Update README.md

README.md

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 ---
 license: mit
+language:
+- en
 ---
+
+---
+language: en
+tags:
+- computer-vision
+- image-classification
+- traffic-signs
+- pytorch
+license: mit
+datasets:
+- https://platform.ultralytics.com/maaaaaaaaaaaaaaaax/datasets/gtsrb-full
+pipeline_tag: image-classification
+---
+
+# 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)