Hugging Face's logo

Spaces:
OmAlve
/
Swin-Transformer-Foods101
Sleeping

App Files Community
1
Swin-Transformer-Foods101
File size: 3,226 Bytes 
d012df7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
98687c5
 
 
 
 
 
d012df7
 
 
 
98687c5
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
 
import timm
from PIL import Image
from torchvision import transforms as T
import gradio as gr
import torch

model = timm.create_model("hf_hub:OmAlve/swin_s3_base_224-Foods-101", pretrained=True)
image_size = (224,224)

test_tf = T.Compose([
    T.Resize(image_size),
    T.ToTensor(),
    T.Normalize(
        mean = (0.5,0.5,0.5),
        std = (0.5,0.5,0.5)
    )
])

labels = [
    "apple_pie",
    "baby_back_ribs",
    "baklava",
    "beef_carpaccio",
    "beef_tartare",
    "beet_salad",
    "beignets",
    "bibimbap",
    "bread_pudding",
    "breakfast_burrito",
    "bruschetta",
    "caesar_salad",
    "cannoli",
    "caprese_salad",
    "carrot_cake",
    "ceviche",
    "cheesecake",
    "cheese_plate",
    "chicken_curry",
    "chicken_quesadilla",
    "chicken_wings",
    "chocolate_cake",
    "chocolate_mousse",
    "churros",
    "clam_chowder",
    "club_sandwich",
    "crab_cakes",
    "creme_brulee",
    "croque_madame",
    "cup_cakes",
    "deviled_eggs",
    "donuts",
    "dumplings",
    "edamame",
    "eggs_benedict",
    "escargots",
    "falafel",
    "filet_mignon",
    "fish_and_chips",
    "foie_gras",
    "french_fries",
    "french_onion_soup",
    "french_toast",
    "fried_calamari",
    "fried_rice",
    "frozen_yogurt",
    "garlic_bread",
    "gnocchi",
    "greek_salad",
    "grilled_cheese_sandwich",
    "grilled_salmon",
    "guacamole",
    "gyoza",
    "hamburger",
    "hot_and_sour_soup",
    "hot_dog",
    "huevos_rancheros",
    "hummus",
    "ice_cream",
    "lasagna",
    "lobster_bisque",
    "lobster_roll_sandwich",
    "macaroni_and_cheese",
    "macarons",
    "miso_soup",
    "mussels",
    "nachos",
    "omelette",
    "onion_rings",
    "oysters",
    "pad_thai",
    "paella",
    "pancakes",
    "panna_cotta",
    "peking_duck",
    "pho",
    "pizza",
    "pork_chop",
    "poutine",
    "prime_rib",
    "pulled_pork_sandwich",
    "ramen",
    "ravioli",
    "red_velvet_cake",
    "risotto",
    "samosa",
    "sashimi",
    "scallops",
    "seaweed_salad",
    "shrimp_and_grits",
    "spaghetti_bolognese",
    "spaghetti_carbonara",
    "spring_rolls",
    "steak",
    "strawberry_shortcake",
    "sushi",
    "tacos",
    "takoyaki",
    "tiramisu",
    "tuna_tartare",
    "waffles"
  ]

def predict(img):
  inp = test_tf(img).unsqueeze(0)
  with torch.no_grad():
    predictions = torch.nn.functional.softmax(model(inp)[0], dim=0)
    toplabels = predictions.argsort(descending=True)[:5]
  results = {labels[label] : float(predictions[label]) for label in toplabels}
  return results

description = """
This is a space for Image Classfication using a Swin Transformer finetuned on the Food101 dataset with Timm and 🤗.
You can find the model [here](https://huggingface.co/OmAlve/swin_s3_base_224-Foods-101)
And the Notebook for finetuning [here](https://github.com/Om-Alve/Finetuning-CV-model/blob/main/Swin-Foods-101.ipynb)

"""
gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             outputs="label",
             examples=['./miso soup.jpg','./cupcake.jpg','./pasta.jpg'],
             title="Food Classification with Swin Transformers",
             description=description,
             live=True).launch()