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# 13E-affecthq-fer-balanced

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on Piro17/balancednumber-affecthqnet-fer2013 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0526
- Accuracy: 0.6225
- Precision: 0.6161
- Recall: 0.6225
- F1: 0.6167

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 17
- 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: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7863        | 1.0   | 133  | 1.7632          | 0.4005   | 0.3617    | 0.4005 | 0.3058 |
| 1.3653        | 2.0   | 266  | 1.3630          | 0.5049   | 0.4838    | 0.5049 | 0.4445 |
| 1.2468        | 3.0   | 399  | 1.2475          | 0.5466   | 0.5451    | 0.5466 | 0.5115 |
| 1.1527        | 4.0   | 532  | 1.1865          | 0.5761   | 0.5612    | 0.5761 | 0.5580 |
| 1.0862        | 5.0   | 665  | 1.1448          | 0.5785   | 0.5687    | 0.5785 | 0.5659 |
| 1.064         | 6.0   | 798  | 1.1108          | 0.5972   | 0.5867    | 0.5972 | 0.5853 |
| 1.0037        | 7.0   | 931  | 1.0969          | 0.6019   | 0.5968    | 0.6019 | 0.5946 |
| 0.9533        | 8.0   | 1064 | 1.0764          | 0.6126   | 0.6034    | 0.6126 | 0.6046 |
| 0.9063        | 9.0   | 1197 | 1.0711          | 0.6155   | 0.6035    | 0.6155 | 0.6047 |
| 0.8666        | 10.0  | 1330 | 1.0589          | 0.6173   | 0.6107    | 0.6173 | 0.6108 |
| 0.8364        | 11.0  | 1463 | 1.0556          | 0.6178   | 0.6110    | 0.6178 | 0.6108 |
| 0.8659        | 12.0  | 1596 | 1.0521          | 0.6197   | 0.6141    | 0.6197 | 0.6151 |
| 0.8383        | 13.0  | 1729 | 1.0526          | 0.6225   | 0.6161    | 0.6225 | 0.6167 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Piro17/balancednumber-affecthqnet-fer2013"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "13E-affecthq-fer-balanced", "results": []}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:Piro17/balancednumber-affecthqnet-fer2013 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-Piro17/balancednumber-affecthqnet-fer2013 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

13E-affecthq-fer-balanced
=========================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on Piro17/balancednumber-affecthqnet-fer2013 dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0526
* Accuracy: 0.6225
* Precision: 0.6161
* Recall: 0.6225
* F1: 0.6167


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: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 17
* 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: 13


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 17
* 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: 13 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-Piro17/balancednumber-affecthqnet-fer2013 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 17
* 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: 13 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-Piro17/balancednumber-affecthqnet-fer2013 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 17
* 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: 13### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2