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| Metric | Value |
|---|---|
| Classification Accuracy | >85% |
| Object Detection mAP | >0.75 |
| Calorie Estimation Accuracy | ±20% |
| Inference Speed | <2 seconds/image |
1from transformers import pipeline
2
3# Load the model
4classifier = pipeline("image-classification", model="BinhQuocNguyen/food-recognition-model")
5
6# Analyze a food image
7result = classifier("path/to/food_image.jpg")
8print(f"Detected foods: {result}")1import torch
2from transformers import AutoModel, AutoImageProcessor
3from PIL import Image
4
5# Load model and processor
6model = AutoModel.from_pretrained("BinhQuocNguyen/food-recognition-model")
7processor = AutoImageProcessor.from_pretrained("BinhQuocNguyen/food-recognition-model")
8
9# Process image
10image = Image.open("food_image.jpg")
11inputs = processor(images=image, return_tensors="pt")
12
13# Get predictions
14with torch.no_grad():
15 outputs = model(**inputs)
16 predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)1@misc{food-recognition-model,
2 title={Food Recognition and Calorie Estimation Model},
3 author={BinhQuocNguyen},
4 year={2024},
5 publisher={Hugging Face},
6 howpublished={\url{https://huggingface.co/BinhQuocNguyen/food-recognition-model}}
7}