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# google-vit-base-patch16-224-face

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: 1.4531
- Accuracy: 0.7249
- Precision: 0.7172
- Recall: 0.7249
- F1: 0.7196

## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8514        | 1.0   | 290  | 0.8464          | 0.7048   | 0.7035    | 0.7048 | 0.6909 |
| 0.7202        | 2.0   | 580  | 0.7791          | 0.7283   | 0.7297    | 0.7283 | 0.7111 |
| 0.5455        | 3.0   | 870  | 0.7950          | 0.7285   | 0.7174    | 0.7285 | 0.7171 |
| 0.334         | 4.0   | 1160 | 0.8948          | 0.7155   | 0.7152    | 0.7155 | 0.7145 |
| 0.1644        | 5.0   | 1450 | 1.0820          | 0.7239   | 0.7189    | 0.7239 | 0.7194 |
| 0.0482        | 6.0   | 1740 | 1.2792          | 0.7204   | 0.7144    | 0.7204 | 0.7160 |
| 0.0236        | 7.0   | 2030 | 1.4162          | 0.7279   | 0.7195    | 0.7279 | 0.7209 |
| 0.0049        | 8.0   | 2320 | 1.4531          | 0.7249   | 0.7172    | 0.7249 | 0.7196 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "google-vit-base-patch16-224-face", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7248574809078198, "name": "Accuracy"}, {"type": "precision", "value": 0.717172031675939, "name": "Precision"}, {"type": "recall", "value": 0.7248574809078198, "name": "Recall"}, {"type": "f1", "value": 0.7195690317790054, "name": "F1"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

google-vit-base-patch16-224-face
================================


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: 1.4531
* Accuracy: 0.7249
* Precision: 0.7172
* Recall: 0.7249
* F1: 0.7196


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: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1