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

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 bird-data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6642
- Accuracy: 0.8215

## 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: 0.0002
- train_batch_size: 72
- eval_batch_size: 72
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8854        | 0.99  | 74   | 3.0164          | 0.3039   |
| 2.066         | 1.99  | 148  | 1.4849          | 0.6095   |
| 1.5066        | 2.99  | 222  | 1.0624          | 0.7145   |
| 1.1904        | 3.99  | 296  | 0.9347          | 0.7450   |
| 0.9986        | 4.99  | 370  | 0.8415          | 0.7709   |
| 0.9437        | 5.99  | 444  | 0.7713          | 0.7901   |
| 0.8297        | 6.99  | 518  | 0.7216          | 0.8081   |
| 0.7805        | 7.99  | 592  | 0.6856          | 0.8152   |
| 0.6978        | 8.99  | 666  | 0.6642          | 0.8215   |
| 0.6147        | 9.99  | 740  | 0.6525          | 0.8207   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["bird-data"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-birds", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "bird-data", "type": "bird-data", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8214882943143813, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer dataset:bird-data license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-bird-data #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

swin-tiny-patch4-window7-224-finetuned-birds
============================================


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


* Loss: 0.6642
* Accuracy: 0.8215


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: 0.0002
* train\_batch\_size: 72
* eval\_batch\_size: 72
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 72
* eval\_batch\_size: 72
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-bird-data #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 72
* eval\_batch\_size: 72
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-bird-data #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 72
* eval\_batch\_size: 72
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2