swin-finetuned-food101
| Property | Value |
|---|---|
| Base Model | microsoft/swin-base-patch4-window7-224 |
| Task | Food Image Classification |
| Accuracy | 92.14% |
| Framework | PyTorch 1.11.0 |
What is swin-finetuned-food101?
This model is a fine-tuned version of the Swin Transformer architecture specifically optimized for food image classification. Built upon Microsoft's Swin-base model, it has been trained on the Food101 dataset to recognize and classify various food items with high accuracy.
Implementation Details
The model was trained using carefully selected hyperparameters including a learning rate of 5e-05, batch size of 64 (with gradient accumulation), and Adam optimizer. The training process spanned 3 epochs with a linear learning rate scheduler and 10% warmup ratio.
- Training batch size: 16 with 4 gradient accumulation steps
- Evaluation batch size: 16
- Final validation loss: 0.2779
- Progressive accuracy improvement from 88.61% to 92.14%
Core Capabilities
- High-accuracy food image classification (92.14%)
- Efficient processing of food-related images
- Optimized for the Food101 dataset categories
- Robust performance with relatively short training time
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful Swin Transformer architecture with specific optimizations for food image classification, achieving over 92% accuracy on the Food101 dataset. The progressive improvement in accuracy during training (from 88.61% to 92.14%) demonstrates its robust learning capabilities.
Q: What are the recommended use cases?
The model is ideal for applications requiring food image classification, such as food recognition apps, dietary tracking systems, or restaurant menu digitization. Its high accuracy makes it suitable for production environments where reliable food identification is crucial.
