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# exper_batch_16_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: 0.3598
- Accuracy: 0.9059

## 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: 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: 4
- mixed_precision_training: Apex, opt level O1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.7606        | 0.16  | 100  | 3.7839          | 0.1989   |
| 3.1072        | 0.31  | 200  | 3.0251          | 0.3285   |
| 2.4068        | 0.47  | 300  | 2.4380          | 0.4719   |
| 2.0881        | 0.63  | 400  | 2.0489          | 0.5412   |
| 1.6817        | 0.78  | 500  | 1.7968          | 0.6025   |
| 1.342         | 0.94  | 600  | 1.5044          | 0.6249   |
| 0.9343        | 1.1   | 700  | 1.1881          | 0.7132   |
| 0.9552        | 1.25  | 800  | 1.1064          | 0.7224   |
| 0.7265        | 1.41  | 900  | 0.9189          | 0.7768   |
| 0.6732        | 1.56  | 1000 | 0.9227          | 0.7606   |
| 0.5587        | 1.72  | 1100 | 0.7912          | 0.7903   |
| 0.6332        | 1.88  | 1200 | 0.7606          | 0.7945   |
| 0.3188        | 2.03  | 1300 | 0.6535          | 0.8288   |
| 0.3079        | 2.19  | 1400 | 0.5686          | 0.8577   |
| 0.2518        | 2.35  | 1500 | 0.5517          | 0.8577   |
| 0.2           | 2.5   | 1600 | 0.5277          | 0.8631   |
| 0.2032        | 2.66  | 1700 | 0.4841          | 0.8701   |
| 0.1555        | 2.82  | 1800 | 0.4578          | 0.8793   |
| 0.145         | 2.97  | 1900 | 0.4466          | 0.8755   |
| 0.0985        | 3.13  | 2000 | 0.4249          | 0.8867   |
| 0.0955        | 3.29  | 2100 | 0.3977          | 0.8932   |
| 0.0438        | 3.44  | 2200 | 0.3785          | 0.9036   |
| 0.0589        | 3.6   | 2300 | 0.3717          | 0.9017   |
| 0.0709        | 3.76  | 2400 | 0.3609          | 0.9052   |
| 0.0706        | 3.91  | 2500 | 0.3598          | 0.9059   |


### 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_16_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\_16\_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: 0.3598
* Accuracy: 0.9059


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: 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: 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: 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: 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: 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: 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: 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: 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