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language:
- en
---

# 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:
  - Linear(256 -> 42)

## 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)