finetuned-fake-food / README.md

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	---
	library_name: transformers
	license: apache-2.0
	base_model: google/vit-base-patch16-224-in21k
	tags:
	- image-classification
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: finetuned-fake-food
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# finetuned-fake-food

	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 indian_food_images dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1348
	- Accuracy: 0.9563

	## 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: 10
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|
	\| 0.3831 \| 0.1010 \| 100 \| 0.2663 \| 0.8912 \|
	\| 0.3699 \| 0.2020 \| 200 \| 0.2570 \| 0.8990 \|
	\| 0.2135 \| 0.3030 \| 300 \| 0.6753 \| 0.7837 \|
	\| 0.214 \| 0.4040 \| 400 \| 0.2690 \| 0.8901 \|
	\| 0.1947 \| 0.5051 \| 500 \| 0.2533 \| 0.9112 \|
	\| 0.3618 \| 0.6061 \| 600 \| 0.3738 \| 0.8571 \|
	\| 0.2065 \| 0.7071 \| 700 \| 0.2919 \| 0.8919 \|
	\| 0.3103 \| 0.8081 \| 800 \| 0.2165 \| 0.9169 \|
	\| 0.1479 \| 0.9091 \| 900 \| 0.2135 \| 0.9173 \|
	\| 0.2421 \| 1.0101 \| 1000 \| 0.2187 \| 0.9184 \|
	\| 0.2264 \| 1.1111 \| 1100 \| 0.1888 \| 0.9205 \|
	\| 0.1664 \| 1.2121 \| 1200 \| 0.2607 \| 0.8876 \|
	\| 0.2049 \| 1.3131 \| 1300 \| 0.2502 \| 0.9005 \|
	\| 0.1503 \| 1.4141 \| 1400 \| 0.2305 \| 0.9177 \|
	\| 0.1846 \| 1.5152 \| 1500 \| 0.1881 \| 0.9219 \|
	\| 0.1571 \| 1.6162 \| 1600 \| 0.1788 \| 0.9284 \|
	\| 0.4091 \| 1.7172 \| 1700 \| 0.2228 \| 0.9216 \|
	\| 0.2954 \| 1.8182 \| 1800 \| 0.1653 \| 0.9366 \|
	\| 0.1366 \| 1.9192 \| 1900 \| 0.1529 \| 0.9420 \|
	\| 0.1657 \| 2.0202 \| 2000 \| 0.1745 \| 0.9255 \|
	\| 0.2531 \| 2.1212 \| 2100 \| 0.1744 \| 0.9381 \|
	\| 0.152 \| 2.2222 \| 2200 \| 0.2513 \| 0.8951 \|
	\| 0.145 \| 2.3232 \| 2300 \| 0.1718 \| 0.9302 \|
	\| 0.202 \| 2.4242 \| 2400 \| 0.2436 \| 0.9033 \|
	\| 0.1346 \| 2.5253 \| 2500 \| 0.1839 \| 0.9234 \|
	\| 0.1554 \| 2.6263 \| 2600 \| 0.1447 \| 0.9463 \|
	\| 0.183 \| 2.7273 \| 2700 \| 0.2474 \| 0.8822 \|
	\| 0.0972 \| 2.8283 \| 2800 \| 0.2223 \| 0.9205 \|
	\| 0.1073 \| 2.9293 \| 2900 \| 0.1860 \| 0.9345 \|
	\| 0.1824 \| 3.0303 \| 3000 \| 0.2324 \| 0.9194 \|
	\| 0.1221 \| 3.1313 \| 3100 \| 0.1475 \| 0.9449 \|
	\| 0.1039 \| 3.2323 \| 3200 \| 0.1480 \| 0.9427 \|
	\| 0.276 \| 3.3333 \| 3300 \| 0.1591 \| 0.9402 \|
	\| 0.2498 \| 3.4343 \| 3400 \| 0.2447 \| 0.9098 \|
	\| 0.1453 \| 3.5354 \| 3500 \| 0.1556 \| 0.9416 \|
	\| 0.1794 \| 3.6364 \| 3600 \| 0.2272 \| 0.9083 \|
	\| 0.1467 \| 3.7374 \| 3700 \| 0.1673 \| 0.9413 \|
	\| 0.1372 \| 3.8384 \| 3800 \| 0.1763 \| 0.9341 \|
	\| 0.2283 \| 3.9394 \| 3900 \| 0.1671 \| 0.9373 \|
	\| 0.164 \| 4.0404 \| 4000 \| 0.1490 \| 0.9477 \|
	\| 0.1513 \| 4.1414 \| 4100 \| 0.1547 \| 0.9488 \|
	\| 0.0991 \| 4.2424 \| 4200 \| 0.1536 \| 0.9431 \|
	\| 0.1419 \| 4.3434 \| 4300 \| 0.1568 \| 0.9445 \|
	\| 0.1452 \| 4.4444 \| 4400 \| 0.2328 \| 0.9320 \|
	\| 0.1445 \| 4.5455 \| 4500 \| 0.1351 \| 0.9513 \|
	\| 0.1366 \| 4.6465 \| 4600 \| 0.1571 \| 0.9416 \|
	\| 0.097 \| 4.7475 \| 4700 \| 0.1506 \| 0.9424 \|
	\| 0.0603 \| 4.8485 \| 4800 \| 0.1435 \| 0.9499 \|
	\| 0.1179 \| 4.9495 \| 4900 \| 0.1754 \| 0.9363 \|
	\| 0.1948 \| 5.0505 \| 5000 \| 0.1609 \| 0.9402 \|
	\| 0.1021 \| 5.1515 \| 5100 \| 0.1566 \| 0.9459 \|
	\| 0.0652 \| 5.2525 \| 5200 \| 0.1564 \| 0.9481 \|
	\| 0.1029 \| 5.3535 \| 5300 \| 0.1410 \| 0.9492 \|
	\| 0.1014 \| 5.4545 \| 5400 \| 0.1490 \| 0.9531 \|
	\| 0.1338 \| 5.5556 \| 5500 \| 0.1865 \| 0.9406 \|
	\| 0.0844 \| 5.6566 \| 5600 \| 0.1631 \| 0.9456 \|
	\| 0.1059 \| 5.7576 \| 5700 \| 0.1738 \| 0.9409 \|
	\| 0.0788 \| 5.8586 \| 5800 \| 0.1801 \| 0.9370 \|
	\| 0.0941 \| 5.9596 \| 5900 \| 0.1575 \| 0.9495 \|
	\| 0.112 \| 6.0606 \| 6000 \| 0.1796 \| 0.9470 \|
	\| 0.0691 \| 6.1616 \| 6100 \| 0.1697 \| 0.9499 \|
	\| 0.1385 \| 6.2626 \| 6200 \| 0.1348 \| 0.9563 \|
	\| 0.1173 \| 6.3636 \| 6300 \| 0.1522 \| 0.9502 \|
	\| 0.046 \| 6.4646 \| 6400 \| 0.2114 \| 0.9391 \|
	\| 0.0319 \| 6.5657 \| 6500 \| 0.1723 \| 0.9477 \|
	\| 0.0757 \| 6.6667 \| 6600 \| 0.1561 \| 0.9527 \|
	\| 0.0744 \| 6.7677 \| 6700 \| 0.1587 \| 0.9567 \|
	\| 0.0341 \| 6.8687 \| 6800 \| 0.1458 \| 0.9578 \|
	\| 0.1512 \| 6.9697 \| 6900 \| 0.1572 \| 0.9531 \|
	\| 0.0153 \| 7.0707 \| 7000 \| 0.1402 \| 0.9617 \|
	\| 0.0711 \| 7.1717 \| 7100 \| 0.1527 \| 0.9610 \|
	\| 0.0453 \| 7.2727 \| 7200 \| 0.1512 \| 0.9570 \|
	\| 0.0052 \| 7.3737 \| 7300 \| 0.1936 \| 0.9520 \|
	\| 0.0477 \| 7.4747 \| 7400 \| 0.1699 \| 0.9513 \|
	\| 0.091 \| 7.5758 \| 7500 \| 0.1628 \| 0.9513 \|
	\| 0.063 \| 7.6768 \| 7600 \| 0.1474 \| 0.9578 \|
	\| 0.0497 \| 7.7778 \| 7700 \| 0.1389 \| 0.9613 \|
	\| 0.0552 \| 7.8788 \| 7800 \| 0.2587 \| 0.9381 \|
	\| 0.0364 \| 7.9798 \| 7900 \| 0.1361 \| 0.9603 \|
	\| 0.0124 \| 8.0808 \| 8000 \| 0.1438 \| 0.9606 \|
	\| 0.0703 \| 8.1818 \| 8100 \| 0.1577 \| 0.9585 \|
	\| 0.025 \| 8.2828 \| 8200 \| 0.1943 \| 0.9484 \|
	\| 0.0259 \| 8.3838 \| 8300 \| 0.1590 \| 0.9613 \|
	\| 0.0049 \| 8.4848 \| 8400 \| 0.1521 \| 0.9581 \|
	\| 0.0174 \| 8.5859 \| 8500 \| 0.1522 \| 0.9599 \|
	\| 0.0194 \| 8.6869 \| 8600 \| 0.1456 \| 0.9606 \|
	\| 0.0315 \| 8.7879 \| 8700 \| 0.1411 \| 0.9599 \|
	\| 0.0419 \| 8.8889 \| 8800 \| 0.1426 \| 0.9592 \|
	\| 0.0193 \| 8.9899 \| 8900 \| 0.1375 \| 0.9642 \|
	\| 0.0027 \| 9.0909 \| 9000 \| 0.1379 \| 0.9635 \|
	\| 0.0345 \| 9.1919 \| 9100 \| 0.1444 \| 0.9631 \|
	\| 0.0291 \| 9.2929 \| 9200 \| 0.1492 \| 0.9624 \|
	\| 0.017 \| 9.3939 \| 9300 \| 0.1466 \| 0.9635 \|
	\| 0.0269 \| 9.4949 \| 9400 \| 0.1523 \| 0.9631 \|
	\| 0.003 \| 9.5960 \| 9500 \| 0.1445 \| 0.9628 \|
	\| 0.0471 \| 9.6970 \| 9600 \| 0.1454 \| 0.9617 \|
	\| 0.0356 \| 9.7980 \| 9700 \| 0.1452 \| 0.9620 \|
	\| 0.0034 \| 9.8990 \| 9800 \| 0.1445 \| 0.9624 \|
	\| 0.0162 \| 10.0 \| 9900 \| 0.1451 \| 0.9628 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.4.1+cu121
	- Datasets 3.0.0
	- Tokenizers 0.19.1

	---
	library_name: transformers
	license: apache-2.0
	base_model: google/vit-base-patch16-224-in21k
	tags:
	- image-classification
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: finetuned-fake-food
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# finetuned-fake-food

	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 indian_food_images dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1348
	- Accuracy: 0.9563

	## 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: 10
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|
	\| 0.3831 \| 0.1010 \| 100 \| 0.2663 \| 0.8912 \|
	\| 0.3699 \| 0.2020 \| 200 \| 0.2570 \| 0.8990 \|
	\| 0.2135 \| 0.3030 \| 300 \| 0.6753 \| 0.7837 \|
	\| 0.214 \| 0.4040 \| 400 \| 0.2690 \| 0.8901 \|
	\| 0.1947 \| 0.5051 \| 500 \| 0.2533 \| 0.9112 \|
	\| 0.3618 \| 0.6061 \| 600 \| 0.3738 \| 0.8571 \|
	\| 0.2065 \| 0.7071 \| 700 \| 0.2919 \| 0.8919 \|
	\| 0.3103 \| 0.8081 \| 800 \| 0.2165 \| 0.9169 \|
	\| 0.1479 \| 0.9091 \| 900 \| 0.2135 \| 0.9173 \|
	\| 0.2421 \| 1.0101 \| 1000 \| 0.2187 \| 0.9184 \|
	\| 0.2264 \| 1.1111 \| 1100 \| 0.1888 \| 0.9205 \|
	\| 0.1664 \| 1.2121 \| 1200 \| 0.2607 \| 0.8876 \|
	\| 0.2049 \| 1.3131 \| 1300 \| 0.2502 \| 0.9005 \|
	\| 0.1503 \| 1.4141 \| 1400 \| 0.2305 \| 0.9177 \|
	\| 0.1846 \| 1.5152 \| 1500 \| 0.1881 \| 0.9219 \|
	\| 0.1571 \| 1.6162 \| 1600 \| 0.1788 \| 0.9284 \|
	\| 0.4091 \| 1.7172 \| 1700 \| 0.2228 \| 0.9216 \|
	\| 0.2954 \| 1.8182 \| 1800 \| 0.1653 \| 0.9366 \|
	\| 0.1366 \| 1.9192 \| 1900 \| 0.1529 \| 0.9420 \|
	\| 0.1657 \| 2.0202 \| 2000 \| 0.1745 \| 0.9255 \|
	\| 0.2531 \| 2.1212 \| 2100 \| 0.1744 \| 0.9381 \|
	\| 0.152 \| 2.2222 \| 2200 \| 0.2513 \| 0.8951 \|
	\| 0.145 \| 2.3232 \| 2300 \| 0.1718 \| 0.9302 \|
	\| 0.202 \| 2.4242 \| 2400 \| 0.2436 \| 0.9033 \|
	\| 0.1346 \| 2.5253 \| 2500 \| 0.1839 \| 0.9234 \|
	\| 0.1554 \| 2.6263 \| 2600 \| 0.1447 \| 0.9463 \|
	\| 0.183 \| 2.7273 \| 2700 \| 0.2474 \| 0.8822 \|
	\| 0.0972 \| 2.8283 \| 2800 \| 0.2223 \| 0.9205 \|
	\| 0.1073 \| 2.9293 \| 2900 \| 0.1860 \| 0.9345 \|
	\| 0.1824 \| 3.0303 \| 3000 \| 0.2324 \| 0.9194 \|
	\| 0.1221 \| 3.1313 \| 3100 \| 0.1475 \| 0.9449 \|
	\| 0.1039 \| 3.2323 \| 3200 \| 0.1480 \| 0.9427 \|
	\| 0.276 \| 3.3333 \| 3300 \| 0.1591 \| 0.9402 \|
	\| 0.2498 \| 3.4343 \| 3400 \| 0.2447 \| 0.9098 \|
	\| 0.1453 \| 3.5354 \| 3500 \| 0.1556 \| 0.9416 \|
	\| 0.1794 \| 3.6364 \| 3600 \| 0.2272 \| 0.9083 \|
	\| 0.1467 \| 3.7374 \| 3700 \| 0.1673 \| 0.9413 \|
	\| 0.1372 \| 3.8384 \| 3800 \| 0.1763 \| 0.9341 \|
	\| 0.2283 \| 3.9394 \| 3900 \| 0.1671 \| 0.9373 \|
	\| 0.164 \| 4.0404 \| 4000 \| 0.1490 \| 0.9477 \|
	\| 0.1513 \| 4.1414 \| 4100 \| 0.1547 \| 0.9488 \|
	\| 0.0991 \| 4.2424 \| 4200 \| 0.1536 \| 0.9431 \|
	\| 0.1419 \| 4.3434 \| 4300 \| 0.1568 \| 0.9445 \|
	\| 0.1452 \| 4.4444 \| 4400 \| 0.2328 \| 0.9320 \|
	\| 0.1445 \| 4.5455 \| 4500 \| 0.1351 \| 0.9513 \|
	\| 0.1366 \| 4.6465 \| 4600 \| 0.1571 \| 0.9416 \|
	\| 0.097 \| 4.7475 \| 4700 \| 0.1506 \| 0.9424 \|
	\| 0.0603 \| 4.8485 \| 4800 \| 0.1435 \| 0.9499 \|
	\| 0.1179 \| 4.9495 \| 4900 \| 0.1754 \| 0.9363 \|
	\| 0.1948 \| 5.0505 \| 5000 \| 0.1609 \| 0.9402 \|
	\| 0.1021 \| 5.1515 \| 5100 \| 0.1566 \| 0.9459 \|
	\| 0.0652 \| 5.2525 \| 5200 \| 0.1564 \| 0.9481 \|
	\| 0.1029 \| 5.3535 \| 5300 \| 0.1410 \| 0.9492 \|
	\| 0.1014 \| 5.4545 \| 5400 \| 0.1490 \| 0.9531 \|
	\| 0.1338 \| 5.5556 \| 5500 \| 0.1865 \| 0.9406 \|
	\| 0.0844 \| 5.6566 \| 5600 \| 0.1631 \| 0.9456 \|
	\| 0.1059 \| 5.7576 \| 5700 \| 0.1738 \| 0.9409 \|
	\| 0.0788 \| 5.8586 \| 5800 \| 0.1801 \| 0.9370 \|
	\| 0.0941 \| 5.9596 \| 5900 \| 0.1575 \| 0.9495 \|
	\| 0.112 \| 6.0606 \| 6000 \| 0.1796 \| 0.9470 \|
	\| 0.0691 \| 6.1616 \| 6100 \| 0.1697 \| 0.9499 \|
	\| 0.1385 \| 6.2626 \| 6200 \| 0.1348 \| 0.9563 \|
	\| 0.1173 \| 6.3636 \| 6300 \| 0.1522 \| 0.9502 \|
	\| 0.046 \| 6.4646 \| 6400 \| 0.2114 \| 0.9391 \|
	\| 0.0319 \| 6.5657 \| 6500 \| 0.1723 \| 0.9477 \|
	\| 0.0757 \| 6.6667 \| 6600 \| 0.1561 \| 0.9527 \|
	\| 0.0744 \| 6.7677 \| 6700 \| 0.1587 \| 0.9567 \|
	\| 0.0341 \| 6.8687 \| 6800 \| 0.1458 \| 0.9578 \|
	\| 0.1512 \| 6.9697 \| 6900 \| 0.1572 \| 0.9531 \|
	\| 0.0153 \| 7.0707 \| 7000 \| 0.1402 \| 0.9617 \|
	\| 0.0711 \| 7.1717 \| 7100 \| 0.1527 \| 0.9610 \|
	\| 0.0453 \| 7.2727 \| 7200 \| 0.1512 \| 0.9570 \|
	\| 0.0052 \| 7.3737 \| 7300 \| 0.1936 \| 0.9520 \|
	\| 0.0477 \| 7.4747 \| 7400 \| 0.1699 \| 0.9513 \|
	\| 0.091 \| 7.5758 \| 7500 \| 0.1628 \| 0.9513 \|
	\| 0.063 \| 7.6768 \| 7600 \| 0.1474 \| 0.9578 \|
	\| 0.0497 \| 7.7778 \| 7700 \| 0.1389 \| 0.9613 \|
	\| 0.0552 \| 7.8788 \| 7800 \| 0.2587 \| 0.9381 \|
	\| 0.0364 \| 7.9798 \| 7900 \| 0.1361 \| 0.9603 \|
	\| 0.0124 \| 8.0808 \| 8000 \| 0.1438 \| 0.9606 \|
	\| 0.0703 \| 8.1818 \| 8100 \| 0.1577 \| 0.9585 \|
	\| 0.025 \| 8.2828 \| 8200 \| 0.1943 \| 0.9484 \|
	\| 0.0259 \| 8.3838 \| 8300 \| 0.1590 \| 0.9613 \|
	\| 0.0049 \| 8.4848 \| 8400 \| 0.1521 \| 0.9581 \|
	\| 0.0174 \| 8.5859 \| 8500 \| 0.1522 \| 0.9599 \|
	\| 0.0194 \| 8.6869 \| 8600 \| 0.1456 \| 0.9606 \|
	\| 0.0315 \| 8.7879 \| 8700 \| 0.1411 \| 0.9599 \|
	\| 0.0419 \| 8.8889 \| 8800 \| 0.1426 \| 0.9592 \|
	\| 0.0193 \| 8.9899 \| 8900 \| 0.1375 \| 0.9642 \|
	\| 0.0027 \| 9.0909 \| 9000 \| 0.1379 \| 0.9635 \|
	\| 0.0345 \| 9.1919 \| 9100 \| 0.1444 \| 0.9631 \|
	\| 0.0291 \| 9.2929 \| 9200 \| 0.1492 \| 0.9624 \|
	\| 0.017 \| 9.3939 \| 9300 \| 0.1466 \| 0.9635 \|
	\| 0.0269 \| 9.4949 \| 9400 \| 0.1523 \| 0.9631 \|
	\| 0.003 \| 9.5960 \| 9500 \| 0.1445 \| 0.9628 \|
	\| 0.0471 \| 9.6970 \| 9600 \| 0.1454 \| 0.9617 \|
	\| 0.0356 \| 9.7980 \| 9700 \| 0.1452 \| 0.9620 \|
	\| 0.0034 \| 9.8990 \| 9800 \| 0.1445 \| 0.9624 \|
	\| 0.0162 \| 10.0 \| 9900 \| 0.1451 \| 0.9628 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.4.1+cu121
	- Datasets 3.0.0
	- Tokenizers 0.19.1