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# hq_fer2013

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8438
- Accuracy: 0.7022
- Precision: 0.7039
- Recall: 0.7022
- F1: 0.7022

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3081        | 1.0   | 398  | 1.3132          | 0.5555   | 0.5079    | 0.5555 | 0.5137 |
| 0.991         | 2.0   | 796  | 1.0141          | 0.6332   | 0.6356    | 0.6332 | 0.6153 |
| 0.9099        | 3.0   | 1194 | 0.9257          | 0.6682   | 0.6677    | 0.6682 | 0.6631 |
| 0.8306        | 4.0   | 1592 | 0.8832          | 0.6765   | 0.6838    | 0.6765 | 0.6747 |
| 0.7755        | 5.0   | 1990 | 0.8583          | 0.6892   | 0.6896    | 0.6892 | 0.6876 |
| 0.7129        | 6.0   | 2388 | 0.8442          | 0.6931   | 0.6951    | 0.6931 | 0.6922 |
| 0.6549        | 7.0   | 2786 | 0.8494          | 0.6952   | 0.7054    | 0.6952 | 0.6978 |
| 0.6246        | 8.0   | 3184 | 0.8394          | 0.6963   | 0.7023    | 0.6963 | 0.6977 |
| 0.6138        | 9.0   | 3582 | 0.8421          | 0.6996   | 0.7080    | 0.6996 | 0.7013 |
| 0.5824        | 10.0  | 3980 | 0.8438          | 0.7022   | 0.7039    | 0.7022 | 0.7022 |
| 0.5517        | 11.0  | 4378 | 0.8497          | 0.7002   | 0.7034    | 0.7002 | 0.7005 |
| 0.5154        | 12.0  | 4776 | 0.8508          | 0.7021   | 0.7030    | 0.7021 | 0.7018 |
| 0.5318        | 13.0  | 5174 | 0.8540          | 0.7010   | 0.7029    | 0.7010 | 0.7013 |


### 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": ["imagefolder"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "hq_fer2013", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7022445455972375, "name": "Accuracy"}, {"type": "precision", "value": 0.7038651811268685, "name": "Precision"}, {"type": "recall", "value": 0.7022445455972375, "name": "Recall"}, {"type": "f1", "value": 0.702185081437324, "name": "F1"}]}]}]}
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 

hq\_fer2013
===========


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


* Loss: 0.8438
* Accuracy: 0.7022
* Precision: 0.7039
* Recall: 0.7022
* F1: 0.7022


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-imagefolder #license-apache-2.0 #model-index #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-imagefolder #license-apache-2.0 #model-index #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