| --- |
| license: mit |
| tags: |
| - vision |
| - food-recognition |
| - ingredients |
| - utensils |
| - portion-size |
| - computer-vision |
| - mobile |
| - ug-food-dataset |
| --- |
| |
| # UG Food Detection Model |
|
|
| This model identifies food ingredients, utensils, and estimates portion sizes from images. |
|
|
| ## Model Description |
|
|
| This Vision Transformer (ViT) model is trained on the UG Food Dataset to recognize: |
| - Food ingredients: Various food items and ingredients |
| - Kitchen utensils: Cooking tools and equipment |
| - Portion sizes: Measurement estimates |
|
|
| ## Classes |
| The model can identify 40 classes. |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import ViTImageProcessor, ViTForImageClassification |
| from PIL import Image |
| import torch |
| |
| # Load model and processor |
| processor = ViTImageProcessor.from_pretrained("ssevan/ug-food-detector") |
| model = ViTForImageClassification.from_pretrained("ssevan/ug-food-detector") |
| |
| # Process image |
| image = Image.open('food_image.jpg') |
| inputs = processor(image, return_tensors='pt') |
| |
| # Get predictions |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) |
| predicted_class_idx = torch.argmax(probabilities, dim=1).item() |
| |
| print(f'Predicted class index: {predicted_class_idx}') |
| ``` |
|
|
| ## Mobile Usage |
| This model is optimized for mobile deployment. |
|
|
