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# large-algae-vit-rgb

This model is a fine-tuned version of [samitizerxu/large-algae-vit-rgb](https://huggingface.co/samitizerxu/large-algae-vit-rgb) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1659
- Accuracy: 0.5798

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2115        | 1.0   | 120  | 0.9078          | 0.6315   |
| 1.1249        | 2.0   | 240  | 0.9217          | 0.6320   |
| 1.1385        | 3.0   | 360  | 0.9518          | 0.6180   |
| 1.1347        | 4.0   | 480  | 1.0201          | 0.6068   |
| 1.1358        | 5.0   | 600  | 1.0801          | 0.5892   |
| 1.098         | 6.0   | 720  | 1.0932          | 0.5851   |
| 1.0882        | 7.0   | 840  | 1.0347          | 0.6033   |
| 1.0688        | 8.0   | 960  | 1.0403          | 0.6056   |
| 1.0863        | 9.0   | 1080 | 1.0466          | 0.6009   |
| 1.1253        | 10.0  | 1200 | 1.2308          | 0.5511   |
| 1.0393        | 11.0  | 1320 | 1.1434          | 0.5869   |
| 1.0749        | 12.0  | 1440 | 1.2155          | 0.5622   |
| 1.0433        | 13.0  | 1560 | 1.2466          | 0.5522   |
| 1.0141        | 14.0  | 1680 | 1.1880          | 0.5563   |
| 1.0516        | 15.0  | 1800 | 1.1006          | 0.5992   |
| 1.0696        | 16.0  | 1920 | 1.0971          | 0.5751   |
| 0.9867        | 17.0  | 2040 | 1.1689          | 0.5827   |
| 1.0234        | 18.0  | 2160 | 1.1846          | 0.5751   |
| 1.0364        | 19.0  | 2280 | 1.1480          | 0.5739   |
| 1.0314        | 20.0  | 2400 | 1.0977          | 0.5880   |
| 1.0179        | 21.0  | 2520 | 1.1258          | 0.5851   |
| 1.0584        | 22.0  | 2640 | 1.1569          | 0.5822   |
| 1.0222        | 23.0  | 2760 | 1.1672          | 0.5839   |
| 0.996         | 24.0  | 2880 | 1.1737          | 0.5798   |
| 1.0343        | 25.0  | 3000 | 1.1588          | 0.5792   |
| 0.9854        | 26.0  | 3120 | 1.1758          | 0.5763   |
| 0.9753        | 27.0  | 3240 | 1.1715          | 0.5763   |
| 0.9881        | 28.0  | 3360 | 1.1403          | 0.5839   |
| 1.0057        | 29.0  | 3480 | 1.1765          | 0.5781   |
| 0.9824        | 30.0  | 3600 | 1.1659          | 0.5798   |


### 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": "large-algae-vit-rgb", "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 

large-algae-vit-rgb
===================


This model is a fine-tuned version of samitizerxu/large-algae-vit-rgb on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1659
* Accuracy: 0.5798


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: 30


### 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: 30 ### 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: 30 ### 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: 30### Training results### Framework versions


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