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# finetuned-affecthq

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: 0.8116
- Accuracy: 0.7179
- Precision: 0.7174
- Recall: 0.7179
- F1: 0.7167

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5413        | 1.0   | 174  | 1.4810          | 0.4898   | 0.4867    | 0.4898 | 0.4409 |
| 1.0367        | 2.0   | 348  | 1.0571          | 0.6155   | 0.6172    | 0.6155 | 0.6041 |
| 0.9534        | 3.0   | 522  | 0.9673          | 0.6475   | 0.6476    | 0.6475 | 0.6375 |
| 0.8532        | 4.0   | 696  | 0.9056          | 0.6748   | 0.6710    | 0.6748 | 0.6704 |
| 0.8211        | 5.0   | 870  | 0.8707          | 0.6903   | 0.6912    | 0.6903 | 0.6836 |
| 0.7797        | 6.0   | 1044 | 0.8472          | 0.7050   | 0.7050    | 0.7050 | 0.7019 |
| 0.7816        | 7.0   | 1218 | 0.8298          | 0.7111   | 0.7099    | 0.7111 | 0.7096 |
| 0.7135        | 8.0   | 1392 | 0.8186          | 0.7111   | 0.7116    | 0.7111 | 0.7105 |
| 0.6697        | 9.0   | 1566 | 0.8143          | 0.7140   | 0.7124    | 0.7140 | 0.7126 |
| 0.6765        | 10.0  | 1740 | 0.8116          | 0.7179   | 0.7174    | 0.7179 | 0.7167 |


### 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": "finetuned-affecthq", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7179302910528207, "name": "Accuracy"}, {"type": "precision", "value": 0.7173911115103917, "name": "Precision"}, {"type": "recall", "value": 0.7179302910528207, "name": "Recall"}, {"type": "f1", "value": 0.7166821507529032, "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 

finetuned-affecthq
==================


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: 0.8116
* Accuracy: 0.7179
* Precision: 0.7174
* Recall: 0.7179
* F1: 0.7167


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: 10


### 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: 10 ### 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: 10 ### 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: 10### Training results### Framework versions


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