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# vit-base-patch16-224-finetuned-algae-wirs

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: 0.9663
- Accuracy: 0.6021

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0733        | 1.0   | 120  | 1.0611          | 0.5781   |
| 1.0243        | 2.0   | 240  | 1.0628          | 0.5663   |
| 0.9852        | 3.0   | 360  | 1.0083          | 0.5845   |
| 0.94          | 4.0   | 480  | 1.0005          | 0.5933   |
| 0.9744        | 5.0   | 600  | 1.0102          | 0.5786   |
| 0.9623        | 6.0   | 720  | 0.9840          | 0.5763   |
| 0.9021        | 7.0   | 840  | 0.9869          | 0.5798   |
| 0.9181        | 8.0   | 960  | 0.9755          | 0.5827   |
| 0.8774        | 9.0   | 1080 | 0.9808          | 0.5798   |
| 0.8294        | 10.0  | 1200 | 0.9663          | 0.6021   |
| 0.8015        | 11.0  | 1320 | 0.9739          | 0.5980   |
| 0.8063        | 12.0  | 1440 | 0.9811          | 0.6009   |
| 0.7857        | 13.0  | 1560 | 0.9833          | 0.5933   |
| 0.7085        | 14.0  | 1680 | 0.9887          | 0.5998   |
| 0.7414        | 15.0  | 1800 | 0.9928          | 0.5974   |
| 0.7442        | 16.0  | 1920 | 0.9963          | 0.5992   |
| 0.7142        | 17.0  | 2040 | 1.0041          | 0.6004   |
| 0.7488        | 18.0  | 2160 | 1.0034          | 0.5962   |
| 0.6731        | 19.0  | 2280 | 1.0055          | 0.6021   |
| 0.6905        | 20.0  | 2400 | 1.0033          | 0.6009   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-finetuned-algae-wirs", "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-algae-wirs
=========================================


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: 0.9663
* Accuracy: 0.6021


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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2