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README.md

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-license: mit
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+---
+license: mit
+---
+
+
+# Micron-Flow: Real-Time Optical Flow Model
+
+## Model Overview
+**Micron-Flow** is a lightweight optical flow model optimized for real-time inference at **80+ FPS** on high-end GPUs. By leveraging knowledge distillation from RAFT-Large, this model achieves **high accuracy** while maintaining an extremely small size of **522K parameters**.
+
+## Model Details
+- **Architecture**: Modified U-Net with MobileNetV2-based Siamese encoder, residual blocks, and a flow refinement module.
+- **Parameters**: 522K
+- **Input Resolution**: (152, 240)
+- **Training Dataset**: 200K video frame pairs generated from the **Moments of Time** dataset using RAFT-Large.
+- **Distillation Approach**: 
+  - Mean squared error (MSE) loss in tanh-space
+  - Edge-aware smoothness loss
+- **Optimization**: Trained with **CosineAnnealing** scheduler and progressive encoder unfreezing.
+
+## Performance
+| Device       | Inference Time | FPS  |
+|-------------|---------------|------|
+| **RTX 4090** | 0.012 sec     | 83   |
+| **RTX 3070 Ti** | TBD       | TBD  |
+| **CPU-Only** | 0.07 sec      | 14   |
+
+## Key Features
+- **Real-time processing**: 80+ FPS on RTX 4090
+- **Small model size**: Only 2.1MB on disk
+- **Efficient architecture**: 
+  - Depthwise convolutions for reduced parameters
+  - Inverted residual blocks for better efficiency
+  - Flow refiner for enhanced motion consistency
+- **Optimized training pipeline**: GPU caching and JPEG decoding acceleration
+
+## Limitations
+- Trained on synthetic optical flow from RAFT-Large, which may introduce biases.
+- Resolution fixed to (152, 240) – requires up/downscaling for different input sizes.
+
+## Model Usage
+```python
+from torchvision.transforms.functional import to_tensor
+from model import MicronFlow  # Assuming model implementation is available
+
+model = MicronFlow().eval()
+frame1 = to_tensor(image1).unsqueeze(0)  # Convert images to tensors
+frame2 = to_tensor(image2).unsqueeze(0)
+flow = model(frame1, frame2)  # Optical flow prediction
+```
+
+## License
+MIT License.
+
+
+## Links
+- **Code**: [GitHub](https://github.com/your-repo)