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# large-algae-vit-wirs

This model is a fine-tuned version of [samitizerxu/large-algae-vit-wirs](https://huggingface.co/samitizerxu/large-algae-vit-wirs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9128
- Accuracy: 0.6209

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1662        | 1.0   | 120  | 0.9128          | 0.6209   |
| 1.0885        | 2.0   | 240  | 0.9469          | 0.6138   |
| 1.1315        | 3.0   | 360  | 1.0919          | 0.5757   |
| 1.0542        | 4.0   | 480  | 1.2291          | 0.5599   |
| 1.028         | 5.0   | 600  | 1.1931          | 0.5599   |
| 1.0023        | 6.0   | 720  | 1.1548          | 0.5675   |
| 1.0176        | 7.0   | 840  | 1.0932          | 0.5757   |
| 0.992         | 8.0   | 960  | 1.1387          | 0.5751   |
| 0.9891        | 9.0   | 1080 | 1.2387          | 0.5464   |
| 0.9635        | 10.0  | 1200 | 1.3772          | 0.5428   |
| 0.9764        | 11.0  | 1320 | 1.4329          | 0.5258   |
| 0.9375        | 12.0  | 1440 | 1.2830          | 0.5522   |
| 0.9574        | 13.0  | 1560 | 1.4003          | 0.5229   |
| 0.9907        | 14.0  | 1680 | 1.3447          | 0.5423   |
| 0.9507        | 15.0  | 1800 | 1.2907          | 0.5604   |
| 0.9866        | 16.0  | 1920 | 1.4578          | 0.5393   |
| 0.9297        | 17.0  | 2040 | 1.4779          | 0.5282   |
| 0.9385        | 18.0  | 2160 | 1.3874          | 0.5469   |
| 0.9951        | 19.0  | 2280 | 1.2976          | 0.5587   |
| 0.9794        | 20.0  | 2400 | 1.3110          | 0.5569   |
| 0.9974        | 21.0  | 2520 | 1.3649          | 0.5276   |
| 0.9284        | 22.0  | 2640 | 1.3713          | 0.5364   |
| 0.9144        | 23.0  | 2760 | 1.4117          | 0.5340   |
| 0.9771        | 24.0  | 2880 | 1.3836          | 0.5358   |
| 0.8994        | 25.0  | 3000 | 1.5077          | 0.5282   |
| 0.9061        | 26.0  | 3120 | 1.4622          | 0.5329   |
| 0.9071        | 27.0  | 3240 | 1.4303          | 0.5393   |
| 0.9288        | 28.0  | 3360 | 1.4556          | 0.5329   |
| 0.9285        | 29.0  | 3480 | 1.3900          | 0.5446   |
| 0.8955        | 30.0  | 3600 | 1.4082          | 0.5387   |


### 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-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 

large-algae-vit-wirs
====================


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


* Loss: 0.9128
* Accuracy: 0.6209


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