format

configs/base_config.py

@@ -0,0 +1,28 @@
+"""
+Base configuration dictionary for the project.
+Contains default values for common hyperparameters and settings.
+"""
+
+base_config = {
+    # --- Data --- #
+    "data_root": "data/PennFudanPed",  # Default dataset path
+    "output_dir": "outputs",  # Base directory for logs, checkpoints, etc.
+    # --- Hardware --- #
+    "device": "cuda",  # 'cuda' or 'cpu'
+    # --- Model --- #
+    "num_classes": 2,  # Number of classes (including background)
+    # --- Training --- #
+    "batch_size": 2,  # Training batch size
+    "num_epochs": 10,  # Total number of training epochs
+    "seed": 42,  # Random seed for reproducibility
+    # --- Optimizer --- #
+    "lr": 0.005,  # Initial learning rate
+    "momentum": 0.9,
+    "weight_decay": 0.0005,
+    # --- LR Scheduler --- #
+    "lr_step_size": 3,  # Step size for StepLR scheduler
+    "lr_gamma": 0.1,  # Multiplicative factor for StepLR scheduler
+    # --- Logging & Checkpointing --- #
+    "log_freq": 10,  # Log training progress every N batches
+    "checkpoint_freq": 1,  # Save checkpoint every N epochs
+}

configs/pennfudan_maskrcnn_config.py

@@ -0,0 +1,23 @@
+"""
+Configuration for training Mask R-CNN on the Penn-Fudan dataset.
+"""
+
+from .base_config import base_config
+
+config = base_config.copy()
+
+# Override necessary settings from base_config
+config.update(
+    {
+        "config_name": "pennfudan_maskrcnn_v1",  # Unique name for this experiment run
+        "data_root": "data/PennFudanPed",  # Explicitly set dataset root
+        "num_classes": 2,  # Penn-Fudan has 1 class (pedestrian) + background
+        # Adjust other parameters as needed for this specific experiment, e.g.:
+        # 'batch_size': 4,
+        # 'num_epochs': 15,
+        # 'lr': 0.001,
+    }
+)
+
+# Ensure derived paths or settings are consistent if needed
+# (Not strictly necessary with this simple structure)

todo.md

@@ -16,10 +16,7 @@ This list outlines the steps required to complete the Torchvision Finetuning pro
 - [x] Install pre-commit hooks
 - [x] Verify PyTorch GPU integration (`scripts/check_gpu.py`)
 - [x] Create data download script (`scripts/download_data.sh`)
-- [ ] Implement PennFudanDataset class (`utils/data_utils.py`)
-- [ ] Implement model finetuning logic (`models/detection.py`)
-- [ ] Implement training script (`train.py`)
-- [ ] Implement evaluation script (`test.py`)
+- [x] Implement configuration system (`configs/base_config.py`, `configs/pennfudan_maskrcnn_config.py`)
 
 ## Phase 2: Data Handling & Model
 
@@ -125,4 +122,4 @@ This list outlines the steps required to complete the Torchvision Finetuning pro
     - [ ] Dependencies list
     - [ ] (Optional) Results section
 - [ ] Perform final code quality checks (`ruff format .`, `ruff check . --fix`).
-- [ ] Ensure all pre-commit hooks pass. 
+- [ ] Ensure all pre-commit hooks pass.