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# leaves

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the defect dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Accuracy: 1.0

## 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: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2249        | 1.25  | 10   | 0.0323          | 1.0      |
| 0.0177        | 2.5   | 20   | 0.0112          | 1.0      |
| 0.0086        | 3.75  | 30   | 0.0075          | 1.0      |
| 0.0063        | 5.0   | 40   | 0.0059          | 1.0      |
| 0.0051        | 6.25  | 50   | 0.0050          | 1.0      |
| 0.0045        | 7.5   | 60   | 0.0044          | 1.0      |
| 0.004         | 8.75  | 70   | 0.0040          | 1.0      |
| 0.0036        | 10.0  | 80   | 0.0036          | 1.0      |
| 0.0033        | 11.25 | 90   | 0.0034          | 1.0      |
| 0.0031        | 12.5  | 100  | 0.0031          | 1.0      |
| 0.0028        | 13.75 | 110  | 0.0029          | 1.0      |
| 0.0026        | 15.0  | 120  | 0.0027          | 1.0      |
| 0.0025        | 16.25 | 130  | 0.0025          | 1.0      |
| 0.0023        | 17.5  | 140  | 0.0024          | 1.0      |
| 0.0022        | 18.75 | 150  | 0.0023          | 1.0      |
| 0.0021        | 20.0  | 160  | 0.0021          | 1.0      |
| 0.002         | 21.25 | 170  | 0.0020          | 1.0      |
| 0.0019        | 22.5  | 180  | 0.0019          | 1.0      |
| 0.0018        | 23.75 | 190  | 0.0019          | 1.0      |
| 0.0017        | 25.0  | 200  | 0.0018          | 1.0      |
| 0.0016        | 26.25 | 210  | 0.0017          | 1.0      |
| 0.0016        | 27.5  | 220  | 0.0017          | 1.0      |
| 0.0015        | 28.75 | 230  | 0.0016          | 1.0      |
| 0.0015        | 30.0  | 240  | 0.0015          | 1.0      |
| 0.0014        | 31.25 | 250  | 0.0015          | 1.0      |
| 0.0014        | 32.5  | 260  | 0.0015          | 1.0      |
| 0.0013        | 33.75 | 270  | 0.0014          | 1.0      |
| 0.0013        | 35.0  | 280  | 0.0014          | 1.0      |
| 0.0013        | 36.25 | 290  | 0.0014          | 1.0      |
| 0.0013        | 37.5  | 300  | 0.0013          | 1.0      |
| 0.0012        | 38.75 | 310  | 0.0013          | 1.0      |
| 0.0012        | 40.0  | 320  | 0.0013          | 1.0      |
| 0.0012        | 41.25 | 330  | 0.0013          | 1.0      |
| 0.0012        | 42.5  | 340  | 0.0013          | 1.0      |
| 0.0012        | 43.75 | 350  | 0.0012          | 1.0      |
| 0.0012        | 45.0  | 360  | 0.0012          | 1.0      |
| 0.0011        | 46.25 | 370  | 0.0012          | 1.0      |
| 0.0012        | 47.5  | 380  | 0.0012          | 1.0      |
| 0.0011        | 48.75 | 390  | 0.0012          | 1.0      |
| 0.0011        | 50.0  | 400  | 0.0012          | 1.0      |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "leaves", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "defect", "type": "imagefolder", "config": "Dhika--Leaves", "split": "validation", "args": "Dhika--Leaves"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

leaves
======


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the defect dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0012
* Accuracy: 1.0


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: 10
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 10
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #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: 0.0002
* train\_batch\_size: 10
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3