Traffic Sign Classifier (GTSRB)
Model Summary
This is a traffic sign classification model trained on GTSRB classes.
Architecture: lightweight CNN with feature extractor, 256-d bottleneck, and linear classifier.
This checkpoint is intended for research and analysis workflows, not safety-critical deployment.
Dataset
- Source: https://platform.ultralytics.com/maaaaaaaaaaaaaaaax/datasets/gtsrb-full
- Domain: German traffic sign recognition
- Image preprocessing:
- Resize to 48 x 48
- RGB input
- Training augmentation:
- Random rotation: ±10 degrees
- Color jitter: brightness 0.2, contrast 0.2
Model Architecture
- Backbone: custom CNN
- Block 1:
- Conv(3,32), BN, ELU
- Conv(32,32), BN, ELU
- MaxPool2d(2), Dropout2d(0.2)
- Block 2:
- Conv(32,64), BN, ELU
- Conv(64,64), BN, ELU
- MaxPool2d(2), Dropout2d(0.3)
- Block 3:
- Conv(64,128), BN, ELU
- Conv(128,128), BN, ELU
- MaxPool2d(2), Dropout2d(0.4)
- Bottleneck:
- Flatten
- Linear(128 x 6 x 6 -> 256), ELU, Dropout(0.5)
- Head:
Framework and Weights
- Framework: PyTorch
- Weight format: state_dict checkpoint
Intended Uses
- Research on traffic sign recognition
- Transfer learning experiments
- Educational use for compact CNN pipelines
Out-of-Scope Uses
- Real-world safety-critical decision making
Limitations
- Trained on a narrow visual domain (GTSRB)
