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# exper3_mesum5

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_mesuem5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6366
- Accuracy: 0.8367

## 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.895         | 0.23  | 100  | 3.8276          | 0.1935   |
| 3.1174        | 0.47  | 200  | 3.1217          | 0.3107   |
| 2.6           | 0.7   | 300  | 2.5399          | 0.4207   |
| 2.256         | 0.93  | 400  | 2.1767          | 0.5160   |
| 1.5441        | 1.16  | 500  | 1.8086          | 0.5852   |
| 1.3834        | 1.4   | 600  | 1.5565          | 0.6325   |
| 1.1995        | 1.63  | 700  | 1.3339          | 0.6763   |
| 1.0845        | 1.86  | 800  | 1.3299          | 0.6533   |
| 0.6472        | 2.09  | 900  | 1.0679          | 0.7219   |
| 0.5948        | 2.33  | 1000 | 1.0286          | 0.7124   |
| 0.5565        | 2.56  | 1100 | 0.9595          | 0.7284   |
| 0.4879        | 2.79  | 1200 | 0.8915          | 0.7420   |
| 0.2816        | 3.02  | 1300 | 0.8159          | 0.7763   |
| 0.2412        | 3.26  | 1400 | 0.7766          | 0.7911   |
| 0.2015        | 3.49  | 1500 | 0.7850          | 0.7828   |
| 0.274         | 3.72  | 1600 | 0.7361          | 0.7935   |
| 0.1244        | 3.95  | 1700 | 0.7299          | 0.7911   |
| 0.0794        | 4.19  | 1800 | 0.7441          | 0.7846   |
| 0.0915        | 4.42  | 1900 | 0.7614          | 0.7941   |
| 0.0817        | 4.65  | 2000 | 0.7310          | 0.8012   |
| 0.0561        | 4.88  | 2100 | 0.7222          | 0.8065   |
| 0.0165        | 5.12  | 2200 | 0.7515          | 0.8059   |
| 0.0168        | 5.35  | 2300 | 0.6687          | 0.8213   |
| 0.0212        | 5.58  | 2400 | 0.6671          | 0.8249   |
| 0.0389        | 5.81  | 2500 | 0.6893          | 0.8278   |
| 0.0087        | 6.05  | 2600 | 0.6839          | 0.8260   |
| 0.0087        | 6.28  | 2700 | 0.6412          | 0.8320   |
| 0.0077        | 6.51  | 2800 | 0.6366          | 0.8367   |
| 0.0065        | 6.74  | 2900 | 0.6697          | 0.8272   |
| 0.0061        | 6.98  | 3000 | 0.6510          | 0.8349   |
| 0.0185        | 7.21  | 3100 | 0.6452          | 0.8367   |
| 0.0059        | 7.44  | 3200 | 0.6426          | 0.8379   |
| 0.0062        | 7.67  | 3300 | 0.6398          | 0.8379   |
| 0.0315        | 7.91  | 3400 | 0.6397          | 0.8385   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper3_mesum5", "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 

exper3\_mesum5
==============


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6366
* Accuracy: 0.8367


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: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* 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: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* 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: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* 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: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1