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# vit-base-txoriaktxori

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 txoriak_txori dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0559
- Accuracy: 0.9864

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.8505        | 0.02  | 100   | 5.8381          | 0.2584   |
| 5.259         | 0.04  | 200   | 5.2556          | 0.4992   |
| 4.6643        | 0.06  | 300   | 4.5950          | 0.6532   |
| 4.0801        | 0.08  | 400   | 3.9534          | 0.6976   |
| 3.3312        | 0.1   | 500   | 3.2908          | 0.7608   |
| 2.773         | 0.12  | 600   | 2.6892          | 0.7704   |
| 2.3108        | 0.14  | 700   | 2.0982          | 0.7976   |
| 1.662         | 0.16  | 800   | 1.6214          | 0.8216   |
| 1.3897        | 0.18  | 900   | 1.2662          | 0.8604   |
| 1.1634        | 0.2   | 1000  | 0.9868          | 0.8892   |
| 1.0498        | 0.22  | 1100  | 0.7855          | 0.8992   |
| 0.5978        | 0.24  | 1200  | 0.6305          | 0.912    |
| 0.6399        | 0.26  | 1300  | 0.5560          | 0.9164   |
| 0.607         | 0.28  | 1400  | 0.5119          | 0.9192   |
| 0.6595        | 0.3   | 1500  | 0.4307          | 0.9272   |
| 0.5239        | 0.32  | 1600  | 0.4124          | 0.9176   |
| 0.5166        | 0.34  | 1700  | 0.3280          | 0.9312   |
| 0.5352        | 0.36  | 1800  | 0.3155          | 0.9308   |
| 0.4036        | 0.38  | 1900  | 0.2893          | 0.9424   |
| 0.3836        | 0.4   | 2000  | 0.3161          | 0.9272   |
| 0.3418        | 0.42  | 2100  | 0.3005          | 0.9384   |
| 0.4172        | 0.44  | 2200  | 0.2518          | 0.9456   |
| 0.4293        | 0.46  | 2300  | 0.2367          | 0.9424   |
| 0.3551        | 0.48  | 2400  | 0.2422          | 0.9432   |
| 0.2718        | 0.5   | 2500  | 0.2207          | 0.9492   |
| 0.3802        | 0.52  | 2600  | 0.2163          | 0.9428   |
| 0.2916        | 0.54  | 2700  | 0.2156          | 0.946    |
| 0.3384        | 0.56  | 2800  | 0.2037          | 0.9508   |
| 0.352         | 0.58  | 2900  | 0.2241          | 0.9432   |
| 0.3868        | 0.6   | 3000  | 0.2525          | 0.9428   |
| 0.3195        | 0.62  | 3100  | 0.2032          | 0.9496   |
| 0.2618        | 0.64  | 3200  | 0.2088          | 0.944    |
| 0.326         | 0.66  | 3300  | 0.1744          | 0.9536   |
| 0.2691        | 0.68  | 3400  | 0.1853          | 0.9516   |
| 0.2629        | 0.7   | 3500  | 0.1788          | 0.9464   |
| 0.2965        | 0.72  | 3600  | 0.1719          | 0.9572   |
| 0.3565        | 0.74  | 3700  | 0.2041          | 0.9452   |
| 0.2344        | 0.76  | 3800  | 0.1863          | 0.9504   |
| 0.4416        | 0.78  | 3900  | 0.1938          | 0.9472   |
| 0.2901        | 0.8   | 4000  | 0.1674          | 0.9572   |
| 0.3158        | 0.82  | 4100  | 0.2006          | 0.9496   |
| 0.3708        | 0.84  | 4200  | 0.1850          | 0.952    |
| 0.2636        | 0.86  | 4300  | 0.1488          | 0.9624   |
| 0.1764        | 0.88  | 4400  | 0.1818          | 0.9524   |
| 0.4299        | 0.9   | 4500  | 0.1642          | 0.9576   |
| 0.4862        | 0.92  | 4600  | 0.1867          | 0.9516   |
| 0.288         | 0.94  | 4700  | 0.1362          | 0.9604   |
| 0.2715        | 0.96  | 4800  | 0.1384          | 0.9668   |
| 0.3139        | 0.98  | 4900  | 0.1607          | 0.956    |
| 0.2301        | 1.0   | 5000  | 0.1428          | 0.9628   |
| 0.1527        | 1.02  | 5100  | 0.1313          | 0.9672   |
| 0.1856        | 1.04  | 5200  | 0.1356          | 0.9628   |
| 0.1143        | 1.06  | 5300  | 0.1469          | 0.962    |
| 0.1465        | 1.08  | 5400  | 0.1320          | 0.9648   |
| 0.1342        | 1.1   | 5500  | 0.1291          | 0.9644   |
| 0.1686        | 1.12  | 5600  | 0.1589          | 0.952    |
| 0.0683        | 1.14  | 5700  | 0.1598          | 0.9592   |
| 0.095         | 1.16  | 5800  | 0.1330          | 0.9628   |
| 0.1458        | 1.18  | 5900  | 0.1307          | 0.9652   |
| 0.2321        | 1.2   | 6000  | 0.1498          | 0.9608   |
| 0.0593        | 1.22  | 6100  | 0.1393          | 0.9636   |
| 0.1721        | 1.24  | 6200  | 0.1564          | 0.9604   |
| 0.2735        | 1.26  | 6300  | 0.1509          | 0.9572   |
| 0.1384        | 1.28  | 6400  | 0.1526          | 0.958    |
| 0.1232        | 1.3   | 6500  | 0.1560          | 0.9596   |
| 0.1615        | 1.32  | 6600  | 0.1348          | 0.9652   |
| 0.2521        | 1.34  | 6700  | 0.1223          | 0.9684   |
| 0.0616        | 1.36  | 6800  | 0.1556          | 0.9616   |
| 0.23          | 1.38  | 6900  | 0.1338          | 0.9652   |
| 0.237         | 1.4   | 7000  | 0.1140          | 0.9664   |
| 0.2572        | 1.42  | 7100  | 0.1191          | 0.9672   |
| 0.1841        | 1.44  | 7200  | 0.1121          | 0.9708   |
| 0.1212        | 1.46  | 7300  | 0.1089          | 0.9708   |
| 0.1436        | 1.48  | 7400  | 0.1246          | 0.9672   |
| 0.1403        | 1.5   | 7500  | 0.1234          | 0.9676   |
| 0.1794        | 1.52  | 7600  | 0.1273          | 0.966    |
| 0.2153        | 1.54  | 7700  | 0.1423          | 0.964    |
| 0.1347        | 1.56  | 7800  | 0.0985          | 0.9708   |
| 0.1989        | 1.58  | 7900  | 0.1117          | 0.9712   |
| 0.2686        | 1.6   | 8000  | 0.1166          | 0.9704   |
| 0.134         | 1.62  | 8100  | 0.1391          | 0.962    |
| 0.2474        | 1.64  | 8200  | 0.1280          | 0.9676   |
| 0.0635        | 1.66  | 8300  | 0.1079          | 0.9696   |
| 0.1073        | 1.68  | 8400  | 0.1335          | 0.9628   |
| 0.1483        | 1.7   | 8500  | 0.1108          | 0.9692   |
| 0.0933        | 1.72  | 8600  | 0.1059          | 0.9708   |
| 0.1204        | 1.74  | 8700  | 0.1007          | 0.9752   |
| 0.1051        | 1.76  | 8800  | 0.1055          | 0.9712   |
| 0.1509        | 1.78  | 8900  | 0.0995          | 0.9704   |
| 0.1404        | 1.8   | 9000  | 0.1012          | 0.9744   |
| 0.0502        | 1.82  | 9100  | 0.0913          | 0.9768   |
| 0.3038        | 1.84  | 9200  | 0.0988          | 0.9732   |
| 0.1651        | 1.86  | 9300  | 0.1146          | 0.9656   |
| 0.1047        | 1.88  | 9400  | 0.1140          | 0.9664   |
| 0.1639        | 1.9   | 9500  | 0.1059          | 0.97     |
| 0.1044        | 1.92  | 9600  | 0.1012          | 0.9744   |
| 0.1955        | 1.94  | 9700  | 0.1119          | 0.9676   |
| 0.1903        | 1.96  | 9800  | 0.1127          | 0.9716   |
| 0.1328        | 1.98  | 9900  | 0.1199          | 0.9628   |
| 0.1219        | 2.0   | 10000 | 0.1011          | 0.972    |
| 0.0514        | 2.02  | 10100 | 0.1040          | 0.9728   |
| 0.0194        | 2.04  | 10200 | 0.0994          | 0.9752   |
| 0.0469        | 2.06  | 10300 | 0.1027          | 0.9716   |
| 0.0417        | 2.08  | 10400 | 0.1045          | 0.9748   |
| 0.0566        | 2.1   | 10500 | 0.0861          | 0.9792   |
| 0.0427        | 2.12  | 10600 | 0.1094          | 0.974    |
| 0.1358        | 2.14  | 10700 | 0.0795          | 0.9776   |
| 0.0119        | 2.16  | 10800 | 0.0972          | 0.9748   |
| 0.0379        | 2.18  | 10900 | 0.1087          | 0.97     |
| 0.0951        | 2.2   | 11000 | 0.1079          | 0.9728   |
| 0.0256        | 2.22  | 11100 | 0.0951          | 0.9748   |
| 0.076         | 2.24  | 11200 | 0.0945          | 0.9764   |
| 0.1004        | 2.26  | 11300 | 0.0870          | 0.9788   |
| 0.0657        | 2.28  | 11400 | 0.1073          | 0.974    |
| 0.0332        | 2.3   | 11500 | 0.0960          | 0.9752   |
| 0.0087        | 2.32  | 11600 | 0.0865          | 0.978    |
| 0.0351        | 2.34  | 11700 | 0.0963          | 0.9736   |
| 0.0127        | 2.36  | 11800 | 0.0989          | 0.976    |
| 0.0447        | 2.38  | 11900 | 0.1038          | 0.9752   |
| 0.023         | 2.4   | 12000 | 0.0919          | 0.9744   |
| 0.0329        | 2.42  | 12100 | 0.0857          | 0.9796   |
| 0.042         | 2.44  | 12200 | 0.0812          | 0.9804   |
| 0.0549        | 2.46  | 12300 | 0.1114          | 0.9732   |
| 0.0806        | 2.48  | 12400 | 0.0971          | 0.9772   |
| 0.1768        | 2.5   | 12500 | 0.0933          | 0.974    |
| 0.059         | 2.52  | 12600 | 0.0943          | 0.9788   |
| 0.0184        | 2.54  | 12700 | 0.0874          | 0.978    |
| 0.021         | 2.56  | 12800 | 0.0903          | 0.9764   |
| 0.0457        | 2.58  | 12900 | 0.0999          | 0.976    |
| 0.0788        | 2.6   | 13000 | 0.0954          | 0.9732   |
| 0.0599        | 2.62  | 13100 | 0.0876          | 0.9752   |
| 0.1041        | 2.64  | 13200 | 0.1017          | 0.9744   |
| 0.0309        | 2.66  | 13300 | 0.0918          | 0.9772   |
| 0.1347        | 2.68  | 13400 | 0.0758          | 0.9792   |
| 0.0432        | 2.7   | 13500 | 0.0790          | 0.9808   |
| 0.0802        | 2.72  | 13600 | 0.0860          | 0.9776   |
| 0.0841        | 2.74  | 13700 | 0.0857          | 0.98     |
| 0.0513        | 2.76  | 13800 | 0.0895          | 0.9764   |
| 0.0129        | 2.78  | 13900 | 0.0861          | 0.9772   |
| 0.1279        | 2.8   | 14000 | 0.0895          | 0.9764   |
| 0.0074        | 2.82  | 14100 | 0.0842          | 0.978    |
| 0.0132        | 2.84  | 14200 | 0.0742          | 0.9796   |
| 0.0974        | 2.86  | 14300 | 0.0854          | 0.9776   |
| 0.0803        | 2.88  | 14400 | 0.0769          | 0.9804   |
| 0.037         | 2.9   | 14500 | 0.0806          | 0.9788   |
| 0.0936        | 2.92  | 14600 | 0.0824          | 0.9812   |
| 0.0064        | 2.94  | 14700 | 0.0748          | 0.9832   |
| 0.0631        | 2.96  | 14800 | 0.0761          | 0.9828   |
| 0.0158        | 2.98  | 14900 | 0.0709          | 0.9848   |
| 0.0433        | 3.0   | 15000 | 0.0704          | 0.9828   |
| 0.0028        | 3.02  | 15100 | 0.0712          | 0.9824   |
| 0.0031        | 3.04  | 15200 | 0.0717          | 0.9808   |
| 0.0191        | 3.06  | 15300 | 0.0716          | 0.9828   |
| 0.0051        | 3.08  | 15400 | 0.0708          | 0.9832   |
| 0.0205        | 3.1   | 15500 | 0.0686          | 0.9828   |
| 0.1147        | 3.12  | 15600 | 0.0670          | 0.984    |
| 0.0014        | 3.14  | 15700 | 0.0628          | 0.9848   |
| 0.0082        | 3.16  | 15800 | 0.0659          | 0.984    |
| 0.0149        | 3.18  | 15900 | 0.0672          | 0.9836   |
| 0.0056        | 3.2   | 16000 | 0.0676          | 0.9852   |
| 0.0059        | 3.22  | 16100 | 0.0706          | 0.9836   |
| 0.0198        | 3.24  | 16200 | 0.0725          | 0.9812   |
| 0.0019        | 3.26  | 16300 | 0.0681          | 0.9828   |
| 0.0013        | 3.28  | 16400 | 0.0681          | 0.9856   |
| 0.0663        | 3.3   | 16500 | 0.0704          | 0.9852   |
| 0.0024        | 3.32  | 16600 | 0.0697          | 0.984    |
| 0.0081        | 3.34  | 16700 | 0.0679          | 0.9852   |
| 0.0264        | 3.36  | 16800 | 0.0631          | 0.9872   |
| 0.0061        | 3.38  | 16900 | 0.0651          | 0.9848   |
| 0.0169        | 3.4   | 17000 | 0.0655          | 0.9828   |
| 0.0013        | 3.42  | 17100 | 0.0661          | 0.9836   |
| 0.0072        | 3.44  | 17200 | 0.0633          | 0.9848   |
| 0.009         | 3.46  | 17300 | 0.0634          | 0.9848   |
| 0.0028        | 3.48  | 17400 | 0.0634          | 0.9844   |
| 0.0024        | 3.5   | 17500 | 0.0637          | 0.9836   |
| 0.0031        | 3.52  | 17600 | 0.0641          | 0.9848   |
| 0.004         | 3.54  | 17700 | 0.0619          | 0.9856   |
| 0.0562        | 3.56  | 17800 | 0.0673          | 0.9856   |
| 0.0005        | 3.58  | 17900 | 0.0644          | 0.9864   |
| 0.0079        | 3.6   | 18000 | 0.0647          | 0.9872   |
| 0.0016        | 3.62  | 18100 | 0.0617          | 0.9872   |
| 0.0019        | 3.64  | 18200 | 0.0636          | 0.9872   |
| 0.0047        | 3.66  | 18300 | 0.0608          | 0.9848   |
| 0.0327        | 3.68  | 18400 | 0.0586          | 0.9868   |
| 0.0108        | 3.7   | 18500 | 0.0594          | 0.9872   |
| 0.0061        | 3.72  | 18600 | 0.0597          | 0.9868   |
| 0.0106        | 3.74  | 18700 | 0.0579          | 0.9872   |
| 0.001         | 3.76  | 18800 | 0.0564          | 0.9872   |
| 0.012         | 3.78  | 18900 | 0.0561          | 0.9876   |
| 0.0038        | 3.8   | 19000 | 0.0566          | 0.9868   |
| 0.0099        | 3.82  | 19100 | 0.0573          | 0.9864   |
| 0.0026        | 3.84  | 19200 | 0.0575          | 0.9864   |
| 0.0062        | 3.86  | 19300 | 0.0573          | 0.9872   |
| 0.0239        | 3.88  | 19400 | 0.0573          | 0.9864   |
| 0.0026        | 3.9   | 19500 | 0.0568          | 0.9868   |
| 0.0014        | 3.92  | 19600 | 0.0557          | 0.9868   |
| 0.0019        | 3.94  | 19700 | 0.0562          | 0.9864   |
| 0.0484        | 3.96  | 19800 | 0.0560          | 0.9864   |
| 0.0022        | 3.98  | 19900 | 0.0559          | 0.9864   |
| 0.0145        | 4.0   | 20000 | 0.0559          | 0.9864   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["txoriak_txori"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-txoriaktxori", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "txoriak_txori", "type": "txoriak_txori", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9864, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:txoriak_txori license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-txoriak_txori #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-base-txoriaktxori
=====================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the txoriak\_txori dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0559
* Accuracy: 0.9864


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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-txoriak_txori #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-txoriak_txori #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2