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# swin-tiny-finetuned-dogfood

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 lewtun/dog_food dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1959
- Accuracy: 0.988

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8198        | 1.0   | 16   | 0.1901          | 0.9822   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder", "lewtun/dog_food"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-finetuned-dogfood", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "lewtun/dog_food", "type": "lewtun/dog_food", "args": "lewtun--dog_food"}, "metrics": [{"type": "accuracy", "value": 0.988, "name": "Accuracy"}]}, {"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "lewtun/dog_food", "type": "lewtun/dog_food", "config": "lewtun--dog_food", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9826666666666667, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.9820904286553143, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.9826666666666667, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.9828416519866903, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.9828453314981092, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.9826666666666667, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.9826666666666667, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.9824101123169301, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.9826666666666667, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.9826983433609648, "name": "F1 Weighted", "verified": true}, {"type": "loss", "value": 0.2326570302248001, "name": "loss", "verified": true}, {"type": "matthews_correlation", "value": 0.974016655798285, "name": "matthews_correlation", "verified": true}]}]}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer dataset:imagefolder dataset:lewtun/dog_food license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

swin-tiny-finetuned-dogfood
===========================


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


* Loss: 0.1959
* Accuracy: 0.988


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: 1


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### 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: 1 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #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: 1 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #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: 1### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1