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EN
Food 101 93M
A food image classification model fine-tuned from google/siglip2-base-patch16-224, capable of recognizing 101 popular dishes
Downloads 158
Release Time : 4/4/2025
Model Overview
This model uses the SiglipForImageClassification architecture, trained on the Food-101 dataset, specifically designed for food image classification tasks.
Model Features
High-precision food recognition
Achieves 89.73% accuracy on 101-category food classification tasks
Based on SigLIP2 architecture
Utilizes Google's advanced SigLIP2 visual architecture with powerful feature extraction capabilities
Extensive food category coverage
Supports classification and recognition of 101 internationally popular dishes
Model Capabilities
Food image classification
Multi-category image recognition
Dish recognition
Use Cases
Food service industry
Smart menu system
Automatically identifies customer-taken dish photos and matches them with menu items
Improves ordering efficiency and accuracy
Nutritional analysis
Estimates meal nutritional content by recognizing food images
Assists in healthy diet management
Social media
Automatic food content tagging
Automatically adds tags to food images on social media
Enhances content categorization and search experience
license: apache-2.0 datasets:
- ethz/food101 language:
- en base_model:
- google/siglip2-base-patch16-224 pipeline_tag: image-classification library_name: transformers tags:
- Food
- '101'
- siglip2
- vit
- biology
Food-101-93M
Food-101-93M is a fine-tuned image classification model built on top of google/siglip2-base-patch16-224 using the SiglipForImageClassification architecture. It is trained to classify food images into one of 101 popular dishes, derived from the Food-101 dataset.
Classification Report:
precision recall f1-score support
apple_pie 0.8399 0.8253 0.8325 750
baby_back_ribs 0.9445 0.8853 0.9140 750
baklava 0.9736 0.9347 0.9537 750
beef_carpaccio 0.9079 0.9200 0.9139 750
beef_tartare 0.8486 0.8293 0.8388 750
beet_salad 0.8649 0.8707 0.8678 750
beignets 0.8961 0.9080 0.9020 750
bibimbap 0.9361 0.9373 0.9367 750
bread_pudding 0.7979 0.8000 0.7989 750
breakfast_burrito 0.8784 0.9053 0.8917 750
bruschetta 0.8672 0.8533 0.8602 750
caesar_salad 0.9444 0.9293 0.9368 750
cannoli 0.9263 0.9547 0.9402 750
caprese_salad 0.9110 0.9280 0.9194 750
carrot_cake 0.9068 0.8040 0.8523 750
ceviche 0.8375 0.8453 0.8414 750
cheesecake 0.8225 0.8093 0.8159 750
cheese_plate 0.9627 0.9627 0.9627 750
chicken_curry 0.8970 0.8827 0.8898 750
chicken_quesadilla 0.9254 0.9093 0.9173 750
chicken_wings 0.9512 0.9360 0.9435 750
chocolate_cake 0.7958 0.8107 0.8032 750
chocolate_mousse 0.6947 0.7827 0.7361 750
churros 0.9440 0.9440 0.9440 750
clam_chowder 0.8883 0.9120 0.9000 750
club_sandwich 0.9396 0.9133 0.9263 750
crab_cakes 0.9185 0.8720 0.8947 750
creme_brulee 0.9141 0.9227 0.9184 750
croque_madame 0.9106 0.8960 0.9032 750
cup_cakes 0.8986 0.9333 0.9156 750
deviled_eggs 0.9787 0.9813 0.9800 750
donuts 0.8893 0.8787 0.8840 750
dumplings 0.9212 0.8880 0.9043 750
edamame 0.9960 0.9920 0.9940 750
eggs_benedict 0.9207 0.9440 0.9322 750
escargots 0.8709 0.8907 0.8807 750
falafel 0.8945 0.8933 0.8939 750
filet_mignon 0.7598 0.7467 0.7532 750
fish_and_chips 0.9454 0.9467 0.9460 750
foie_gras 0.6659 0.8027 0.7279 750
french_fries 0.9447 0.9333 0.9390 750
french_onion_soup 0.8667 0.9187 0.8919 750
french_toast 0.8890 0.8760 0.8825 750
fried_calamari 0.9448 0.9133 0.9288 750
fried_rice 0.9325 0.9213 0.9269 750
frozen_yogurt 0.8716 0.9507 0.9094 750
garlic_bread 0.9103 0.8800 0.8949 750
gnocchi 0.8554 0.8280 0.8415 750
greek_salad 0.9203 0.9240 0.9222 750
grilled_cheese_sandwich 0.8523 0.8773 0.8647 750
grilled_salmon 0.8463 0.8960 0.8705 750
guacamole 0.9537 0.9347 0.9441 750
gyoza 0.8970 0.9173 0.9071 750
hamburger 0.8899 0.8947 0.8923 750
hot_and_sour_soup 0.9439 0.9413 0.9426 750
hot_dog 0.8859 0.9320 0.9084 750
huevos_rancheros 0.8465 0.8827 0.8642 750
hummus 0.9394 0.9093 0.9241 750
ice_cream 0.8633 0.8507 0.8570 750
lasagna 0.8780 0.8733 0.8757 750
lobster_bisque 0.8952 0.9107 0.9028 750
lobster_roll_sandwich 0.9664 0.9573 0.9618 750
macaroni_and_cheese 0.9273 0.9013 0.9141 750
macarons 0.9892 0.9747 0.9819 750
miso_soup 0.9565 0.9667 0.9615 750
mussels 0.9602 0.9640 0.9621 750
nachos 0.9337 0.9387 0.9362 750
omelette 0.8889 0.8960 0.8924 750
onion_rings 0.9493 0.9493 0.9493 750
oysters 0.9808 0.9533 0.9669 750
pad_thai 0.9188 0.9507 0.9345 750
paella 0.9352 0.9240 0.9296 750
pancakes 0.9277 0.9067 0.9171 750
panna_cotta 0.8056 0.8507 0.8275 750
peking_duck 0.8529 0.9120 0.8814 750
pho 0.9746 0.9227 0.9479 750
pizza 0.9512 0.9360 0.9435 750
pork_chop 0.8085 0.7373 0.7713 750
poutine 0.9424 0.9387 0.9405 750
prime_rib 0.9106 0.8147 0.8600 750
pulled_pork_sandwich 0.8887 0.9053 0.8970 750
ramen 0.8986 0.9213 0.9098 750
ravioli 0.8532 0.8293 0.8411 750
red_velvet_cake 0.9330 0.8907 0.9113 750
risotto 0.8809 0.8680 0.8744 750
samosa 0.9153 0.9227 0.9190 750
sashimi 0.9248 0.9187 0.9217 750
scallops 0.8564 0.8507 0.8535 750
seaweed_salad 0.9597 0.9533 0.9565 750
shrimp_and_grits 0.8995 0.8947 0.8971 750
spaghetti_bolognese 0.9667 0.9667 0.9667 750
spaghetti_carbonara 0.9601 0.9627 0.9614 750
spring_rolls 0.9045 0.9467 0.9251 750
steak 0.6311 0.7027 0.6650 750
strawberry_shortcake 0.8832 0.8467 0.8645 750
sushi 0.9204 0.8947 0.9074 750
tacos 0.9225 0.8893 0.9056 750
takoyaki 0.9419 0.9507 0.9463 750
tiramisu 0.9074 0.8627 0.8845 750
tuna_tartare 0.7691 0.7773 0.7732 750
waffles 0.9629 0.9347 0.9486 750
accuracy 0.8973 75750
macro avg 0.8987 0.8973 0.8977 75750
weighted avg 0.8987 0.8973 0.8977 75750
The model categorizes images into 101 food classes such as sushi, hamburger, waffles, pad_thai, and more.
Run with Transformers 🤗
!pip install -q transformers torch pillow gradio
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Food-101-93M"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Food-101 labels
labels = {
"0": "apple_pie", "1": "baby_back_ribs", "2": "baklava", "3": "beef_carpaccio", "4": "beef_tartare",
"5": "beet_salad", "6": "beignets", "7": "bibimbap", "8": "bread_pudding", "9": "breakfast_burrito",
"10": "bruschetta", "11": "caesar_salad", "12": "cannoli", "13": "caprese_salad", "14": "carrot_cake",
"15": "ceviche", "16": "cheesecake", "17": "cheese_plate", "18": "chicken_curry", "19": "chicken_quesadilla",
"20": "chicken_wings", "21": "chocolate_cake", "22": "chocolate_mousse", "23": "churros", "24": "clam_chowder",
"25": "club_sandwich", "26": "crab_cakes", "27": "creme_brulee", "28": "croque_madame", "29": "cup_cakes",
"30": "deviled_eggs", "31": "donuts", "32": "dumplings", "33": "edamame", "34": "eggs_benedict",
"35": "escargots", "36": "falafel", "37": "filet_mignon", "38": "fish_and_chips", "39": "foie_gras",
"40": "french_fries", "41": "french_onion_soup", "42": "french_toast", "43": "fried_calamari", "44": "fried_rice",
"45": "frozen_yogurt", "46": "garlic_bread", "47": "gnocchi", "48": "greek_salad", "49": "grilled_cheese_sandwich",
"50": "grilled_salmon", "51": "guacamole", "52": "gyoza", "53": "hamburger", "54": "hot_and_sour_soup",
"55": "hot_dog", "56": "huevos_rancheros", "57": "hummus", "58": "ice_cream", "59": "lasagna",
"60": "lobster_bisque", "61": "lobster_roll_sandwich", "62": "macaroni_and_cheese", "63": "macarons", "64": "miso_soup",
"65": "mussels", "66": "nachos", "67": "omelette", "68": "onion_rings", "69": "oysters",
"70": "pad_thai", "71": "paella", "72": "pancakes", "73": "panna_cotta", "74": "peking_duck",
"75": "pho", "76": "pizza", "77": "pork_chop", "78": "poutine", "79": "prime_rib",
"80": "pulled_pork_sandwich", "81": "ramen", "82": "ravioli", "83": "red_velvet_cake", "84": "risotto",
"85": "samosa", "86": "sashimi", "87": "scallops", "88": "seaweed_salad", "89": "shrimp_and_grits",
"90": "spaghetti_bolognese", "91": "spaghetti_carbonara", "92": "spring_rolls", "93": "steak", "94": "strawberry_shortcake",
"95": "sushi", "96": "tacos", "97": "takoyaki", "98": "tiramisu", "99": "tuna_tartare", "100": "waffles"
}
def classify_food(image):
"""Predicts the type of food in the image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
# Sort by descending probability
predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True)[:5])
return predictions
# Gradio Interface
iface = gr.Interface(
fn=classify_food,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=5, label="Top 5 Prediction Scores"),
title="Food-101-93M 🍽️",
description="Upload an image of food to classify it into one of 101 dish categories based on the Food-101 dataset."
)
# Launch app
if __name__ == "__main__":
iface.launch()
Intended Use:
The Food-101-93M model is intended for:
- Recipe Recommendation Engines: Automatically tagging food images to suggest recipes.
- Food Logging & Calorie Tracking Apps: Categorizing meals based on photos.
- Smart Kitchens: Assisting food recognition in smart appliances.
- Restaurant Menu Digitization: Auto-classifying dishes for visual menus or ordering systems.
- Dataset Labeling: Enabling automatic annotation of food datasets for training other ML models.
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