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# swinv2-tiny-patch4-window8-256-finetuned_swinv2tiny-autotags-256

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1115
- Accuracy: 0.9655

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6169        | 0.99  | 61   | 1.1018          | 0.6701   |
| 0.7747        | 1.99  | 122  | 0.4571          | 0.8670   |
| 0.6088        | 2.99  | 183  | 0.3002          | 0.9198   |
| 0.3908        | 3.99  | 244  | 0.2334          | 0.9299   |
| 0.399         | 4.99  | 305  | 0.2138          | 0.9320   |
| 0.2969        | 5.99  | 366  | 0.1650          | 0.9492   |
| 0.2743        | 6.99  | 427  | 0.1514          | 0.9533   |
| 0.2947        | 7.99  | 488  | 0.1428          | 0.9513   |
| 0.2304        | 8.99  | 549  | 0.1541          | 0.9523   |
| 0.1957        | 9.99  | 610  | 0.1256          | 0.9604   |
| 0.1645        | 10.99 | 671  | 0.1138          | 0.9645   |
| 0.2317        | 11.99 | 732  | 0.1140          | 0.9655   |
| 0.1001        | 12.99 | 793  | 0.1068          | 0.9706   |
| 0.1564        | 13.99 | 854  | 0.1119          | 0.9675   |
| 0.1386        | 14.99 | 915  | 0.1115          | 0.9655   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.10.2+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swinv2-tiny-patch4-window8-256-finetuned_swinv2tiny-autotags-256", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.965482233502538, "name": "Accuracy"}]}]}]}
transformers pytorch swinv2 image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

swinv2-tiny-patch4-window8-256-finetuned\_swinv2tiny-autotags-256
=================================================================


This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1115
* Accuracy: 0.9655


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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.10.2+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.10.2+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.10.2+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.10.2+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2