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| **Component** | **Description** |
|-------------------------------|-----------------------------------------------------------------------------------------------|
| **Backbone** | ResNet-50 with FPN (Feature Pyramid Network) |
| **Pretrained Weights** | Trained on ImageNet for feature extraction. |
| **RPN (Region Proposal Network)** | Generates region proposals based on extracted features from the backbone. |
| **ROI Align** | Aligns region proposals to a fixed size for consistent feature extraction. |
| **Box Head** | Fully connected layers for refining bounding boxes and classifying objects. |
| **Box Predictor** | Replaced with a custom predictor: `FastRCNNPredictor` for handling custom classes. |
| **Number of Classes** | Configurable (including background). |
| **Loss Function** | Combines classification and regression losses for multi-task optimization. |
| **Optimizer** | Stochastic Gradient Descent (SGD) with momentum for optimization. |
| **Learning Rate Scheduler** | StepLR to decay learning rate every few epochs for better convergence. |
| **Batch Normalization** | Applied within the backbone for stable training. |
| **Data Format** | Input: Tensor of shape `(Batch Size, Channels, Height, Width)` in PyTorch's NCHW format. |
| **Output** | - Class probabilities for each region proposal. |
| | - Refined bounding box coordinates for each detected object. |
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