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# google-vit-base-patch16-224-cartoon-face-recognition

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Accuracy: 0.9005
- Precision: 0.9066
- Recall: 0.9005
- F1: 0.8984

## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.89  | 6    | 0.5459          | 0.8611   | 0.8683    | 0.8611 | 0.8577 |
| 0.0812        | 1.89  | 12   | 0.4703          | 0.8796   | 0.8833    | 0.8796 | 0.8764 |
| 0.0812        | 2.89  | 18   | 0.4430          | 0.8935   | 0.8969    | 0.8935 | 0.8906 |
| 0.0307        | 3.89  | 24   | 0.4045          | 0.8819   | 0.8849    | 0.8819 | 0.8767 |
| 0.0091        | 4.89  | 30   | 0.3672          | 0.9005   | 0.9025    | 0.9005 | 0.8980 |
| 0.0091        | 5.89  | 36   | 0.3841          | 0.9028   | 0.9125    | 0.9028 | 0.9011 |
| 0.0043        | 6.89  | 42   | 0.3926          | 0.9005   | 0.9073    | 0.9005 | 0.8972 |
| 0.0043        | 7.89  | 48   | 0.3786          | 0.8958   | 0.9005    | 0.8958 | 0.8931 |
| 0.0031        | 8.89  | 54   | 0.3791          | 0.9028   | 0.9091    | 0.9028 | 0.9007 |
| 0.002         | 9.89  | 60   | 0.3677          | 0.9028   | 0.9106    | 0.9028 | 0.9001 |
| 0.002         | 10.89 | 66   | 0.3740          | 0.9028   | 0.9099    | 0.9028 | 0.9007 |
| 0.0027        | 11.89 | 72   | 0.3869          | 0.8981   | 0.9043    | 0.8981 | 0.8956 |
| 0.0027        | 12.89 | 78   | 0.3801          | 0.8981   | 0.9021    | 0.8981 | 0.8954 |
| 0.004         | 13.89 | 84   | 0.3674          | 0.9051   | 0.9113    | 0.9051 | 0.9028 |
| 0.0024        | 14.89 | 90   | 0.3620          | 0.9051   | 0.9096    | 0.9051 | 0.9027 |
| 0.0024        | 15.89 | 96   | 0.3670          | 0.9028   | 0.9089    | 0.9028 | 0.9006 |
| 0.0021        | 16.89 | 102  | 0.3827          | 0.9005   | 0.9065    | 0.9005 | 0.8980 |
| 0.0021        | 17.89 | 108  | 0.3748          | 0.8981   | 0.9049    | 0.8981 | 0.8958 |
| 0.0022        | 18.89 | 114  | 0.3825          | 0.9028   | 0.9101    | 0.9028 | 0.9006 |
| 0.0019        | 19.89 | 120  | 0.3707          | 0.9005   | 0.9066    | 0.9005 | 0.8984 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "google-vit-base-patch16-224-cartoon-face-recognition", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9004629629629629, "name": "Accuracy"}, {"type": "precision", "value": 0.9066341895316832, "name": "Precision"}, {"type": "recall", "value": 0.9004629629629629, "name": "Recall"}, {"type": "f1", "value": 0.8984296743444529, "name": "F1"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

google-vit-base-patch16-224-cartoon-face-recognition
====================================================


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


* Loss: 0.3707
* Accuracy: 0.9005
* Precision: 0.9066
* Recall: 0.9005
* F1: 0.8984


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: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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: 20


### Training results



### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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: 20 ### Training results ### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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: 20 ### Training results ### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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: 20### Training results### Framework versions


* Transformers 4.24.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.6.1
* Tokenizers 0.13.1