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cifar10-mobilenetv2-transfer-learning
aijadugar/cifar10-mobilenetv2-transfer-learning
cifar10-mobilenetv2-transfer-learning is a machine learning model from aijadugar. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This project demonstrates Transfer Learning using a pretrained MobileNetV2 model on the CIFAR-10 dataset with PyTorch.
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Public
Updated Jul 21, 2026
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At a glance
- Access
- Public
- Created
- Jul 21, 2026
- Updated
- Jul 21, 2026
- SHA
- d883a14d
Model card
From the Hugging Face model README
Transfer Learning with MobileNetV2 on CIFAR-10
This project demonstrates Transfer Learning using a pretrained MobileNetV2 model on the CIFAR-10 dataset with PyTorch.
Two transfer learning approaches are implemented:
- Feature Extraction
- Fine-Tuning
Dataset
- Dataset: CIFAR-10
- Training Images: 45,000
- Validation Images: 5,000
- Test Images: 10,000
- Classes: 10
Training Pipeline
Feature Extraction
- Load pretrained MobileNetV2
- Replace the final classifier
- Freeze the backbone
- Train only the classifier
Fine-Tuning
- Load the best feature extraction model
- Unfreeze features[18] and the classifier
- Fine-tune using different learning rates
Results
Feature Extraction
| Loss | Accuracy |
|---|---|
 |  |
Fine-Tuning
| Loss | Accuracy |
|---|---|
 |  |
Technologies
- Python
- PyTorch
- TorchVision
- NumPy
- Matplotlib
- tqdm
Author
Ankit Bari
- Created
- Jul 21, 2026
- Updated
- Jul 21, 2026