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.

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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()

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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.