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# vit-base-patch16-224-finetuned-on-all-affectnet_short

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0520
- Accuracy: 0.7259
- Precision: 0.7293
- Recall: 0.7259
- F1: 0.7255

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- 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: 14
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5399        | 1.0   | 91   | 1.4698          | 0.5097   | 0.5079    | 0.5097 | 0.4827 |
| 1.284         | 2.0   | 182  | 1.2026          | 0.6409   | 0.6514    | 0.6409 | 0.6226 |
| 1.2259        | 3.0   | 273  | 1.1367          | 0.6722   | 0.6749    | 0.6722 | 0.6694 |
| 1.1663        | 4.0   | 364  | 1.1086          | 0.6838   | 0.6903    | 0.6838 | 0.6814 |
| 1.1401        | 5.0   | 455  | 1.0782          | 0.7055   | 0.7070    | 0.7055 | 0.7042 |
| 1.1229        | 6.0   | 546  | 1.0734          | 0.7055   | 0.7093    | 0.7055 | 0.7036 |
| 1.0929        | 7.0   | 637  | 1.0674          | 0.7120   | 0.7147    | 0.7120 | 0.7099 |
| 1.0826        | 8.0   | 728  | 1.0601          | 0.7210   | 0.7226    | 0.7210 | 0.7191 |
| 1.0414        | 9.0   | 819  | 1.0558          | 0.7203   | 0.7211    | 0.7203 | 0.7196 |
| 1.0649        | 10.0  | 910  | 1.0499          | 0.7179   | 0.7194    | 0.7179 | 0.7175 |
| 1.0554        | 11.0  | 1001 | 1.0520          | 0.7259   | 0.7293    | 0.7259 | 0.7255 |
| 1.0496        | 12.0  | 1092 | 1.0466          | 0.7210   | 0.7212    | 0.7210 | 0.7204 |
| 1.064         | 13.0  | 1183 | 1.0502          | 0.7220   | 0.7235    | 0.7220 | 0.7211 |
| 1.0386        | 14.0  | 1274 | 1.0475          | 0.7206   | 0.7208    | 0.7206 | 0.7200 |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "vit-base-patch16-224-finetuned-on-all-affectnet_short", "results": []}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

vit-base-patch16-224-finetuned-on-all-affectnet\_short
======================================================


This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0520
* Accuracy: 0.7259
* Precision: 0.7293
* Recall: 0.7259
* F1: 0.7255


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 16
* 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: 14
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 16
* 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: 14
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 16
* 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: 14
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3