--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - food-ingredient-classification - food101 - food101-finetuned - generated_from_trainer metrics: - accuracy model-index: - name: finetuned-food101 results: [] --- # finetuned-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.6105 - Accuracy: 0.8400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:| | 4.1344 | 0.0248 | 100 | 4.0304 | 0.3063 | | 3.5328 | 0.0497 | 200 | 3.3729 | 0.4410 | | 2.9715 | 0.0745 | 300 | 2.8900 | 0.5135 | | 2.724 | 0.0994 | 400 | 2.5096 | 0.5443 | | 2.311 | 0.1242 | 500 | 2.1726 | 0.5895 | | 2.266 | 0.1491 | 600 | 2.0223 | 0.5880 | | 1.9671 | 0.1739 | 700 | 1.7585 | 0.6330 | | 1.8617 | 0.1988 | 800 | 1.7300 | 0.6212 | | 1.4694 | 0.2236 | 900 | 1.7507 | 0.6078 | | 1.7876 | 0.2484 | 1000 | 1.6520 | 0.6133 | | 1.7647 | 0.2733 | 1100 | 1.4576 | 0.6598 | | 1.7 | 0.2981 | 1200 | 1.4420 | 0.6577 | | 1.533 | 0.3230 | 1300 | 1.4389 | 0.6537 | | 1.3895 | 0.3478 | 1400 | 1.4178 | 0.6587 | | 1.5497 | 0.3727 | 1500 | 1.3048 | 0.6861 | | 1.3327 | 0.3975 | 1600 | 1.3361 | 0.6714 | | 1.53 | 0.4224 | 1700 | 1.3425 | 0.6697 | | 1.538 | 0.4472 | 1800 | 1.3453 | 0.6642 | | 1.5056 | 0.4720 | 1900 | 1.2742 | 0.6783 | | 1.2728 | 0.4969 | 2000 | 1.1779 | 0.7045 | | 1.1734 | 0.5217 | 2100 | 1.2630 | 0.6808 | | 1.527 | 0.5466 | 2200 | 1.1810 | 0.7023 | | 1.3873 | 0.5714 | 2300 | 1.1831 | 0.7040 | | 1.3545 | 0.5963 | 2400 | 1.1836 | 0.7002 | | 1.4842 | 0.6211 | 2500 | 1.1441 | 0.7129 | | 1.1974 | 0.6460 | 2600 | 1.1230 | 0.7155 | | 1.4204 | 0.6708 | 2700 | 1.1766 | 0.7002 | | 1.152 | 0.6957 | 2800 | 1.2166 | 0.6950 | | 1.162 | 0.7205 | 2900 | 1.1674 | 0.7003 | | 1.4516 | 0.7453 | 3000 | 1.1207 | 0.7140 | | 1.2378 | 0.7702 | 3100 | 1.2072 | 0.6906 | | 0.991 | 0.7950 | 3200 | 1.1122 | 0.7131 | | 1.3078 | 0.8199 | 3300 | 1.1207 | 0.7170 | | 1.1483 | 0.8447 | 3400 | 1.0665 | 0.7245 | | 1.453 | 0.8696 | 3500 | 1.0640 | 0.7267 | | 1.4457 | 0.8944 | 3600 | 1.0565 | 0.7321 | | 1.1636 | 0.9193 | 3700 | 1.0576 | 0.7255 | | 1.157 | 0.9441 | 3800 | 1.0648 | 0.7261 | | 1.1923 | 0.9689 | 3900 | 1.0473 | 0.7271 | | 1.2325 | 0.9938 | 4000 | 1.0501 | 0.7298 | | 1.1503 | 1.0186 | 4100 | 1.0566 | 0.7243 | | 1.0633 | 1.0435 | 4200 | 1.0005 | 0.7444 | | 1.2061 | 1.0683 | 4300 | 1.0196 | 0.7377 | | 1.0315 | 1.0932 | 4400 | 1.0139 | 0.7392 | | 1.038 | 1.1180 | 4500 | 1.0299 | 0.7318 | | 0.7728 | 1.1429 | 4600 | 1.0522 | 0.7257 | | 0.9302 | 1.1677 | 4700 | 1.0219 | 0.7362 | | 1.1084 | 1.1925 | 4800 | 0.9940 | 0.7349 | | 1.0345 | 1.2174 | 4900 | 0.9775 | 0.7446 | | 1.0541 | 1.2422 | 5000 | 1.0076 | 0.7366 | | 0.9345 | 1.2671 | 5100 | 1.0075 | 0.7398 | | 0.9149 | 1.2919 | 5200 | 1.0558 | 0.7261 | | 1.2583 | 1.3168 | 5300 | 0.9703 | 0.7476 | | 1.0745 | 1.3416 | 5400 | 0.9902 | 0.7425 | | 0.8319 | 1.3665 | 5500 | 0.9442 | 0.7553 | | 1.1286 | 1.3913 | 5600 | 0.9620 | 0.7532 | | 0.8228 | 1.4161 | 5700 | 0.9329 | 0.7555 | | 1.3209 | 1.4410 | 5800 | 0.9402 | 0.7543 | | 0.7629 | 1.4658 | 5900 | 0.9497 | 0.7547 | | 0.9906 | 1.4907 | 6000 | 0.9362 | 0.7589 | | 0.9966 | 1.5155 | 6100 | 0.9322 | 0.7595 | | 0.8868 | 1.5404 | 6200 | 0.9613 | 0.7506 | | 0.956 | 1.5652 | 6300 | 0.9370 | 0.7568 | | 1.1833 | 1.5901 | 6400 | 0.9277 | 0.7597 | | 0.9747 | 1.6149 | 6500 | 0.8777 | 0.7696 | | 1.0119 | 1.6398 | 6600 | 0.8980 | 0.7653 | | 0.9764 | 1.6646 | 6700 | 0.9071 | 0.7641 | | 1.0528 | 1.6894 | 6800 | 0.8941 | 0.7694 | | 0.942 | 1.7143 | 6900 | 0.8718 | 0.7737 | | 1.0387 | 1.7391 | 7000 | 0.8615 | 0.7787 | | 0.9054 | 1.7640 | 7100 | 0.8689 | 0.7735 | | 1.0327 | 1.7888 | 7200 | 0.8953 | 0.7692 | | 0.8425 | 1.8137 | 7300 | 0.8533 | 0.7761 | | 0.9388 | 1.8385 | 7400 | 0.8772 | 0.7687 | | 1.1037 | 1.8634 | 7500 | 0.8634 | 0.7731 | | 0.9659 | 1.8882 | 7600 | 0.8502 | 0.7766 | | 1.0133 | 1.9130 | 7700 | 0.8479 | 0.7766 | | 0.8395 | 1.9379 | 7800 | 0.8052 | 0.7889 | | 0.8803 | 1.9627 | 7900 | 0.8379 | 0.7775 | | 0.7866 | 1.9876 | 8000 | 0.8283 | 0.7835 | | 0.5067 | 2.0124 | 8100 | 0.8207 | 0.7835 | | 0.7083 | 2.0373 | 8200 | 0.8320 | 0.7803 | | 0.6581 | 2.0621 | 8300 | 0.8162 | 0.7869 | | 0.7376 | 2.0870 | 8400 | 0.8222 | 0.7871 | | 0.6492 | 2.1118 | 8500 | 0.8153 | 0.7868 | | 0.6356 | 2.1366 | 8600 | 0.7930 | 0.7929 | | 0.7626 | 2.1615 | 8700 | 0.8167 | 0.7874 | | 0.7389 | 2.1863 | 8800 | 0.8076 | 0.7889 | | 0.503 | 2.2112 | 8900 | 0.8312 | 0.7869 | | 0.7901 | 2.2360 | 9000 | 0.8137 | 0.7900 | | 0.8387 | 2.2609 | 9100 | 0.8207 | 0.7832 | | 0.7048 | 2.2857 | 9200 | 0.8105 | 0.7898 | | 0.6412 | 2.3106 | 9300 | 0.7829 | 0.7950 | | 0.6864 | 2.3354 | 9400 | 0.7851 | 0.7941 | | 0.7411 | 2.3602 | 9500 | 0.7642 | 0.8031 | | 0.6221 | 2.3851 | 9600 | 0.7603 | 0.8030 | | 0.7769 | 2.4099 | 9700 | 0.7846 | 0.7975 | | 0.7939 | 2.4348 | 9800 | 0.7914 | 0.7933 | | 0.5641 | 2.4596 | 9900 | 0.7700 | 0.7992 | | 0.8009 | 2.4845 | 10000 | 0.7699 | 0.8015 | | 0.6111 | 2.5093 | 10100 | 0.7603 | 0.8036 | | 0.925 | 2.5342 | 10200 | 0.7727 | 0.8003 | | 0.6206 | 2.5590 | 10300 | 0.7765 | 0.7984 | | 0.5977 | 2.5839 | 10400 | 0.7793 | 0.7960 | | 0.8146 | 2.6087 | 10500 | 0.7799 | 0.7978 | | 0.7869 | 2.6335 | 10600 | 0.7396 | 0.8087 | | 0.8966 | 2.6584 | 10700 | 0.7386 | 0.8071 | | 0.6654 | 2.6832 | 10800 | 0.7305 | 0.8103 | | 0.737 | 2.7081 | 10900 | 0.7317 | 0.8083 | | 0.9283 | 2.7329 | 11000 | 0.7409 | 0.8072 | | 0.7491 | 2.7578 | 11100 | 0.7088 | 0.8153 | | 0.6807 | 2.7826 | 11200 | 0.7154 | 0.8123 | | 0.4485 | 2.8075 | 11300 | 0.6985 | 0.8180 | | 0.6694 | 2.8323 | 11400 | 0.7124 | 0.8147 | | 0.6661 | 2.8571 | 11500 | 0.7075 | 0.8153 | | 0.7971 | 2.8820 | 11600 | 0.7375 | 0.8078 | | 0.9771 | 2.9068 | 11700 | 0.7133 | 0.8133 | | 0.5238 | 2.9317 | 11800 | 0.7077 | 0.8157 | | 0.5636 | 2.9565 | 11900 | 0.7419 | 0.8030 | | 0.8962 | 2.9814 | 12000 | 0.7021 | 0.8175 | | 0.4561 | 3.0062 | 12100 | 0.7031 | 0.8162 | | 0.4906 | 3.0311 | 12200 | 0.7104 | 0.8171 | | 0.5422 | 3.0559 | 12300 | 0.7035 | 0.8154 | | 0.5541 | 3.0807 | 12400 | 0.6905 | 0.8232 | | 0.5009 | 3.1056 | 12500 | 0.6994 | 0.8173 | | 0.4567 | 3.1304 | 12600 | 0.6911 | 0.8203 | | 0.4431 | 3.1553 | 12700 | 0.6933 | 0.8192 | | 0.5915 | 3.1801 | 12800 | 0.6838 | 0.8221 | | 0.5551 | 3.2050 | 12900 | 0.6886 | 0.8199 | | 0.4528 | 3.2298 | 13000 | 0.6883 | 0.8212 | | 0.5563 | 3.2547 | 13100 | 0.6867 | 0.8192 | | 0.4836 | 3.2795 | 13200 | 0.6771 | 0.8253 | | 0.4535 | 3.3043 | 13300 | 0.6713 | 0.8249 | | 0.468 | 3.3292 | 13400 | 0.6616 | 0.8270 | | 0.4691 | 3.3540 | 13500 | 0.6707 | 0.8261 | | 0.4784 | 3.3789 | 13600 | 0.6733 | 0.8241 | | 0.5187 | 3.4037 | 13700 | 0.6658 | 0.8251 | | 0.5105 | 3.4286 | 13800 | 0.6631 | 0.8275 | | 0.3935 | 3.4534 | 13900 | 0.6656 | 0.8283 | | 0.463 | 3.4783 | 14000 | 0.6554 | 0.8301 | | 0.3259 | 3.5031 | 14100 | 0.6640 | 0.8292 | | 0.7286 | 3.5280 | 14200 | 0.6500 | 0.8308 | | 0.4422 | 3.5528 | 14300 | 0.6540 | 0.8313 | | 0.4374 | 3.5776 | 14400 | 0.6497 | 0.8317 | | 0.7962 | 3.6025 | 14500 | 0.6416 | 0.8340 | | 0.6297 | 3.6273 | 14600 | 0.6393 | 0.8339 | | 0.4933 | 3.6522 | 14700 | 0.6379 | 0.8336 | | 0.5548 | 3.6770 | 14800 | 0.6300 | 0.8356 | | 0.564 | 3.7019 | 14900 | 0.6284 | 0.8352 | | 0.2638 | 3.7267 | 15000 | 0.6299 | 0.8338 | | 0.6129 | 3.7516 | 15100 | 0.6253 | 0.8374 | | 0.51 | 3.7764 | 15200 | 0.6205 | 0.8390 | | 0.4612 | 3.8012 | 15300 | 0.6165 | 0.8390 | | 0.5304 | 3.8261 | 15400 | 0.6112 | 0.8412 | | 0.4738 | 3.8509 | 15500 | 0.6149 | 0.8388 | | 0.3845 | 3.8758 | 15600 | 0.6141 | 0.8391 | | 0.4533 | 3.9006 | 15700 | 0.6139 | 0.8399 | | 0.3539 | 3.9255 | 15800 | 0.6131 | 0.8402 | | 0.6485 | 3.9503 | 15900 | 0.6118 | 0.8397 | | 0.331 | 3.9752 | 16000 | 0.6108 | 0.8397 | | 0.3582 | 4.0 | 16100 | 0.6105 | 0.8400 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1