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# ViTFineTuned

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 KTH-TIPS2-b dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0075
- Accuracy: 1.0

## Model description

Transfer learning by fine tuning the Vision Transformer by Google on KTP-TIP2-b dataset.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2859        | 0.99  | 67   | 0.2180          | 0.9784   |
| 0.293         | 1.99  | 134  | 0.3308          | 0.9185   |
| 0.1444        | 2.99  | 201  | 0.1532          | 0.9568   |
| 0.0833        | 3.99  | 268  | 0.0515          | 0.9856   |
| 0.1007        | 4.99  | 335  | 0.0295          | 0.9904   |
| 0.0372        | 5.99  | 402  | 0.0574          | 0.9808   |
| 0.0919        | 6.99  | 469  | 0.0537          | 0.9880   |
| 0.0135        | 7.99  | 536  | 0.0117          | 0.9952   |
| 0.0472        | 8.99  | 603  | 0.0075          | 1.0      |
| 0.0151        | 9.99  | 670  | 0.0048          | 1.0      |
| 0.0052        | 10.99 | 737  | 0.0073          | 0.9976   |
| 0.0109        | 11.99 | 804  | 0.0198          | 0.9952   |
| 0.0033        | 12.99 | 871  | 0.0066          | 0.9976   |
| 0.011         | 13.99 | 938  | 0.0067          | 0.9976   |
| 0.0032        | 14.99 | 1005 | 0.0060          | 0.9976   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "ViTFineTuned", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "KTH-TIPS2-b", "type": "images", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1.0, "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 

ViTFineTuned
============


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the KTH-TIPS2-b dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0075
* Accuracy: 1.0


Model description
-----------------


Transfer learning by fine tuning the Vision Transformer by Google on KTP-TIP2-b dataset.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 15


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 15 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
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: 0.0005
* 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: 15 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: 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: 0.0005
* 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: 15### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1