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# final-algae-swin-wirs

This model is a fine-tuned version of [samitizerxu/final-algae-swin-wirs](https://huggingface.co/samitizerxu/final-algae-swin-wirs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7645
- Accuracy: 0.6725

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3024        | 1.0   | 120  | 0.7645          | 0.6725   |
| 1.2375        | 2.0   | 240  | 0.8013          | 0.6673   |
| 1.1875        | 3.0   | 360  | 0.8251          | 0.6649   |
| 1.187         | 4.0   | 480  | 0.8960          | 0.6403   |
| 1.1472        | 5.0   | 600  | 0.9558          | 0.6244   |
| 1.1374        | 6.0   | 720  | 1.1951          | 0.4877   |
| 1.1114        | 7.0   | 840  | 1.1109          | 0.5358   |
| 1.1201        | 8.0   | 960  | 0.9724          | 0.6227   |
| 1.0801        | 9.0   | 1080 | 0.9913          | 0.5863   |
| 1.0995        | 10.0  | 1200 | 1.0117          | 0.5933   |
| 1.0817        | 11.0  | 1320 | 1.0239          | 0.5951   |
| 1.0679        | 12.0  | 1440 | 1.0381          | 0.5839   |
| 1.094         | 13.0  | 1560 | 1.0480          | 0.5910   |
| 1.0325        | 14.0  | 1680 | 1.0671          | 0.5839   |
| 1.0087        | 15.0  | 1800 | 1.0133          | 0.5892   |
| 1.0525        | 16.0  | 1920 | 1.0332          | 0.5775   |
| 1.0614        | 17.0  | 2040 | 1.0085          | 0.5939   |
| 1.0065        | 18.0  | 2160 | 1.0070          | 0.5974   |
| 1.0474        | 19.0  | 2280 | 1.0023          | 0.5898   |
| 1.0346        | 20.0  | 2400 | 1.0072          | 0.5839   |
| 1.0226        | 21.0  | 2520 | 1.0219          | 0.5792   |
| 1.0474        | 22.0  | 2640 | 1.0106          | 0.5880   |
| 0.983         | 23.0  | 2760 | 1.0020          | 0.5874   |
| 0.9997        | 24.0  | 2880 | 1.0838          | 0.5593   |
| 1.0074        | 25.0  | 3000 | 1.0781          | 0.5593   |
| 1.0           | 26.0  | 3120 | 1.0378          | 0.5751   |
| 1.0279        | 27.0  | 3240 | 1.0737          | 0.5604   |
| 0.9696        | 28.0  | 3360 | 1.1385          | 0.5123   |
| 0.9862        | 29.0  | 3480 | 1.1236          | 0.5282   |
| 1.0155        | 30.0  | 3600 | 1.0415          | 0.5798   |
| 0.9723        | 31.0  | 3720 | 1.1447          | 0.5258   |
| 0.9935        | 32.0  | 3840 | 1.1166          | 0.5323   |
| 0.9965        | 33.0  | 3960 | 1.0502          | 0.5716   |
| 0.9645        | 34.0  | 4080 | 1.1316          | 0.5329   |
| 0.9771        | 35.0  | 4200 | 1.1860          | 0.5170   |
| 0.9976        | 36.0  | 4320 | 1.2937          | 0.4906   |
| 0.9207        | 37.0  | 4440 | 1.2272          | 0.5135   |
| 0.9813        | 38.0  | 4560 | 1.2067          | 0.5258   |
| 0.9337        | 39.0  | 4680 | 1.2162          | 0.5282   |
| 0.9628        | 40.0  | 4800 | 1.2700          | 0.5059   |
| 0.9561        | 41.0  | 4920 | 1.2428          | 0.5094   |
| 0.9208        | 42.0  | 5040 | 1.2271          | 0.5158   |
| 0.9097        | 43.0  | 5160 | 1.2388          | 0.5182   |
| 0.9487        | 44.0  | 5280 | 1.1966          | 0.5264   |
| 0.9386        | 45.0  | 5400 | 1.2107          | 0.5258   |
| 0.9291        | 46.0  | 5520 | 1.2893          | 0.4977   |
| 0.9357        | 47.0  | 5640 | 1.2764          | 0.5041   |
| 0.9064        | 48.0  | 5760 | 1.2710          | 0.5012   |
| 0.9032        | 49.0  | 5880 | 1.2695          | 0.5      |
| 0.9423        | 50.0  | 6000 | 1.2703          | 0.4982   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "final-algae-swin-wirs", "results": []}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

final-algae-swin-wirs
=====================


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


* Loss: 0.7645
* Accuracy: 0.6725


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: 50


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* 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: 50 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #swin #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: 50 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #swin #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: 50### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2