
<!-- 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_32_e8

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: 0.3520
- Accuracy: 0.9113

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Apex, opt level O1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.3787        | 0.31  | 100  | 3.3100          | 0.3566   |
| 2.3975        | 0.62  | 200  | 2.3196          | 0.5717   |
| 1.5578        | 0.94  | 300  | 1.6764          | 0.6461   |
| 1.0291        | 1.25  | 400  | 1.1713          | 0.7463   |
| 0.8185        | 1.56  | 500  | 0.9292          | 0.7953   |
| 0.6181        | 1.88  | 600  | 0.7732          | 0.8169   |
| 0.3873        | 2.19  | 700  | 0.6877          | 0.8277   |
| 0.2979        | 2.5   | 800  | 0.6250          | 0.8404   |
| 0.2967        | 2.81  | 900  | 0.6151          | 0.8365   |
| 0.1874        | 3.12  | 1000 | 0.5401          | 0.8608   |
| 0.2232        | 3.44  | 1100 | 0.5032          | 0.8712   |
| 0.1109        | 3.75  | 1200 | 0.4635          | 0.8774   |
| 0.0539        | 4.06  | 1300 | 0.4495          | 0.8843   |
| 0.0668        | 4.38  | 1400 | 0.4273          | 0.8951   |
| 0.0567        | 4.69  | 1500 | 0.4427          | 0.8867   |
| 0.0285        | 5.0   | 1600 | 0.4092          | 0.8955   |
| 0.0473        | 5.31  | 1700 | 0.3720          | 0.9071   |
| 0.0225        | 5.62  | 1800 | 0.3691          | 0.9063   |
| 0.0196        | 5.94  | 1900 | 0.3775          | 0.9048   |
| 0.0173        | 6.25  | 2000 | 0.3641          | 0.9040   |
| 0.0092        | 6.56  | 2100 | 0.3551          | 0.9090   |
| 0.008         | 6.88  | 2200 | 0.3591          | 0.9125   |
| 0.0072        | 7.19  | 2300 | 0.3542          | 0.9121   |
| 0.007         | 7.5   | 2400 | 0.3532          | 0.9106   |
| 0.007         | 7.81  | 2500 | 0.3520          | 0.9113   |


### 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_32_e8", "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\_32\_e8
====================


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: 0.3520
* Accuracy: 0.9113


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: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* 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: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* 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: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* 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: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* 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