Current Status: I already have a fully runnable MMDetection/YOLOv3-based framework with training and evaluation scripts, core source code, and custom modules for the Tampered_IC13 dataset. The dataset is prepared and partially configured for training. The framework can already run standard YOLOv3 object detection training and evaluation.
Requirements: Implement a dual-branch architecture with RGB and frequency-domain branches. Develop a fusion module to effectively combine features from both branches. Train and evaluate the model on the Tampered_IC13 dataset (and potentially other datasets) to achieve a target F1 score ≥ 0.85, with balanced Precision and Recall.
Deliverables include: Trained model weights (.pth) Complete training and evaluation logs (including hyperparameters and loss curves) Detailed evaluation metrics (Precision, Recall, F1) A reproducibility guide (instructions to run and replicate the results in my environment) Optimize the model’s performance to exceed baseline results.
Required Skills: Proficiency in PyTorch and MMDetection/YOLOv3 Experience with frequency-domain image processing (e.g., FFT, DCT) Strong background in object detection model design and optimization
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