This model classifies images of Indian food into 95 different categories. Developed by TEAM ANALIZ, it uses a fine-tuned Vision Transformer (ViT) model to recognize a variety of Indian dishes.

This model is trained on a large dataset of Indian food images, designed to recognize popular Indian dishes with high accuracy. The model uses the Vision Transformer (ViT) architecture, which has proven effective for image classification tasks. The model is optimized for use in mobile and web-based food recognition applications.

This model can be used to classify Indian food images into predefined categories. It is intended for food recognition applications, nutrition tracking apps, or any system that needs food image classification.

This model is not suited for classifying images from non-food categories. It is not designed for identifying food in non-Indian cuisines or for use in situations where the model might be subjected to adversarial or misleading images.

The model is trained on a dataset that may not cover all possible variations of Indian food. Additionally, some food items may have multiple regional variations that are not fully represented. Users should be cautious about the model's accuracy in regions where a different variety of the dish is served.

Users should be aware of the limitations of this model when deploying it in real-world applications, especially when dealing with regional variations or low-quality images. Further fine-tuning may be required to improve classification for certain food items.