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# vit-base-patch16-224-finetuned-main-gpu-30e-final

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0231
- Accuracy: 0.9940

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5113        | 1.0   | 551   | 0.4745          | 0.7971   |
| 0.3409        | 2.0   | 1102  | 0.2697          | 0.8961   |
| 0.2675        | 3.0   | 1653  | 0.1611          | 0.9381   |
| 0.2092        | 4.0   | 2204  | 0.1176          | 0.9548   |
| 0.2008        | 5.0   | 2755  | 0.0889          | 0.9656   |
| 0.1555        | 6.0   | 3306  | 0.0666          | 0.9759   |
| 0.1614        | 7.0   | 3857  | 0.0576          | 0.9778   |
| 0.1518        | 8.0   | 4408  | 0.0517          | 0.9814   |
| 0.1231        | 9.0   | 4959  | 0.0528          | 0.9812   |
| 0.1076        | 10.0  | 5510  | 0.0426          | 0.9850   |
| 0.0953        | 11.0  | 6061  | 0.0634          | 0.9795   |
| 0.1097        | 12.0  | 6612  | 0.0398          | 0.9860   |
| 0.0763        | 13.0  | 7163  | 0.0348          | 0.9866   |
| 0.0895        | 14.0  | 7714  | 0.0341          | 0.9884   |
| 0.06          | 15.0  | 8265  | 0.0381          | 0.9883   |
| 0.0767        | 16.0  | 8816  | 0.0382          | 0.9875   |
| 0.0868        | 17.0  | 9367  | 0.0309          | 0.9898   |
| 0.091         | 18.0  | 9918  | 0.0339          | 0.9885   |
| 0.0817        | 19.0  | 10469 | 0.0243          | 0.9913   |
| 0.0641        | 20.0  | 11020 | 0.0286          | 0.9906   |
| 0.0703        | 21.0  | 11571 | 0.0314          | 0.9906   |
| 0.0642        | 22.0  | 12122 | 0.0261          | 0.9913   |
| 0.0695        | 23.0  | 12673 | 0.0260          | 0.9920   |
| 0.0664        | 24.0  | 13224 | 0.0241          | 0.9928   |
| 0.0552        | 25.0  | 13775 | 0.0258          | 0.9928   |
| 0.056         | 26.0  | 14326 | 0.0230          | 0.9939   |
| 0.0488        | 27.0  | 14877 | 0.0221          | 0.9936   |
| 0.0389        | 28.0  | 15428 | 0.0225          | 0.9930   |
| 0.0402        | 29.0  | 15979 | 0.0231          | 0.9940   |
| 0.0424        | 30.0  | 16530 | 0.0211          | 0.9939   |


### 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": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-finetuned-main-gpu-30e-final", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9940476190476191, "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 

vit-base-patch16-224-finetuned-main-gpu-30e-final
=================================================


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


* Loss: 0.0231
* Accuracy: 0.9940


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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2