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

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: 0.3605
- Accuracy: 0.9203

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5913        | 1.0   | 31   | 0.7046          | 0.7175   |
| 0.1409        | 2.0   | 62   | 0.8423          | 0.6788   |
| 0.0825        | 3.0   | 93   | 0.6224          | 0.7654   |
| 0.0509        | 4.0   | 124  | 0.4379          | 0.8360   |
| 0.0439        | 5.0   | 155  | 0.1706          | 0.9317   |
| 0.0107        | 6.0   | 186  | 0.1914          | 0.9362   |
| 0.0134        | 7.0   | 217  | 0.2491          | 0.9089   |
| 0.0338        | 8.0   | 248  | 0.2119          | 0.9362   |
| 0.0306        | 9.0   | 279  | 0.4502          | 0.8610   |
| 0.0054        | 10.0  | 310  | 0.4990          | 0.8747   |
| 0.0033        | 11.0  | 341  | 0.2746          | 0.9112   |
| 0.0021        | 12.0  | 372  | 0.2501          | 0.9317   |
| 0.0068        | 13.0  | 403  | 0.1883          | 0.9522   |
| 0.0038        | 14.0  | 434  | 0.3672          | 0.9134   |
| 0.0006        | 15.0  | 465  | 0.2275          | 0.9408   |
| 0.0011        | 16.0  | 496  | 0.3349          | 0.9134   |
| 0.0017        | 17.0  | 527  | 0.3329          | 0.9157   |
| 0.0007        | 18.0  | 558  | 0.2508          | 0.9317   |
| 0.0023        | 19.0  | 589  | 0.2338          | 0.9385   |
| 0.0003        | 20.0  | 620  | 0.3193          | 0.9226   |
| 0.002         | 21.0  | 651  | 0.4604          | 0.9043   |
| 0.0023        | 22.0  | 682  | 0.3338          | 0.9203   |
| 0.005         | 23.0  | 713  | 0.2925          | 0.9271   |
| 0.0001        | 24.0  | 744  | 0.2022          | 0.9522   |
| 0.0002        | 25.0  | 775  | 0.2699          | 0.9339   |
| 0.0007        | 26.0  | 806  | 0.2603          | 0.9385   |
| 0.0005        | 27.0  | 837  | 0.4120          | 0.9134   |
| 0.0003        | 28.0  | 868  | 0.3550          | 0.9203   |
| 0.0008        | 29.0  | 899  | 0.3657          | 0.9203   |
| 0.0           | 30.0  | 930  | 0.3605          | 0.9203   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-agrivision", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9202733485193622, "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-tiny-patch4-window7-224-finetuned-agrivision
=================================================


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: 0.3605
* Accuracy: 0.9203


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: 30


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* 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: 30 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
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: 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: 30 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: 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: 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: 30### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1