Vision Policy Decomposition

# Vision Policy Decomposition ## Project Title Decomposing Internal Policies in Transformer-based Vision Models to Enhance Image Classification Performance ## Research Hypothesis Decomposing internal policies in transformer-based vision models enhances image classification performance by optimizing feature extraction layers. ## Implementation Approach We propose to implement policy decomposition techniques on a vision transformer model by identifying and optimizing internal policies within its layers. We will conduct experiments on the ImageNet dataset to evaluate improvements in accuracy and efficiency, comparing results with baseline models that do not utilize policy decomposition. ## Setup Instructions 1. Clone the repository: ```bash git clone cd vision_policy_decomposition ``` 2. Install the required packages: ```bash pip install -r requirements.txt ``` 3. Prepare the dataset (ImageNet): Follow the instructions to download and prepare the ImageNet dataset. ## Usage Examples - To train the model: ```bash python src/train.py ``` - To evaluate the model: ```bash python src/evaluate.py ``` ## Expected Results We expect to see an improvement in image classification accuracy and efficiency compared to baseline transformer models. ## References - Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies (http://arxiv.org/abs/2512.19673v1)