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# Beit-for-rice-disease

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1687
- Accuracy: 0.95

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4305        | 0.16  | 10   | 2.0142          | 0.5      |
| 1.804         | 0.32  | 20   | 1.5097          | 0.5446   |
| 1.2088        | 0.48  | 30   | 1.0185          | 0.6786   |
| 0.9168        | 0.63  | 40   | 0.8841          | 0.7768   |
| 0.7893        | 0.79  | 50   | 0.7512          | 0.7768   |
| 0.6253        | 0.95  | 60   | 0.9253          | 0.7411   |
| 0.4949        | 1.11  | 70   | 0.5854          | 0.8214   |
| 0.3272        | 1.27  | 80   | 0.5379          | 0.8393   |
| 0.3753        | 1.43  | 90   | 0.5194          | 0.8571   |
| 0.3378        | 1.59  | 100  | 0.3831          | 0.8661   |
| 0.3643        | 1.75  | 110  | 0.4066          | 0.8661   |
| 0.3413        | 1.9   | 120  | 0.4268          | 0.875    |
| 0.3278        | 2.06  | 130  | 0.4754          | 0.8482   |
| 0.1784        | 2.22  | 140  | 0.4979          | 0.8839   |
| 0.2002        | 2.38  | 150  | 0.3606          | 0.8929   |
| 0.1224        | 2.54  | 160  | 0.3852          | 0.8839   |
| 0.2458        | 2.7   | 170  | 0.3492          | 0.9107   |
| 0.1816        | 2.86  | 180  | 0.3506          | 0.8839   |
| 0.2194        | 3.02  | 190  | 0.3255          | 0.9018   |
| 0.1582        | 3.17  | 200  | 0.4486          | 0.875    |
| 0.2072        | 3.33  | 210  | 0.3760          | 0.8929   |
| 0.1317        | 3.49  | 220  | 0.3021          | 0.9286   |
| 0.1223        | 3.65  | 230  | 0.3867          | 0.9018   |
| 0.1543        | 3.81  | 240  | 0.2640          | 0.9286   |
| 0.1256        | 3.97  | 250  | 0.2925          | 0.9196   |
| 0.095         | 4.13  | 260  | 0.2909          | 0.9107   |
| 0.0578        | 4.29  | 270  | 0.2825          | 0.9196   |
| 0.0428        | 4.44  | 280  | 0.2826          | 0.9196   |
| 0.1743        | 4.6   | 290  | 0.2746          | 0.9196   |
| 0.0659        | 4.76  | 300  | 0.3457          | 0.8929   |
| 0.0871        | 4.92  | 310  | 0.2902          | 0.9196   |
| 0.0726        | 5.08  | 320  | 0.2810          | 0.9196   |
| 0.0556        | 5.24  | 330  | 0.3244          | 0.9107   |
| 0.0629        | 5.4   | 340  | 0.2812          | 0.9286   |
| 0.0517        | 5.56  | 350  | 0.2638          | 0.9464   |
| 0.078         | 5.71  | 360  | 0.2580          | 0.9554   |
| 0.0376        | 5.87  | 370  | 0.2375          | 0.9464   |
| 0.0262        | 6.03  | 380  | 0.2274          | 0.9464   |
| 0.0582        | 6.19  | 390  | 0.2254          | 0.9643   |
| 0.0215        | 6.35  | 400  | 0.2545          | 0.9375   |
| 0.0379        | 6.51  | 410  | 0.2925          | 0.9286   |
| 0.0664        | 6.67  | 420  | 0.2842          | 0.9196   |
| 0.0375        | 6.83  | 430  | 0.2643          | 0.9375   |
| 0.0088        | 6.98  | 440  | 0.2401          | 0.9375   |
| 0.0299        | 7.14  | 450  | 0.2634          | 0.9286   |
| 0.0199        | 7.3   | 460  | 0.2800          | 0.9286   |
| 0.0182        | 7.46  | 470  | 0.2996          | 0.9107   |
| 0.0199        | 7.62  | 480  | 0.2813          | 0.9286   |
| 0.0189        | 7.78  | 490  | 0.2571          | 0.9286   |
| 0.053         | 7.94  | 500  | 0.2260          | 0.9196   |
| 0.0448        | 8.1   | 510  | 0.2112          | 0.9464   |
| 0.0072        | 8.25  | 520  | 0.2133          | 0.9286   |
| 0.0112        | 8.41  | 530  | 0.2149          | 0.9464   |
| 0.021         | 8.57  | 540  | 0.2189          | 0.9286   |
| 0.0073        | 8.73  | 550  | 0.2397          | 0.9375   |
| 0.0128        | 8.89  | 560  | 0.2578          | 0.9375   |
| 0.005         | 9.05  | 570  | 0.2578          | 0.9375   |
| 0.0246        | 9.21  | 580  | 0.2485          | 0.9286   |
| 0.0073        | 9.37  | 590  | 0.2460          | 0.9286   |
| 0.005         | 9.52  | 600  | 0.2429          | 0.9286   |
| 0.0083        | 9.68  | 610  | 0.2362          | 0.9286   |
| 0.0114        | 9.84  | 620  | 0.2338          | 0.9286   |
| 0.0031        | 10.0  | 630  | 0.2327          | 0.9286   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "Beit-for-rice-disease", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.95, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard beit image-classification generated_from_trainer dataset:image_folder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Beit-for-rice-disease
=====================


This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the image\_folder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1687
* Accuracy: 0.95


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-image_folder #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3