
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# exper_batch_8_e4

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 sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3353
- Accuracy: 0.9183

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Apex, opt level O1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2251        | 0.08  | 100  | 4.1508          | 0.1203   |
| 3.4942        | 0.16  | 200  | 3.5566          | 0.2082   |
| 3.2871        | 0.23  | 300  | 3.0942          | 0.3092   |
| 2.7273        | 0.31  | 400  | 2.8338          | 0.3308   |
| 2.4984        | 0.39  | 500  | 2.4860          | 0.4341   |
| 2.3423        | 0.47  | 600  | 2.2201          | 0.4796   |
| 1.8785        | 0.55  | 700  | 2.1890          | 0.4653   |
| 1.8012        | 0.63  | 800  | 1.9901          | 0.4865   |
| 1.7236        | 0.7   | 900  | 1.6821          | 0.5736   |
| 1.4949        | 0.78  | 1000 | 1.5422          | 0.6083   |
| 1.5573        | 0.86  | 1100 | 1.5436          | 0.6110   |
| 1.3241        | 0.94  | 1200 | 1.4077          | 0.6207   |
| 1.0773        | 1.02  | 1300 | 1.1417          | 0.6916   |
| 0.7935        | 1.1   | 1400 | 1.1194          | 0.6931   |
| 0.7677        | 1.17  | 1500 | 1.0727          | 0.7167   |
| 0.9468        | 1.25  | 1600 | 1.0707          | 0.7136   |
| 0.7563        | 1.33  | 1700 | 0.9427          | 0.7390   |
| 0.8471        | 1.41  | 1800 | 0.8906          | 0.7571   |
| 0.9998        | 1.49  | 1900 | 0.8098          | 0.7845   |
| 0.6039        | 1.57  | 2000 | 0.7244          | 0.8034   |
| 0.7052        | 1.64  | 2100 | 0.7881          | 0.7953   |
| 0.6753        | 1.72  | 2200 | 0.7458          | 0.7926   |
| 0.3758        | 1.8   | 2300 | 0.6987          | 0.8022   |
| 0.4985        | 1.88  | 2400 | 0.6286          | 0.8265   |
| 0.4122        | 1.96  | 2500 | 0.5949          | 0.8358   |
| 0.1286        | 2.04  | 2600 | 0.5691          | 0.8385   |
| 0.1989        | 2.11  | 2700 | 0.5535          | 0.8389   |
| 0.3304        | 2.19  | 2800 | 0.5261          | 0.8520   |
| 0.3415        | 2.27  | 2900 | 0.5504          | 0.8477   |
| 0.4066        | 2.35  | 3000 | 0.5418          | 0.8497   |
| 0.1208        | 2.43  | 3100 | 0.5156          | 0.8612   |
| 0.1668        | 2.51  | 3200 | 0.5655          | 0.8539   |
| 0.0727        | 2.58  | 3300 | 0.4971          | 0.8658   |
| 0.0929        | 2.66  | 3400 | 0.4962          | 0.8635   |
| 0.0678        | 2.74  | 3500 | 0.4903          | 0.8670   |
| 0.1212        | 2.82  | 3600 | 0.4357          | 0.8867   |
| 0.1579        | 2.9   | 3700 | 0.4642          | 0.8739   |
| 0.2625        | 2.98  | 3800 | 0.3994          | 0.8951   |
| 0.024         | 3.05  | 3900 | 0.3953          | 0.8971   |
| 0.0696        | 3.13  | 4000 | 0.3883          | 0.9056   |
| 0.0169        | 3.21  | 4100 | 0.3755          | 0.9086   |
| 0.023         | 3.29  | 4200 | 0.3685          | 0.9109   |
| 0.0337        | 3.37  | 4300 | 0.3623          | 0.9109   |
| 0.0123        | 3.45  | 4400 | 0.3647          | 0.9067   |
| 0.0159        | 3.52  | 4500 | 0.3630          | 0.9082   |
| 0.0154        | 3.6   | 4600 | 0.3522          | 0.9094   |
| 0.0112        | 3.68  | 4700 | 0.3439          | 0.9163   |
| 0.0219        | 3.76  | 4800 | 0.3404          | 0.9194   |
| 0.0183        | 3.84  | 4900 | 0.3371          | 0.9183   |
| 0.0103        | 3.92  | 5000 | 0.3362          | 0.9183   |
| 0.0357        | 3.99  | 5100 | 0.3353          | 0.9183   |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_8_e4", "results": []}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

exper\_batch\_8\_e4
===================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3353
* Accuracy: 0.9183


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Apex, opt level O1


### Training results



### Framework versions


* Transformers 4.19.4
* Pytorch 1.5.1
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Apex, opt level O1 ### Training results ### Framework versions


* Transformers 4.19.4
* Pytorch 1.5.1
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Apex, opt level O1 ### Training results ### Framework versions


* Transformers 4.19.4
* Pytorch 1.5.1
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Apex, opt level O1### Training results### Framework versions


* Transformers 4.19.4
* Pytorch 1.5.1
* Datasets 2.3.2
* Tokenizers 0.12.1