question Answering | 850 | Acc
-CoronaHack | X-Ray | Multiple Choice Question Answering | 684 | Acc
-Covid CT | CT Imaging | Multiple Choice Question Answering | 199 | Acc
-Covid-19 tianchi | X-Ray | Multiple Choice Question Answering | 96 | Acc
-Covid19 heywhale | X-Ray | Multiple Choice Question Answering | 690 | Acc
-COVIDx CXR-4 | X-Ray | Multiple Choice Question Answering | 485 | Acc
-CRC100k | Magnetic Resonance Imaging | Multiple Choice Question Answering | 1322 | Acc
-DeepDRiD | Fundus Photography | Multiple Choice Question Answering | 131 | Acc
-Diabetic Retinopathy | Fundus Photography | Multiple Choice Question Answering | 2051 | Acc
-DRIMDB | Fundus Photography | Multiple Choice Question Answering | 132 | Acc
-Fitzpatrick 17k | Dermoscopy | Multiple Choice Question Answering | 1552 | Acc
-HuSHeM | Microscopy Images | Multiple Choice Question Answering | 89 | Acc
-ISBI2016 | Dermoscopy | Multiple Choice Question Answering | 681 | Acc
-ISIC2018 | Dermoscopy | Multiple Choice Question Answering | 272 | Acc
-ISIC2019 | Dermoscopy | Multiple Choice Question Answering | 1952 | Acc
-ISIC2020 | Dermoscopy | Multiple Choice Question Answering | 1580 | Acc
-JSIEC | Fundus Photography | Multiple Choice Question Answering | 220 | Acc
-Knee Osteoarthritis | X-Ray | Multiple Choice Question Answering | 518 | Acc
-MAlig Lymph | Magnetic Resonance Imaging | Multiple Choice Question Answering | 149 | Acc
-MHSMA | Microscopy Images | Multiple Choice Question Answering | 1282 | Acc
-MIAS | X-Ray | Multiple Choice Question Answering | 142 | Acc
-Monkeypox Skin Image 2022 | Dermoscopy | Multiple Choice Question Answering | 163 | Acc
-Mura | X-Ray | Multiple Choice Question Answering | 1464 | Acc
-NLM- Malaria Data | Magnetic Resonance Imaging | Multiple Choice Question Answering | 75 | Acc
-OCT & X-Ray 2017 | X-Ray, Optical Coherence Tomography | Multiple Choice Question Answering | 1301 | Acc
-OLIVES | Fundus Photography | Multiple Choice Question Answering | 593 | Acc
-PAD-UFES-20 | Dermoscopy | Multiple Choice Question Answering | 479 | Acc
-PALM2019 | Fundus Photography | Multiple Choice Question Answering | 510 | Acc
-Pulmonary Chest MC | X-Ray | Multiple Choice Question Answering | 38 | Acc
-Pulmonary Chest Shenzhen | X-Ray | Multiple Choice Question Answering | 296 | Acc
-RadImageNet | CT; Magnetic Resonance Imaging; Ultrasound | Multiple Choice Question Answering | 56697 | Acc
-Retinal OCT-C8 | Optical Coherence Tomography | Multiple Choice Question Answering | 4016 | Acc
-RUS CHN | X-Ray | Multiple Choice Question Answering | 1982 | Acc
-SARS-CoV-2 CT-scan | CT | Multiple Choice Question Answering | 910 | Acc
-Yangxi | Fundus Photography | Multiple Choice Question Answering | 1515 | Acc
-
-## Usage
-
-The following steps detail how to use [**Baichuan-Omni-1.5**](https://github.com/baichuan-inc/Baichuan-Omni-1.5) with OpenMM-Medical for evaluation using [**VLMEvalKit**](https://github.com/open-compass/VLMEvalKit):
-
----
-
-### **1. Add `baichuan.py` in `VLMEvalKit/vlmeval/vlm`**
-
-Download `baichuan.py` (which defines the `Baichuan` model class) and add it in `VLMEvalKit/vlmeval/vlm`.
-
----
-
-### **2. Modify `VLMEvalKit/vlmeval/vlm/__init__.py`**
-Add the following line:
-```python
-from .baichuan import Baichuan
-```
-
----
-
-### **3. Modify `VLMEvalKit/vlmeval/config.py`**
-Import the `Baichuan` model:
-```python
-from vlmeval.vlm import Baichuan
-```
-
-Add the `Baichuan-omni` model configuration:
-```python
-'Baichuan-omni': partial(
-    Baichuan, 
-    sft=True, 
-    model_path='/your/path/to/the/model/checkpoint'
-)
-```
-
----
-
-### **4. Modify `VLMEvalKit/vlmeval/dataset/image_mcq.py`**
-Download `image_mcq.py` and add the following code to define the `OpenMMMedical` class. Ensure the `image_folder` points to your OpenMM-Medical dataset location:
-
-```python
-class OpenMMMedical(ImageMCQDataset):
-
-    @classmethod
-    def supported_datasets(cls):
-        return ['OpenMMMedical']
-
-    def load_data(self, dataset='OpenMMMedical'):
-        image_folder = "/your/path/to/OpenMM_Medical"
-        def generate_tsv(pth):
-            import csv
-            from pathlib import Path
-            tsv_file_path = os.path.join(LMUDataRoot(), f'{dataset}.tsv')
-        ...
-```
-
----
-
-### **5. Update `VLMEvalKit/vlmeval/dataset/__init__.py`**
-Import `OpenMMMedical`:
-```python
-from .image_mcq import (
-    ImageMCQDataset, MMMUDataset, CustomMCQDataset, 
-    MUIRDataset, GMAIMMBenchDataset, MMERealWorld, OpenMMMedical
-)
-
-IMAGE_DATASET = [
-    ImageCaptionDataset, ImageYORNDataset, ImageMCQDataset, ImageVQADataset,
-    MathVision, MMMUDataset, OCRBench, MathVista, LLaVABench, MMVet,
-    MTVQADataset, TableVQABench, MMLongBench, VCRDataset, MMDUDataset,
-    DUDE, SlideVQA, MUIRDataset, GMAIMMBenchDataset, MMERealWorld, OpenMMMedical
-]
-```
-
----
-
-### **6. Update `VLMEvalKit/vlmeval/dataset/image_base.py`**
-Modify the `img_root_map` function:
-```python
-def img_root_map(dataset):
-    if 'OpenMMMedical' in dataset:
-        return 'OpenMMMedical'
-    if 'OCRVQA' in dataset:
-        return 'OCRVQA'
-    if 'COCO_VAL' == dataset:
-        return 'COCO'
-    if 'MMMU' in dataset:
-        return 'MMMU'
-```
-
----
-
-### **7. Run the Evaluation**
-Execute the following command to start the evaluation:
-```bash
-python run.py --data OpenMMMedical --model Baichuan-omni --verbose
-```
-
----
-
-### **Notes:**
-- Ensure that all paths (e.g., `/your/path/to/OpenMM_Medical`) are correctly specified.
-- Confirm that the Baichuan model checkpoint is accessible at the defined `model_path`.
-- Validate the dependencies and configurations of VLMEvalKit to avoid runtime issues.
-
+# OpenMM-Medical
+
+## Introduction
+
+OpenMM-Medical is a comprehensive medical evaluation dataset, which is an integration of existing datasets. OpenMM-Medical spans multiple domains, including Magnetic Resonance Imaging (MRI), CT scans, X-rays, microscopy images, endoscopy, fundus imaging, and dermoscopy. 
+
+Components | Content | Type | Number | Metrics
+| :----: | :----: |:----: | :----: |:----: |
+ACRIMA | Fundus Photography | Multiple Choice Question Answering | 159 | Acc
+Adam Challenge | Endoscopy | Multiple Choice Question Answering | 87 | Acc
+ALL Challenge | Microscopy Images | Multiple Choice Question Answering | 342 | Acc
+BioMediTech | Microscopy Images | Multiple Choice Question Answering | 511 | Acc
+Blood Cell | Microscopy Images | Multiple Choice Question Answering | 1175 | Acc
+BreakHis | Magnetic Resonance Imaging | Multiple Choice Question Answering | 735 | Acc
+Chest CT Scan | CT Imaging | Multiple Choice Question Answering | 871 | Acc
+Chest X-Ray PA | X-Ray | Multiple Choice Question Answering | 850 | Acc
+CoronaHack | X-Ray | Multiple Choice Question Answering | 684 | Acc
+Covid CT | CT Imaging | Multiple Choice Question Answering | 199 | Acc
+Covid-19 tianchi | X-Ray | Multiple Choice Question Answering | 96 | Acc
+Covid19 heywhale | X-Ray | Multiple Choice Question Answering | 690 | Acc
+COVIDx CXR-4 | X-Ray | Multiple Choice Question Answering | 485 | Acc
+CRC100k | Magnetic Resonance Imaging | Multiple Choice Question Answering | 1322 | Acc
+DeepDRiD | Fundus Photography | Multiple Choice Question Answering | 131 | Acc
+Diabetic Retinopathy | Fundus Photography | Multiple Choice Question Answering | 2051 | Acc
+DRIMDB | Fundus Photography | Multiple Choice Question Answering | 132 | Acc
+Fitzpatrick 17k | Dermoscopy | Multiple Choice Question Answering | 1552 | Acc
+HuSHeM | Microscopy Images | Multiple Choice Question Answering | 89 | Acc
+ISBI2016 | Dermoscopy | Multiple Choice Question Answering | 681 | Acc
+ISIC2018 | Dermoscopy | Multiple Choice Question Answering | 272 | Acc
+ISIC2019 | Dermoscopy | Multiple Choice Question Answering | 1952 | Acc
+ISIC2020 | Dermoscopy | Multiple Choice Question Answering | 1580 | Acc
+JSIEC | Fundus Photography | Multiple Choice Question Answering | 220 | Acc
+Knee Osteoarthritis | X-Ray | Multiple Choice Question Answering | 518 | Acc
+MAlig Lymph | Magnetic Resonance Imaging | Multiple Choice Question Answering | 149 | Acc
+MHSMA | Microscopy Images | Multiple Choice Question Answering | 1282 | Acc
+MIAS | X-Ray | Multiple Choice Question Answering | 142 | Acc
+Monkeypox Skin Image 2022 | Dermoscopy | Multiple Choice Question Answering | 163 | Acc
+Mura | X-Ray | Multiple Choice Question Answering | 1464 | Acc
+NLM- Malaria Data | Magnetic Resonance Imaging | Multiple Choice Question Answering | 75 | Acc
+OCT & X-Ray 2017 | X-Ray, Optical Coherence Tomography | Multiple Choice Question Answering | 1301 | Acc
+OLIVES | Fundus Photography | Multiple Choice Question Answering | 593 | Acc
+PAD-UFES-20 | Dermoscopy | Multiple Choice Question Answering | 479 | Acc
+PALM2019 | Fundus Photography | Multiple Choice Question Answering | 510 | Acc
+Pulmonary Chest MC | X-Ray | Multiple Choice Question Answering | 38 | Acc
+Pulmonary Chest Shenzhen | X-Ray | Multiple Choice Question Answering | 296 | Acc
+RadImageNet | CT; Magnetic Resonance Imaging; Ultrasound | Multiple Choice Question Answering | 56697 | Acc
+Retinal OCT-C8 | Optical Coherence Tomography | Multiple Choice Question Answering | 4016 | Acc
+RUS CHN | X-Ray | Multiple Choice Question Answering | 1982 | Acc
+SARS-CoV-2 CT-scan | CT | Multiple Choice Question Answering | 910 | Acc
+Yangxi | Fundus Photography | Multiple Choice Question Answering | 1515 | Acc
+
+## Usage
+
+The following steps detail how to use [**Baichuan-Omni-1.5**](https://github.com/baichuan-inc/Baichuan-Omni-1.5) with OpenMM-Medical for evaluation using [**VLMEvalKit**](https://github.com/open-compass/VLMEvalKit):
+
+---
+
+### **1. Add `baichuan.py` in `VLMEvalKit/vlmeval/vlm`**
+
+Download `baichuan.py` (which defines the `Baichuan` model class) and add it in `VLMEvalKit/vlmeval/vlm`.
+
+---
+
+### **2. Modify `VLMEvalKit/vlmeval/vlm/__init__.py`**
+Add the following line:
+```python
+from .baichuan import Baichuan
+```
+
+---
+
+### **3. Modify `VLMEvalKit/vlmeval/config.py`**
+Import the `Baichuan` model:
+```python
+from vlmeval.vlm import Baichuan
+```
+
+Add the `Baichuan-omni` model configuration:
+```python
+'Baichuan-omni': partial(
+    Baichuan, 
+    sft=True, 
+    model_path='/your/path/to/the/model/checkpoint'
+)
+```
+
+---
+
+### **4. Modify `VLMEvalKit/vlmeval/dataset/image_mcq.py`**
+Download `image_mcq.py` and add the following code to define the `OpenMMMedical` class. Ensure the `image_folder` points to your OpenMM-Medical dataset location:
+
+```python
+class OpenMMMedical(ImageMCQDataset):
+
+    @classmethod
+    def supported_datasets(cls):
+        return ['OpenMMMedical']
+
+    def load_data(self, dataset='OpenMMMedical'):
+        image_folder = "/your/path/to/OpenMM_Medical"
+        def generate_tsv(pth):
+            import csv
+            from pathlib import Path
+            tsv_file_path = os.path.join(LMUDataRoot(), f'{dataset}.tsv')
+        ...
+```
+
+---
+
+### **5. Update `VLMEvalKit/vlmeval/dataset/__init__.py`**
+Import `OpenMMMedical`:
+```python
+from .image_mcq import (
+    ImageMCQDataset, MMMUDataset, CustomMCQDataset, 
+    MUIRDataset, GMAIMMBenchDataset, MMERealWorld, OpenMMMedical
+)
+
+IMAGE_DATASET = [
+    ImageCaptionDataset, ImageYORNDataset, ImageMCQDataset, ImageVQADataset,
+    MathVision, MMMUDataset, OCRBench, MathVista, LLaVABench, MMVet,
+    MTVQADataset, TableVQABench, MMLongBench, VCRDataset, MMDUDataset,
+    DUDE, SlideVQA, MUIRDataset, GMAIMMBenchDataset, MMERealWorld, OpenMMMedical
+]
+```
+
+---
+
+### **6. Update `VLMEvalKit/vlmeval/dataset/image_base.py`**
+Modify the `img_root_map` function:
+```python
+def img_root_map(dataset):
+    if 'OpenMMMedical' in dataset:
+        return 'OpenMMMedical'
+    if 'OCRVQA' in dataset:
+        return 'OCRVQA'
+    if 'COCO_VAL' == dataset:
+        return 'COCO'
+    if 'MMMU' in dataset:
+        return 'MMMU'
+```
+
+---
+
+### **7. Run the Evaluation**
+Execute the following command to start the evaluation:
+```bash
+python run.py --data OpenMMMedical --model Baichuan-omni --verbose
+```
+
+---
+
+### **Notes:**
+- Ensure that all paths (e.g., `/your/path/to/OpenMM_Medical`) are correctly specified.
+- Confirm that the Baichuan model checkpoint is accessible at the defined `model_path`.
+- Validate the dependencies and configurations of VLMEvalKit to avoid runtime issues.
+
 With this setup, you should be able to evaluate OpenMM-Medical using Baichuan-Omni successfully.