Food Classification Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | google/vit-base-patch16-224-in21k |
| Accuracy | 89.13% |
| Training Dataset | Food-101 |
What is food?
This is a Vision Transformer (ViT) based model specifically fine-tuned for food image classification. Built upon Google's ViT architecture, it demonstrates impressive accuracy in identifying various food items from the Food-101 dataset. The model achieves a remarkable 89.13% accuracy, making it particularly useful for applications in food recognition systems.
Implementation Details
The model was trained using PyTorch with mixed precision training (Native AMP) over 5 epochs. It utilizes the Adam optimizer with carefully tuned hyperparameters (betas=0.9,0.999, epsilon=1e-08) and implements a linear learning rate scheduler with a rate of 0.0002.
- Training batch size: 128 samples
- Evaluation batch size: 128 samples
- Training duration: 5 epochs
- Final validation loss: 0.4501
Core Capabilities
- High-accuracy food image classification
- Efficient inference with TensorBoard support
- Robust performance across diverse food categories
- Suitable for both research and production environments
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines the power of Vision Transformers with specialized food recognition capabilities, achieving an impressive 89.13% accuracy through careful fine-tuning on the Food-101 dataset. Its progressive improvement over training epochs, from 85.62% to 89.13%, demonstrates robust learning capabilities.
Q: What are the recommended use cases?
The model is ideal for applications in food recognition systems, dietary tracking apps, restaurant menu digitization, and automated food logging systems. It can be effectively deployed in both mobile and cloud-based applications requiring accurate food classification.
