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# swin-tiny-patch4-window7-224-finetuned-new_dataset_50e

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6407
- Accuracy: 0.7973

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.94  | 4    | 0.7081          | 0.6081   |
| No log        | 1.94  | 8    | 0.7104          | 0.6351   |
| 0.5516        | 2.94  | 12   | 0.6911          | 0.6351   |
| 0.5516        | 3.94  | 16   | 0.7156          | 0.7027   |
| 0.537         | 4.94  | 20   | 0.7345          | 0.7297   |
| 0.537         | 5.94  | 24   | 0.6745          | 0.6892   |
| 0.537         | 6.94  | 28   | 0.7146          | 0.7297   |
| 0.5333        | 7.94  | 32   | 0.7057          | 0.6892   |
| 0.5333        | 8.94  | 36   | 0.6531          | 0.7027   |
| 0.4871        | 9.94  | 40   | 0.6405          | 0.7027   |
| 0.4871        | 10.94 | 44   | 0.6126          | 0.6892   |
| 0.4871        | 11.94 | 48   | 0.6303          | 0.7027   |
| 0.4432        | 12.94 | 52   | 0.6264          | 0.7027   |
| 0.4432        | 13.94 | 56   | 0.6347          | 0.7432   |
| 0.3669        | 14.94 | 60   | 0.6698          | 0.6622   |
| 0.3669        | 15.94 | 64   | 0.6346          | 0.7568   |
| 0.3669        | 16.94 | 68   | 0.6510          | 0.6892   |
| 0.3704        | 17.94 | 72   | 0.6491          | 0.6892   |
| 0.3704        | 18.94 | 76   | 0.5947          | 0.7568   |
| 0.3624        | 19.94 | 80   | 0.6248          | 0.7027   |
| 0.3624        | 20.94 | 84   | 0.6580          | 0.7027   |
| 0.3624        | 21.94 | 88   | 0.6345          | 0.7162   |
| 0.3164        | 22.94 | 92   | 0.6092          | 0.7568   |
| 0.3164        | 23.94 | 96   | 0.6498          | 0.7162   |
| 0.2777        | 24.94 | 100  | 0.6915          | 0.7703   |
| 0.2777        | 25.94 | 104  | 0.6482          | 0.7838   |
| 0.2777        | 26.94 | 108  | 0.6407          | 0.7973   |
| 0.2946        | 27.94 | 112  | 0.6135          | 0.7838   |
| 0.2946        | 28.94 | 116  | 0.6819          | 0.7568   |
| 0.2546        | 29.94 | 120  | 0.6401          | 0.7568   |
| 0.2546        | 30.94 | 124  | 0.6370          | 0.7432   |
| 0.2546        | 31.94 | 128  | 0.6488          | 0.7703   |
| 0.2477        | 32.94 | 132  | 0.6429          | 0.7973   |
| 0.2477        | 33.94 | 136  | 0.6540          | 0.7703   |
| 0.1968        | 34.94 | 140  | 0.5895          | 0.7973   |
| 0.1968        | 35.94 | 144  | 0.6242          | 0.7568   |
| 0.1968        | 36.94 | 148  | 0.6575          | 0.7568   |
| 0.2235        | 37.94 | 152  | 0.6263          | 0.7703   |
| 0.2235        | 38.94 | 156  | 0.6225          | 0.7838   |
| 0.2005        | 39.94 | 160  | 0.6731          | 0.7703   |
| 0.2005        | 40.94 | 164  | 0.6844          | 0.7703   |
| 0.2005        | 41.94 | 168  | 0.6550          | 0.7703   |
| 0.2062        | 42.94 | 172  | 0.6700          | 0.7703   |
| 0.2062        | 43.94 | 176  | 0.6661          | 0.7703   |
| 0.1933        | 44.94 | 180  | 0.6606          | 0.7838   |
| 0.1933        | 45.94 | 184  | 0.6757          | 0.7703   |
| 0.1933        | 46.94 | 188  | 0.6889          | 0.7568   |
| 0.1895        | 47.94 | 192  | 0.6940          | 0.7568   |
| 0.1895        | 48.94 | 196  | 0.6919          | 0.7568   |
| 0.1666        | 49.94 | 200  | 0.6899          | 0.7432   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-new_dataset_50e", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7972972972972973, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

swin-tiny-patch4-window7-224-finetuned-new\_dataset\_50e
========================================================


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


* Loss: 0.6407
* Accuracy: 0.7973


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 4.26.0
* 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: 50 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 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 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 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 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2