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# vit-transfer-learning

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5438
- Accuracy: 0.8417

## 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: 0.0001
- 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: 150

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7496        | 1.0   | 210   | 1.7976          | 0.3472   |
| 1.5383        | 2.0   | 420   | 1.6204          | 0.4208   |
| 1.4952        | 3.0   | 630   | 1.5130          | 0.4792   |
| 1.4119        | 4.0   | 840   | 1.4325          | 0.5069   |
| 1.2574        | 5.0   | 1050  | 1.3759          | 0.5083   |
| 1.22          | 6.0   | 1260  | 1.3241          | 0.5389   |
| 1.2337        | 7.0   | 1470  | 1.2850          | 0.5361   |
| 1.0326        | 8.0   | 1680  | 1.2407          | 0.5486   |
| 1.0324        | 9.0   | 1890  | 1.2108          | 0.5639   |
| 1.0585        | 10.0  | 2100  | 1.1799          | 0.5708   |
| 1.0901        | 11.0  | 2310  | 1.1469          | 0.5847   |
| 0.9732        | 12.0  | 2520  | 1.1306          | 0.5972   |
| 0.9296        | 13.0  | 2730  | 1.1049          | 0.6111   |
| 0.9816        | 14.0  | 2940  | 1.0782          | 0.6208   |
| 1.0264        | 15.0  | 3150  | 1.0600          | 0.6264   |
| 0.8306        | 16.0  | 3360  | 1.0358          | 0.6375   |
| 0.8897        | 17.0  | 3570  | 1.0187          | 0.6375   |
| 0.8084        | 18.0  | 3780  | 1.0035          | 0.6472   |
| 0.8002        | 19.0  | 3990  | 0.9875          | 0.65     |
| 0.8268        | 20.0  | 4200  | 0.9731          | 0.6556   |
| 0.7675        | 21.0  | 4410  | 0.9556          | 0.6667   |
| 0.771         | 22.0  | 4620  | 0.9414          | 0.6861   |
| 0.8116        | 23.0  | 4830  | 0.9237          | 0.6917   |
| 0.7656        | 24.0  | 5040  | 0.9177          | 0.675    |
| 0.7831        | 25.0  | 5250  | 0.9004          | 0.6903   |
| 0.6994        | 26.0  | 5460  | 0.8887          | 0.7014   |
| 0.7049        | 27.0  | 5670  | 0.8797          | 0.7014   |
| 0.7354        | 28.0  | 5880  | 0.8668          | 0.7125   |
| 0.6543        | 29.0  | 6090  | 0.8590          | 0.7083   |
| 0.6978        | 30.0  | 6300  | 0.8484          | 0.7125   |
| 0.6395        | 31.0  | 6510  | 0.8381          | 0.7125   |
| 0.7869        | 32.0  | 6720  | 0.8304          | 0.7194   |
| 0.5881        | 33.0  | 6930  | 0.8210          | 0.7236   |
| 0.6378        | 34.0  | 7140  | 0.8087          | 0.7333   |
| 0.6627        | 35.0  | 7350  | 0.8033          | 0.7333   |
| 0.5914        | 36.0  | 7560  | 0.7930          | 0.7278   |
| 0.6632        | 37.0  | 7770  | 0.7855          | 0.7292   |
| 0.7052        | 38.0  | 7980  | 0.7778          | 0.7375   |
| 0.587         | 39.0  | 8190  | 0.7701          | 0.7319   |
| 0.6044        | 40.0  | 8400  | 0.7633          | 0.7444   |
| 0.5306        | 41.0  | 8610  | 0.7567          | 0.7472   |
| 0.5634        | 42.0  | 8820  | 0.7522          | 0.7472   |
| 0.588         | 43.0  | 9030  | 0.7456          | 0.7528   |
| 0.5567        | 44.0  | 9240  | 0.7401          | 0.7597   |
| 0.5823        | 45.0  | 9450  | 0.7283          | 0.7583   |
| 0.5638        | 46.0  | 9660  | 0.7241          | 0.7653   |
| 0.5413        | 47.0  | 9870  | 0.7210          | 0.7583   |
| 0.4506        | 48.0  | 10080 | 0.7134          | 0.7653   |
| 0.5293        | 49.0  | 10290 | 0.7083          | 0.7667   |
| 0.454         | 50.0  | 10500 | 0.7047          | 0.7708   |
| 0.5858        | 51.0  | 10710 | 0.6984          | 0.7778   |
| 0.5209        | 52.0  | 10920 | 0.6933          | 0.7736   |
| 0.5704        | 53.0  | 11130 | 0.6865          | 0.7792   |
| 0.4993        | 54.0  | 11340 | 0.6835          | 0.7778   |
| 0.4677        | 55.0  | 11550 | 0.6787          | 0.7847   |
| 0.4808        | 56.0  | 11760 | 0.6746          | 0.7792   |
| 0.4543        | 57.0  | 11970 | 0.6716          | 0.7889   |
| 0.4304        | 58.0  | 12180 | 0.6645          | 0.7792   |
| 0.474         | 59.0  | 12390 | 0.6591          | 0.7917   |
| 0.444         | 60.0  | 12600 | 0.6544          | 0.7875   |
| 0.4743        | 61.0  | 12810 | 0.6526          | 0.7903   |
| 0.5157        | 62.0  | 13020 | 0.6480          | 0.7972   |
| 0.5253        | 63.0  | 13230 | 0.6451          | 0.7903   |
| 0.4351        | 64.0  | 13440 | 0.6419          | 0.7958   |
| 0.3988        | 65.0  | 13650 | 0.6382          | 0.7944   |
| 0.4919        | 66.0  | 13860 | 0.6319          | 0.8028   |
| 0.4085        | 67.0  | 14070 | 0.6314          | 0.8042   |
| 0.4562        | 68.0  | 14280 | 0.6274          | 0.8056   |
| 0.4822        | 69.0  | 14490 | 0.6223          | 0.8028   |
| 0.4173        | 70.0  | 14700 | 0.6213          | 0.8042   |
| 0.4775        | 71.0  | 14910 | 0.6181          | 0.8097   |
| 0.4069        | 72.0  | 15120 | 0.6156          | 0.8083   |
| 0.429         | 73.0  | 15330 | 0.6112          | 0.8097   |
| 0.4165        | 74.0  | 15540 | 0.6093          | 0.8111   |
| 0.452         | 75.0  | 15750 | 0.6048          | 0.8139   |
| 0.3727        | 76.0  | 15960 | 0.6018          | 0.8139   |
| 0.4692        | 77.0  | 16170 | 0.6004          | 0.8208   |
| 0.4075        | 78.0  | 16380 | 0.5964          | 0.8153   |
| 0.4102        | 79.0  | 16590 | 0.5944          | 0.8167   |
| 0.4043        | 80.0  | 16800 | 0.5937          | 0.8208   |
| 0.3813        | 81.0  | 17010 | 0.5891          | 0.8167   |
| 0.4022        | 82.0  | 17220 | 0.5859          | 0.8194   |
| 0.423         | 83.0  | 17430 | 0.5846          | 0.8222   |
| 0.4503        | 84.0  | 17640 | 0.5831          | 0.8222   |
| 0.3758        | 85.0  | 17850 | 0.5812          | 0.8292   |
| 0.3469        | 86.0  | 18060 | 0.5796          | 0.8222   |
| 0.3344        | 87.0  | 18270 | 0.5764          | 0.8236   |
| 0.3928        | 88.0  | 18480 | 0.5742          | 0.8333   |
| 0.4039        | 89.0  | 18690 | 0.5726          | 0.8292   |
| 0.3589        | 90.0  | 18900 | 0.5702          | 0.8333   |
| 0.3135        | 91.0  | 19110 | 0.5676          | 0.8306   |
| 0.3588        | 92.0  | 19320 | 0.5663          | 0.8306   |
| 0.3851        | 93.0  | 19530 | 0.5641          | 0.8333   |
| 0.3564        | 94.0  | 19740 | 0.5629          | 0.8292   |
| 0.3446        | 95.0  | 19950 | 0.5598          | 0.8375   |
| 0.3586        | 96.0  | 20160 | 0.5583          | 0.8361   |
| 0.3362        | 97.0  | 20370 | 0.5560          | 0.8347   |
| 0.4145        | 98.0  | 20580 | 0.5564          | 0.8361   |
| 0.3158        | 99.0  | 20790 | 0.5539          | 0.8361   |
| 0.4119        | 100.0 | 21000 | 0.5520          | 0.8375   |
| 0.3541        | 101.0 | 21210 | 0.5507          | 0.8389   |
| 0.4606        | 102.0 | 21420 | 0.5491          | 0.8389   |
| 0.3529        | 103.0 | 21630 | 0.5479          | 0.8375   |
| 0.3412        | 104.0 | 21840 | 0.5467          | 0.8347   |
| 0.3532        | 105.0 | 22050 | 0.5460          | 0.8361   |
| 0.39          | 106.0 | 22260 | 0.5439          | 0.8375   |
| 0.3764        | 107.0 | 22470 | 0.5429          | 0.8389   |
| 0.3163        | 108.0 | 22680 | 0.5411          | 0.8389   |
| 0.3287        | 109.0 | 22890 | 0.5398          | 0.8389   |
| 0.3249        | 110.0 | 23100 | 0.5384          | 0.8403   |
| 0.3339        | 111.0 | 23310 | 0.5372          | 0.8389   |
| 0.3073        | 112.0 | 23520 | 0.5361          | 0.8417   |
| 0.336         | 113.0 | 23730 | 0.5352          | 0.8389   |
| 0.2888        | 114.0 | 23940 | 0.5335          | 0.8403   |
| 0.2934        | 115.0 | 24150 | 0.5326          | 0.8431   |
| 0.3194        | 116.0 | 24360 | 0.5325          | 0.8403   |
| 0.3247        | 117.0 | 24570 | 0.5312          | 0.8403   |
| 0.3566        | 118.0 | 24780 | 0.5302          | 0.8403   |
| 0.3298        | 119.0 | 24990 | 0.5292          | 0.8417   |
| 0.3224        | 120.0 | 25200 | 0.5280          | 0.8431   |
| 0.3841        | 121.0 | 25410 | 0.5277          | 0.8403   |
| 0.2983        | 122.0 | 25620 | 0.5273          | 0.8417   |
| 0.3206        | 123.0 | 25830 | 0.5268          | 0.8417   |
| 0.3243        | 124.0 | 26040 | 0.5262          | 0.8403   |
| 0.3075        | 125.0 | 26250 | 0.5249          | 0.8403   |
| 0.3079        | 126.0 | 26460 | 0.5238          | 0.8417   |
| 0.3295        | 127.0 | 26670 | 0.5235          | 0.8431   |
| 0.3084        | 128.0 | 26880 | 0.5233          | 0.8403   |
| 0.3289        | 129.0 | 27090 | 0.5224          | 0.8444   |
| 0.3489        | 130.0 | 27300 | 0.5225          | 0.8417   |
| 0.3362        | 131.0 | 27510 | 0.5215          | 0.8444   |
| 0.3187        | 132.0 | 27720 | 0.5209          | 0.8431   |
| 0.332         | 133.0 | 27930 | 0.5204          | 0.8431   |
| 0.3196        | 134.0 | 28140 | 0.5201          | 0.8417   |
| 0.2873        | 135.0 | 28350 | 0.5194          | 0.8431   |
| 0.3391        | 136.0 | 28560 | 0.5191          | 0.8444   |
| 0.3006        | 137.0 | 28770 | 0.5184          | 0.8444   |
| 0.2912        | 138.0 | 28980 | 0.5182          | 0.8431   |
| 0.3421        | 139.0 | 29190 | 0.5180          | 0.8431   |
| 0.2924        | 140.0 | 29400 | 0.5178          | 0.8417   |
| 0.2989        | 141.0 | 29610 | 0.5177          | 0.8431   |
| 0.3207        | 142.0 | 29820 | 0.5175          | 0.8431   |
| 0.3041        | 143.0 | 30030 | 0.5172          | 0.8444   |
| 0.3468        | 144.0 | 30240 | 0.5169          | 0.8431   |
| 0.3571        | 145.0 | 30450 | 0.5170          | 0.8444   |
| 0.284         | 146.0 | 30660 | 0.5168          | 0.8444   |
| 0.294         | 147.0 | 30870 | 0.5168          | 0.8444   |
| 0.3104        | 148.0 | 31080 | 0.5167          | 0.8444   |
| 0.3278        | 149.0 | 31290 | 0.5166          | 0.8444   |
| 0.3037        | 150.0 | 31500 | 0.5166          | 0.8444   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

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