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# swin-base-patch4-window7-224-in22k-finetuned_swinv1-autotags-224

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1186
- Accuracy: 0.9675

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1468        | 0.99  | 61   | 0.8540          | 0.7503   |
| 0.6167        | 1.99  | 122  | 0.3772          | 0.8904   |
| 0.45          | 2.99  | 183  | 0.2963          | 0.9086   |
| 0.2163        | 3.99  | 244  | 0.2172          | 0.9391   |
| 0.209         | 4.99  | 305  | 0.1733          | 0.9431   |
| 0.1558        | 5.99  | 366  | 0.2101          | 0.9310   |
| 0.109         | 6.99  | 427  | 0.1268          | 0.9655   |
| 0.1214        | 7.99  | 488  | 0.1251          | 0.9706   |
| 0.1471        | 8.99  | 549  | 0.1194          | 0.9665   |
| 0.0888        | 9.99  | 610  | 0.1376          | 0.9574   |
| 0.1077        | 10.99 | 671  | 0.1211          | 0.9614   |
| 0.0969        | 11.99 | 732  | 0.1231          | 0.9695   |
| 0.0585        | 12.99 | 793  | 0.1472          | 0.9553   |
| 0.0659        | 13.99 | 854  | 0.1203          | 0.9655   |
| 0.0645        | 14.99 | 915  | 0.1405          | 0.9614   |
| 0.0472        | 15.99 | 976  | 0.1340          | 0.9604   |
| 0.0616        | 16.99 | 1037 | 0.1272          | 0.9655   |
| 0.0609        | 17.99 | 1098 | 0.1121          | 0.9685   |
| 0.0525        | 18.99 | 1159 | 0.1162          | 0.9685   |
| 0.0406        | 19.99 | 1220 | 0.1186          | 0.9675   |


### 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": "swin-base-patch4-window7-224-in22k-finetuned_swinv1-autotags-224", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9675126903553299, "name": "Accuracy"}]}]}]}
transformers pytorch swin image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

swin-base-patch4-window7-224-in22k-finetuned\_swinv1-autotags-224
=================================================================


This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1186
* Accuracy: 0.9675


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: 20


### 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: 20 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.10.2+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #swin #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: 20 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.10.2+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #swin #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: 20### Training results### Framework versions


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