--- license: apache-2.0 metrics: - accuracy base_model: - google/vit-base-patch16-224-in21k --- Achieved 75% accuracy for a validation dataset for classifying 80 types of common Indian food. See [my Kaggle notebook](https://www.kaggle.com/code/dima806/indian-food-image-detection-vit) for more details. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6449300e3adf50d864095b90/s8tU9m3FfY34jZqNOSluO.png) ``` Classification report: precision recall f1-score support adhirasam 0.9412 0.8000 0.8649 20 aloo_gobi 0.7857 0.5500 0.6471 20 aloo_matar 0.8500 0.8500 0.8500 20 aloo_methi 0.7407 1.0000 0.8511 20 aloo_shimla_mirch 0.7619 0.8000 0.7805 20 aloo_tikki 1.0000 0.7500 0.8571 20 anarsa 1.0000 0.7000 0.8235 20 ariselu 0.7692 1.0000 0.8696 20 bandar_laddu 0.8333 0.7500 0.7895 20 basundi 0.2254 0.8000 0.3516 20 bhatura 0.7600 0.9500 0.8444 20 bhindi_masala 0.8636 0.9500 0.9048 20 biryani 0.8571 0.9000 0.8780 20 boondi 0.9474 0.9000 0.9231 20 butter_chicken 0.4419 0.9500 0.6032 20 chak_hao_kheer 0.9474 0.9000 0.9231 20 cham_cham 1.0000 0.4000 0.5714 20 chana_masala 0.7692 1.0000 0.8696 20 chapati 0.7407 1.0000 0.8511 20 chhena_kheeri 0.0000 0.0000 0.0000 20 chicken_razala 0.8000 1.0000 0.8889 20 chicken_tikka 0.9091 0.5000 0.6452 20 chicken_tikka_masala 0.7273 0.4000 0.5161 20 chikki 0.7308 0.9500 0.8261 20 daal_baati_churma 0.6957 0.8000 0.7442 20 daal_puri 1.0000 0.3000 0.4615 20 dal_makhani 0.8182 0.9000 0.8571 20 dal_tadka 0.6552 0.9500 0.7755 20 dharwad_pedha 1.0000 0.8000 0.8889 20 doodhpak 0.6667 0.1000 0.1739 20 double_ka_meetha 0.7917 0.9500 0.8636 20 dum_aloo 0.8462 0.5500 0.6667 20 gajar_ka_halwa 0.8000 1.0000 0.8889 20 gavvalu 0.8095 0.8500 0.8293 20 ghevar 1.0000 0.8000 0.8889 20 gulab_jamun 0.5429 0.9500 0.6909 20 imarti 0.8333 1.0000 0.9091 20 jalebi 0.9474 0.9000 0.9231 20 kachori 0.6364 0.7000 0.6667 20 kadai_paneer 0.6923 0.9000 0.7826 20 kadhi_pakoda 0.8500 0.8500 0.8500 20 kajjikaya 0.9412 0.8000 0.8649 20 kakinada_khaja 0.8824 0.7500 0.8108 20 kalakand 0.7692 0.5000 0.6061 20 karela_bharta 1.0000 0.2000 0.3333 20 kofta 0.9333 0.7000 0.8000 20 kuzhi_paniyaram 0.6667 0.9000 0.7660 20 lassi 0.8000 1.0000 0.8889 20 ledikeni 0.5714 0.2000 0.2963 20 litti_chokha 1.0000 0.8000 0.8889 20 lyangcha 0.8947 0.8500 0.8718 20 maach_jhol 0.9375 0.7500 0.8333 20 makki_di_roti_sarson_da_saag 1.0000 0.8500 0.9189 20 malapua 1.0000 0.7000 0.8235 20 misi_roti 0.8571 0.9000 0.8780 20 misti_doi 0.6364 0.7000 0.6667 20 modak 0.7826 0.9000 0.8372 20 mysore_pak 0.7917 0.9500 0.8636 20 naan 0.9091 1.0000 0.9524 20 navrattan_korma 0.9286 0.6500 0.7647 20 palak_paneer 0.7917 0.9500 0.8636 20 paneer_butter_masala 0.6667 0.7000 0.6829 20 phirni 0.5500 0.5500 0.5500 20 pithe 1.0000 0.2500 0.4000 20 poha 0.6786 0.9500 0.7917 20 poornalu 0.9000 0.9000 0.9000 20 pootharekulu 0.8636 0.9500 0.9048 20 qubani_ka_meetha 1.0000 0.6500 0.7879 20 rabri 0.0000 0.0000 0.0000 20 ras_malai 0.7083 0.8500 0.7727 20 rasgulla 0.5263 1.0000 0.6897 20 sandesh 0.6000 0.1500 0.2400 20 shankarpali 0.8333 1.0000 0.9091 20 sheer_korma 0.4643 0.6500 0.5417 20 sheera 0.8667 0.6500 0.7429 20 shrikhand 0.8000 0.6000 0.6857 20 sohan_halwa 1.0000 0.5000 0.6667 20 sohan_papdi 0.5556 1.0000 0.7143 20 sutar_feni 0.8571 0.9000 0.8780 20 unni_appam 0.5556 0.7500 0.6383 20 accuracy 0.7519 1600 macro avg 0.7813 0.7519 0.7352 1600 weighted avg 0.7813 0.7519 0.7352 1600 ```