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# turcoins-classifier

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1763
- Accuracy: 0.9549

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9277        | 1.0   | 146  | 1.9660          | 0.7726   |
| 1.6627        | 2.0   | 292  | 1.7154          | 0.7917   |
| 1.4071        | 2.99  | 438  | 1.4120          | 0.8079   |
| 1.09          | 4.0   | 585  | 1.1225          | 0.8362   |
| 0.8086        | 5.0   | 731  | 0.8917          | 0.8675   |
| 0.7636        | 6.0   | 877  | 0.7596          | 0.8709   |
| 0.611         | 6.99  | 1023 | 0.6493          | 0.8883   |
| 0.4605        | 8.0   | 1170 | 0.5899          | 0.8872   |
| 0.37          | 9.0   | 1316 | 0.4978          | 0.9045   |
| 0.3882        | 10.0  | 1462 | 0.4424          | 0.9132   |
| 0.3139        | 10.99 | 1608 | 0.3969          | 0.9115   |
| 0.3178        | 12.0  | 1755 | 0.3525          | 0.9294   |
| 0.2796        | 13.0  | 1901 | 0.3552          | 0.9161   |
| 0.2571        | 14.0  | 2047 | 0.3189          | 0.9265   |
| 0.2481        | 14.99 | 2193 | 0.2945          | 0.9358   |
| 0.1875        | 16.0  | 2340 | 0.2647          | 0.9392   |
| 0.1861        | 17.0  | 2486 | 0.2404          | 0.9410   |
| 0.1839        | 18.0  | 2632 | 0.2556          | 0.9421   |
| 0.173         | 18.99 | 2778 | 0.2387          | 0.9462   |
| 0.1837        | 20.0  | 2925 | 0.2049          | 0.9485   |
| 0.1724        | 21.0  | 3071 | 0.2065          | 0.9525   |
| 0.1399        | 22.0  | 3217 | 0.2089          | 0.9404   |
| 0.1696        | 22.99 | 3363 | 0.1957          | 0.9497   |
| 0.1405        | 24.0  | 3510 | 0.1848          | 0.9554   |
| 0.1009        | 25.0  | 3656 | 0.1912          | 0.9520   |
| 0.1126        | 26.0  | 3802 | 0.1717          | 0.9560   |
| 0.1336        | 26.99 | 3948 | 0.1699          | 0.9589   |
| 0.1046        | 28.0  | 4095 | 0.1600          | 0.9601   |
| 0.126         | 29.0  | 4241 | 0.1839          | 0.9520   |
| 0.0882        | 29.95 | 4380 | 0.1763          | 0.9549   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "turcoins-classifier", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "hsyntemiz--turcoins", "split": "test", "args": "hsyntemiz--turcoins"}, "metrics": [{"type": "accuracy", "value": 0.9548611111111112, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

turcoins-classifier
===================


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


* Loss: 0.1763
* Accuracy: 0.9549


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3