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# 20E-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: 0.8271
- Accuracy: 0.7188
- Precision: 0.7220
- Recall: 0.7188
- F1: 0.7190

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.9149        | 1.0   | 194  | 1.8887          | 0.3750   | 0.3413    | 0.3750 | 0.3045 |
| 1.2903        | 2.0   | 388  | 1.2485          | 0.5792   | 0.5726    | 0.5792 | 0.5526 |
| 1.071         | 3.0   | 582  | 1.0587          | 0.6321   | 0.6258    | 0.6321 | 0.6228 |
| 1.0185        | 4.0   | 776  | 0.9817          | 0.6617   | 0.6584    | 0.6617 | 0.6553 |
| 0.894         | 5.0   | 970  | 0.9293          | 0.6869   | 0.6872    | 0.6869 | 0.6820 |
| 0.8283        | 6.0   | 1164 | 0.8881          | 0.6936   | 0.6929    | 0.6936 | 0.6905 |
| 0.8185        | 7.0   | 1358 | 0.8659          | 0.6982   | 0.7011    | 0.6982 | 0.6988 |
| 0.7499        | 8.0   | 1552 | 0.8558          | 0.7046   | 0.7050    | 0.7046 | 0.7021 |
| 0.7219        | 9.0   | 1746 | 0.8399          | 0.7124   | 0.7165    | 0.7124 | 0.7127 |
| 0.7382        | 10.0  | 1940 | 0.8300          | 0.7159   | 0.7184    | 0.7159 | 0.7145 |
| 0.6392        | 11.0  | 2134 | 0.8329          | 0.7088   | 0.7135    | 0.7088 | 0.7095 |
| 0.6549        | 12.0  | 2328 | 0.8297          | 0.7133   | 0.7135    | 0.7133 | 0.7120 |
| 0.6762        | 13.0  | 2522 | 0.8180          | 0.7156   | 0.7162    | 0.7156 | 0.7153 |
| 0.5937        | 14.0  | 2716 | 0.8271          | 0.7188   | 0.7220    | 0.7188 | 0.7190 |
| 0.569         | 15.0  | 2910 | 0.8245          | 0.7178   | 0.7175    | 0.7178 | 0.7165 |
| 0.5623        | 16.0  | 3104 | 0.8228          | 0.7165   | 0.7153    | 0.7165 | 0.7157 |
| 0.5291        | 17.0  | 3298 | 0.8238          | 0.7162   | 0.7165    | 0.7162 | 0.7156 |
| 0.5775        | 18.0  | 3492 | 0.8246          | 0.7153   | 0.7162    | 0.7153 | 0.7151 |
| 0.545         | 19.0  | 3686 | 0.8257          | 0.7178   | 0.7192    | 0.7178 | 0.7174 |
| 0.5409        | 20.0  | 3880 | 0.8245          | 0.7178   | 0.7187    | 0.7178 | 0.7177 |


### 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": "20E-affecthq", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7188003869719445, "name": "Accuracy"}, {"type": "precision", "value": 0.7219837313936599, "name": "Precision"}, {"type": "recall", "value": 0.7188003869719445, "name": "Recall"}, {"type": "f1", "value": 0.718989971086903, "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 

20E-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: 0.8271
* Accuracy: 0.7188
* Precision: 0.7220
* Recall: 0.7188
* F1: 0.7190


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: 20


### 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: 20 ### 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: 20 ### 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: 20### Training results### Framework versions


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