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dima806
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indian_food_image_detection

Image Classification
Transformers
PyTorch
Safetensors
vit
Inference Endpoints
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3
  metrics:
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  - accuracy
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  ---
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- Achieved 74% accuracy for a validation dataset for classifying 80 types of common Indian food.
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  See [my Kaggle notebook](https://www.kaggle.com/code/dima806/indian-food-image-detection-vit) for more details.
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/wGyVYgTY8e8ketTbOx11n.png)
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  ```
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  Classification report:
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  precision recall f1-score support
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- adhirasam 1.0000 0.6500 0.7879 20
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- aloo_gobi 0.8750 0.7000 0.7778 20
19
- aloo_matar 0.7391 0.8500 0.7907 20
20
- aloo_methi 0.6667 1.0000 0.8000 20
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- aloo_shimla_mirch 0.6957 0.8000 0.7442 20
22
- aloo_tikki 1.0000 0.7500 0.8571 20
23
- anarsa 1.0000 0.8000 0.8889 20
24
- ariselu 0.6552 0.9500 0.7755 20
25
- bandar_laddu 0.6818 0.7500 0.7143 20
26
- basundi 0.2571 0.9000 0.4000 20
27
- bhatura 0.7692 1.0000 0.8696 20
28
- bhindi_masala 0.8696 1.0000 0.9302 20
29
- biryani 0.8000 1.0000 0.8889 20
30
- boondi 0.8889 0.8000 0.8421 20
31
- butter_chicken 0.4146 0.8500 0.5574 20
32
- chak_hao_kheer 1.0000 0.8500 0.9189 20
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- cham_cham 1.0000 0.2500 0.4000 20
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- chana_masala 0.8696 1.0000 0.9302 20
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- chapati 0.7143 1.0000 0.8333 20
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  chhena_kheeri 0.0000 0.0000 0.0000 20
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- chicken_razala 0.9048 0.9500 0.9268 20
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- chicken_tikka 0.7143 0.5000 0.5882 20
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- chicken_tikka_masala 0.6364 0.3500 0.4516 20
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- chikki 0.7917 0.9500 0.8636 20
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- daal_baati_churma 0.8500 0.8500 0.8500 20
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- daal_puri 1.0000 0.3000 0.4615 20
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- dal_makhani 0.8182 0.9000 0.8571 20
44
- dal_tadka 0.6552 0.9500 0.7755 20
45
- dharwad_pedha 0.8889 0.8000 0.8421 20
46
- doodhpak 1.0000 0.1500 0.2609 20
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- double_ka_meetha 0.7273 0.8000 0.7619 20
48
- dum_aloo 0.7647 0.6500 0.7027 20
49
- gajar_ka_halwa 0.6667 1.0000 0.8000 20
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- gavvalu 0.9375 0.7500 0.8333 20
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  ghevar 1.0000 0.8500 0.9189 20
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  gulab_jamun 0.5758 0.9500 0.7170 20
53
- imarti 0.8696 1.0000 0.9302 20
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- jalebi 0.9474 0.9000 0.9231 20
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- kachori 0.7368 0.7000 0.7179 20
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- kadai_paneer 0.6667 0.8000 0.7273 20
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- kadhi_pakoda 0.8947 0.8500 0.8718 20
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- kajjikaya 0.8182 0.9000 0.8571 20
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- kakinada_khaja 0.8333 0.7500 0.7895 20
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- kalakand 0.7333 0.5500 0.6286 20
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  karela_bharta 1.0000 0.1000 0.1818 20
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- kofta 0.9412 0.8000 0.8649 20
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- kuzhi_paniyaram 0.6667 0.9000 0.7660 20
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- lassi 0.8333 1.0000 0.9091 20
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- ledikeni 0.7273 0.4000 0.5161 20
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- litti_chokha 0.9444 0.8500 0.8947 20
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- lyangcha 0.9231 0.6000 0.7273 20
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- maach_jhol 0.9474 0.9000 0.9231 20
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- makki_di_roti_sarson_da_saag 0.9524 1.0000 0.9756 20
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- malapua 0.9091 0.5000 0.6452 20
71
- misi_roti 0.9091 1.0000 0.9524 20
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- misti_doi 0.5862 0.8500 0.6939 20
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- modak 0.7917 0.9500 0.8636 20
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  mysore_pak 0.7500 0.9000 0.8182 20
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- naan 0.9000 0.9000 0.9000 20
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  navrattan_korma 0.9091 0.5000 0.6452 20
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  palak_paneer 0.8947 0.8500 0.8718 20
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- paneer_butter_masala 0.8750 0.7000 0.7778 20
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- phirni 0.5789 0.5500 0.5641 20
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- pithe 1.0000 0.2000 0.3333 20
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- poha 0.7692 1.0000 0.8696 20
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- poornalu 0.8500 0.8500 0.8500 20
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- pootharekulu 0.8571 0.9000 0.8780 20
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- qubani_ka_meetha 1.0000 0.6500 0.7879 20
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  rabri 0.0000 0.0000 0.0000 20
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- ras_malai 0.6800 0.8500 0.7556 20
87
- rasgulla 0.5128 1.0000 0.6780 20
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- sandesh 0.4000 0.1000 0.1600 20
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- shankarpali 0.8636 0.9500 0.9048 20
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- sheer_korma 0.4800 0.6000 0.5333 20
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- sheera 0.7273 0.4000 0.5161 20
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- shrikhand 0.6667 0.5000 0.5714 20
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- sohan_halwa 1.0000 0.6500 0.7879 20
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- sohan_papdi 0.5000 0.9000 0.6429 20
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- sutar_feni 0.8500 0.8500 0.8500 20
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- unni_appam 0.5152 0.8500 0.6415 20
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- accuracy 0.7444 1600
99
- macro avg 0.7755 0.7444 0.7252 1600
100
- weighted avg 0.7755 0.7444 0.7252 1600
101
  ```
 
3
  metrics:
4
  - accuracy
5
  ---
6
+ Achieved 75% accuracy for a validation dataset for classifying 80 types of common Indian food.
7
 
8
  See [my Kaggle notebook](https://www.kaggle.com/code/dima806/indian-food-image-detection-vit) for more details.
9
 
10
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/kxYPpVt28p4J1OzKFSoT5.png)
11
 
12
  ```
13
  Classification report:
14
 
15
  precision recall f1-score support
16
 
17
+ adhirasam 0.9231 0.6000 0.7273 20
18
+ aloo_gobi 0.8667 0.6500 0.7429 20
19
+ aloo_matar 0.7826 0.9000 0.8372 20
20
+ aloo_methi 0.7143 1.0000 0.8333 20
21
+ aloo_shimla_mirch 0.7143 0.7500 0.7317 20
22
+ aloo_tikki 1.0000 0.8000 0.8889 20
23
+ anarsa 1.0000 0.5000 0.6667 20
24
+ ariselu 0.6897 1.0000 0.8163 20
25
+ bandar_laddu 0.7727 0.8500 0.8095 20
26
+ basundi 0.2687 0.9000 0.4138 20
27
+ bhatura 0.6552 0.9500 0.7755 20
28
+ bhindi_masala 0.8333 1.0000 0.9091 20
29
+ biryani 0.9091 1.0000 0.9524 20
30
+ boondi 0.9474 0.9000 0.9231 20
31
+ butter_chicken 0.4419 0.9500 0.6032 20
32
+ chak_hao_kheer 0.9444 0.8500 0.8947 20
33
+ cham_cham 1.0000 0.3000 0.4615 20
34
+ chana_masala 0.9091 1.0000 0.9524 20
35
+ chapati 0.7600 0.9500 0.8444 20
36
  chhena_kheeri 0.0000 0.0000 0.0000 20
37
+ chicken_razala 0.7727 0.8500 0.8095 20
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+ chicken_tikka 0.8462 0.5500 0.6667 20
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+ chicken_tikka_masala 0.7000 0.3500 0.4667 20
40
+ chikki 0.7037 0.9500 0.8085 20
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+ daal_baati_churma 0.7500 0.7500 0.7500 20
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+ daal_puri 1.0000 0.4500 0.6207 20
43
+ dal_makhani 0.8636 0.9500 0.9048 20
44
+ dal_tadka 0.5758 0.9500 0.7170 20
45
+ dharwad_pedha 0.9333 0.7000 0.8000 20
46
+ doodhpak 0.6667 0.1000 0.1739 20
47
+ double_ka_meetha 0.8571 0.9000 0.8780 20
48
+ dum_aloo 0.7857 0.5500 0.6471 20
49
+ gajar_ka_halwa 0.8000 1.0000 0.8889 20
50
+ gavvalu 0.8889 0.8000 0.8421 20
51
  ghevar 1.0000 0.8500 0.9189 20
52
  gulab_jamun 0.5758 0.9500 0.7170 20
53
+ imarti 0.7917 0.9500 0.8636 20
54
+ jalebi 0.9444 0.8500 0.8947 20
55
+ kachori 0.8333 0.7500 0.7895 20
56
+ kadai_paneer 0.6923 0.9000 0.7826 20
57
+ kadhi_pakoda 0.8182 0.9000 0.8571 20
58
+ kajjikaya 0.9444 0.8500 0.8947 20
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+ kakinada_khaja 0.7895 0.7500 0.7692 20
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+ kalakand 0.8571 0.6000 0.7059 20
61
  karela_bharta 1.0000 0.1000 0.1818 20
62
+ kofta 0.8824 0.7500 0.8108 20
63
+ kuzhi_paniyaram 0.7500 0.9000 0.8182 20
64
+ lassi 0.7600 0.9500 0.8444 20
65
+ ledikeni 0.8000 0.4000 0.5333 20
66
+ litti_chokha 0.9412 0.8000 0.8649 20
67
+ lyangcha 0.8235 0.7000 0.7568 20
68
+ maach_jhol 0.8889 0.8000 0.8421 20
69
+ makki_di_roti_sarson_da_saag 0.9091 1.0000 0.9524 20
70
+ malapua 0.9375 0.7500 0.8333 20
71
+ misi_roti 0.9474 0.9000 0.9231 20
72
+ misti_doi 0.6250 0.7500 0.6818 20
73
+ modak 0.8947 0.8500 0.8718 20
74
  mysore_pak 0.7500 0.9000 0.8182 20
75
+ naan 0.9524 1.0000 0.9756 20
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  navrattan_korma 0.9091 0.5000 0.6452 20
77
  palak_paneer 0.8947 0.8500 0.8718 20
78
+ paneer_butter_masala 0.7778 0.7000 0.7368 20
79
+ phirni 0.5238 0.5500 0.5366 20
80
+ pithe 1.0000 0.2500 0.4000 20
81
+ poha 0.7143 1.0000 0.8333 20
82
+ poornalu 0.7308 0.9500 0.8261 20
83
+ pootharekulu 0.9091 1.0000 0.9524 20
84
+ qubani_ka_meetha 1.0000 0.5500 0.7097 20
85
  rabri 0.0000 0.0000 0.0000 20
86
+ ras_malai 0.5926 0.8000 0.6809 20
87
+ rasgulla 0.4545 1.0000 0.6250 20
88
+ sandesh 0.6667 0.3000 0.4138 20
89
+ shankarpali 0.8696 1.0000 0.9302 20
90
+ sheer_korma 0.5000 0.7000 0.5833 20
91
+ sheera 0.9231 0.6000 0.7273 20
92
+ shrikhand 0.6875 0.5500 0.6111 20
93
+ sohan_halwa 1.0000 0.4500 0.6207 20
94
+ sohan_papdi 0.5758 0.9500 0.7170 20
95
+ sutar_feni 0.9000 0.9000 0.9000 20
96
+ unni_appam 0.6207 0.9000 0.7347 20
97
 
98
+ accuracy 0.7513 1600
99
+ macro avg 0.7829 0.7513 0.7339 1600
100
+ weighted avg 0.7829 0.7512 0.7339 1600
101
  ```