Create README.md

README.md

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+# Combined Multimodal Model
+
+This model performs medical image classification and report generation using a custom architecture that combines a video model and a text generation model.
+
+## Model Details
+
+- **Architecture**: Custom model combining a 3D ResNet (`r3d_18`) and BioBART.
+- **Tasks**:
+  - **Classification**: Classifies medical images into one of four classes: acute, normal, chronic, or lacunar.
+  - **Report Generation**: Generates medical reports based on the input images.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer
+from model import CombinedModel, ImageToTextProjector
+from torchvision import models
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("YOUR_HF_USERNAME/combined-multimodal-model")
+
+# Initialize models
+video_model = models.video.r3d_18(pretrained=True)
+video_model.fc = torch.nn.Linear(video_model.fc.in_features, 512)
+
+report_generator = AutoModelForSeq2SeqLM.from_pretrained("GanjinZero/biobart-v2-base")
+
+projector = ImageToTextProjector(512, report_generator.config.d_model)
+
+num_classes = 4
+combined_model = CombinedModel(video_model, report_generator, num_classes, projector)
+
+# Load state dict
+state_dict = torch.hub.load_state_dict_from_url(
+    "https://huggingface.co/YOUR_HF_USERNAME/combined-multimodal-model/resolve/main/pytorch_model.bin",
+    map_location=torch.device('cpu')
+)
+combined_model.load_state_dict(state_dict)
+combined_model.eval()
+
+# Now you can use combined_model for inference