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.
1Classification Report:
2 precision recall f1-score support
3
4 apple_pie 0.8399 0.8253 0.8325 750
5 baby_back_ribs 0.9445 0.8853 0.9140 750
6 baklava 0.9736 0.9347 0.9537 750
7 beef_carpaccio 0.9079 0.9200 0.9139 750
8 beef_tartare 0.8486 0.8293 0.8388 750
9 beet_salad 0.8649 0.8707 0.8678 750
10 beignets 0.8961 0.9080 0.9020 750
11 bibimbap 0.9361 0.9373 0.9367 750
12 bread_pudding 0.7979 0.8000 0.7989 750
13 breakfast_burrito 0.8784 0.9053 0.8917 750
14 bruschetta 0.8672 0.8533 0.8602 750
15 caesar_salad 0.9444 0.9293 0.9368 750
16 cannoli 0.9263 0.9547 0.9402 750
17 caprese_salad 0.9110 0.9280 0.9194 750
18 carrot_cake 0.9068 0.8040 0.8523 750
19 ceviche 0.8375 0.8453 0.8414 750
20 cheesecake 0.8225 0.8093 0.8159 750
21 cheese_plate 0.9627 0.9627 0.9627 750
22 chicken_curry 0.8970 0.8827 0.8898 750
23 chicken_quesadilla 0.9254 0.9093 0.9173 750
24 chicken_wings 0.9512 0.9360 0.9435 750
25 chocolate_cake 0.7958 0.8107 0.8032 750
26 chocolate_mousse 0.6947 0.7827 0.7361 750
27 churros 0.9440 0.9440 0.9440 750
28 clam_chowder 0.8883 0.9120 0.9000 750
29 club_sandwich 0.9396 0.9133 0.9263 750
30 crab_cakes 0.9185 0.8720 0.8947 750
31 creme_brulee 0.9141 0.9227 0.9184 750
32 croque_madame 0.9106 0.8960 0.9032 750
33 cup_cakes 0.8986 0.9333 0.9156 750
34 deviled_eggs 0.9787 0.9813 0.9800 750
35 donuts 0.8893 0.8787 0.8840 750
36 dumplings 0.9212 0.8880 0.9043 750
37 edamame 0.9960 0.9920 0.9940 750
38 eggs_benedict 0.9207 0.9440 0.9322 750
39 escargots 0.8709 0.8907 0.8807 750
40 falafel 0.8945 0.8933 0.8939 750
41 filet_mignon 0.7598 0.7467 0.7532 750
42 fish_and_chips 0.9454 0.9467 0.9460 750
43 foie_gras 0.6659 0.8027 0.7279 750
44 french_fries 0.9447 0.9333 0.9390 750
45 french_onion_soup 0.8667 0.9187 0.8919 750
46 french_toast 0.8890 0.8760 0.8825 750
47 fried_calamari 0.9448 0.9133 0.9288 750
48 fried_rice 0.9325 0.9213 0.9269 750
49 frozen_yogurt 0.8716 0.9507 0.9094 750
50 garlic_bread 0.9103 0.8800 0.8949 750
51 gnocchi 0.8554 0.8280 0.8415 750
52 greek_salad 0.9203 0.9240 0.9222 750
53grilled_cheese_sandwich 0.8523 0.8773 0.8647 750
54 grilled_salmon 0.8463 0.8960 0.8705 750
55 guacamole 0.9537 0.9347 0.9441 750
56 gyoza 0.8970 0.9173 0.9071 750
57 hamburger 0.8899 0.8947 0.8923 750
58 hot_and_sour_soup 0.9439 0.9413 0.9426 750
59 hot_dog 0.8859 0.9320 0.9084 750
60 huevos_rancheros 0.8465 0.8827 0.8642 750
61 hummus 0.9394 0.9093 0.9241 750
62 ice_cream 0.8633 0.8507 0.8570 750
63 lasagna 0.8780 0.8733 0.8757 750
64 lobster_bisque 0.8952 0.9107 0.9028 750
65 lobster_roll_sandwich 0.9664 0.9573 0.9618 750
66 macaroni_and_cheese 0.9273 0.9013 0.9141 750
67 macarons 0.9892 0.9747 0.9819 750
68 miso_soup 0.9565 0.9667 0.9615 750
69 mussels 0.9602 0.9640 0.9621 750
70 nachos 0.9337 0.9387 0.9362 750
71 omelette 0.8889 0.8960 0.8924 750
72 onion_rings 0.9493 0.9493 0.9493 750
73 oysters 0.9808 0.9533 0.9669 750
74 pad_thai 0.9188 0.9507 0.9345 750
75 paella 0.9352 0.9240 0.9296 750
76 pancakes 0.9277 0.9067 0.9171 750
77 panna_cotta 0.8056 0.8507 0.8275 750
78 peking_duck 0.8529 0.9120 0.8814 750
79 pho 0.9746 0.9227 0.9479 750
80 pizza 0.9512 0.9360 0.9435 750
81 pork_chop 0.8085 0.7373 0.7713 750
82 poutine 0.9424 0.9387 0.9405 750
83 prime_rib 0.9106 0.8147 0.8600 750
84 pulled_pork_sandwich 0.8887 0.9053 0.8970 750
85 ramen 0.8986 0.9213 0.9098 750
86 ravioli 0.8532 0.8293 0.8411 750
87 red_velvet_cake 0.9330 0.8907 0.9113 750
88 risotto 0.8809 0.8680 0.8744 750
89 samosa 0.9153 0.9227 0.9190 750
90 sashimi 0.9248 0.9187 0.9217 750
91 scallops 0.8564 0.8507 0.8535 750
92 seaweed_salad 0.9597 0.9533 0.9565 750
93 shrimp_and_grits 0.8995 0.8947 0.8971 750
94 spaghetti_bolognese 0.9667 0.9667 0.9667 750
95 spaghetti_carbonara 0.9601 0.9627 0.9614 750
96 spring_rolls 0.9045 0.9467 0.9251 750
97 steak 0.6311 0.7027 0.6650 750
98 strawberry_shortcake 0.8832 0.8467 0.8645 750
99 sushi 0.9204 0.8947 0.9074 750
100 tacos 0.9225 0.8893 0.9056 750
101 takoyaki 0.9419 0.9507 0.9463 750
102 tiramisu 0.9074 0.8627 0.8845 750
103 tuna_tartare 0.7691 0.7773 0.7732 750
104 waffles 0.9629 0.9347 0.9486 750
105
106 accuracy 0.8973 75750
107 macro avg 0.8987 0.8973 0.8977 75750
108 weighted avg 0.8987 0.8973 0.8977 75750sushi, hamburger, waffles, pad_thai, and more.!pip install -q transformers torch pillow gradio1import gradio as gr
2from transformers import AutoImageProcessor, SiglipForImageClassification
3from PIL import Image
4import torch
5
6# Load model and processor
7model_name = "prithivMLmods/Food-101-93M"
8model = SiglipForImageClassification.from_pretrained(model_name)
9processor = AutoImageProcessor.from_pretrained(model_name)
10
11# Food-101 labels
12labels = {
13 "0": "apple_pie", "1": "baby_back_ribs", "2": "baklava", "3": "beef_carpaccio", "4": "beef_tartare",
14 "5": "beet_salad", "6": "beignets", "7": "bibimbap", "8": "bread_pudding", "9": "breakfast_burrito",
15 "10": "bruschetta", "11": "caesar_salad", "12": "cannoli", "13": "caprese_salad", "14": "carrot_cake",
16 "15": "ceviche", "16": "cheesecake", "17": "cheese_plate", "18": "chicken_curry", "19": "chicken_quesadilla",
17 "20": "chicken_wings", "21": "chocolate_cake", "22": "chocolate_mousse", "23": "churros", "24": "clam_chowder",
18 "25": "club_sandwich", "26": "crab_cakes", "27": "creme_brulee", "28": "croque_madame", "29": "cup_cakes",
19 "30": "deviled_eggs", "31": "donuts", "32": "dumplings", "33": "edamame", "34": "eggs_benedict",
20 "35": "escargots", "36": "falafel", "37": "filet_mignon", "38": "fish_and_chips", "39": "foie_gras",
21 "40": "french_fries", "41": "french_onion_soup", "42": "french_toast", "43": "fried_calamari", "44": "fried_rice",
22 "45": "frozen_yogurt", "46": "garlic_bread", "47": "gnocchi", "48": "greek_salad", "49": "grilled_cheese_sandwich",
23 "50": "grilled_salmon", "51": "guacamole", "52": "gyoza", "53": "hamburger", "54": "hot_and_sour_soup",
24 "55": "hot_dog", "56": "huevos_rancheros", "57": "hummus", "58": "ice_cream", "59": "lasagna",
25 "60": "lobster_bisque", "61": "lobster_roll_sandwich", "62": "macaroni_and_cheese", "63": "macarons", "64": "miso_soup",
26 "65": "mussels", "66": "nachos", "67": "omelette", "68": "onion_rings", "69": "oysters",
27 "70": "pad_thai", "71": "paella", "72": "pancakes", "73": "panna_cotta", "74": "peking_duck",
28 "75": "pho", "76": "pizza", "77": "pork_chop", "78": "poutine", "79": "prime_rib",
29 "80": "pulled_pork_sandwich", "81": "ramen", "82": "ravioli", "83": "red_velvet_cake", "84": "risotto",
30 "85": "samosa", "86": "sashimi", "87": "scallops", "88": "seaweed_salad", "89": "shrimp_and_grits",
31 "90": "spaghetti_bolognese", "91": "spaghetti_carbonara", "92": "spring_rolls", "93": "steak", "94": "strawberry_shortcake",
32 "95": "sushi", "96": "tacos", "97": "takoyaki", "98": "tiramisu", "99": "tuna_tartare", "100": "waffles"
33}
34
35def classify_food(image):
36 """Predicts the type of food in the image."""
37 image = Image.fromarray(image).convert("RGB")
38 inputs = processor(images=image, return_tensors="pt")
39
40 with torch.no_grad():
41 outputs = model(**inputs)
42 logits = outputs.logits
43 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
44
45 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
46 # Sort by descending probability
47 predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True)[:5])
48
49 return predictions
50
51# Gradio Interface
52iface = gr.Interface(
53 fn=classify_food,
54 inputs=gr.Image(type="numpy"),
55 outputs=gr.Label(num_top_classes=5, label="Top 5 Prediction Scores"),
56 title="Food-101-93M 🍽️",
57 description="Upload an image of food to classify it into one of 101 dish categories based on the Food-101 dataset."
58)
59
60# Launch app
61if __name__ == "__main__":
62 iface.launch()