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# exper_batch_8_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: 0.4608
- Accuracy: 0.9052

## 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: 8
- mixed_precision_training: Apex, opt level O1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.2202        | 0.08  | 100   | 4.1245          | 0.1237   |
| 3.467         | 0.16  | 200   | 3.5622          | 0.2143   |
| 3.3469        | 0.23  | 300   | 3.1688          | 0.2675   |
| 2.8086        | 0.31  | 400   | 2.8965          | 0.3034   |
| 2.6291        | 0.39  | 500   | 2.5858          | 0.4025   |
| 2.2382        | 0.47  | 600   | 2.2908          | 0.4133   |
| 1.9259        | 0.55  | 700   | 2.2007          | 0.4676   |
| 1.8088        | 0.63  | 800   | 2.0419          | 0.4742   |
| 1.9462        | 0.7   | 900   | 1.6793          | 0.5578   |
| 1.5392        | 0.78  | 1000  | 1.5460          | 0.6079   |
| 1.561         | 0.86  | 1100  | 1.5793          | 0.5690   |
| 1.2135        | 0.94  | 1200  | 1.4663          | 0.5929   |
| 1.0725        | 1.02  | 1300  | 1.2974          | 0.6534   |
| 0.8696        | 1.1   | 1400  | 1.2406          | 0.6569   |
| 0.8758        | 1.17  | 1500  | 1.2127          | 0.6623   |
| 1.1737        | 1.25  | 1600  | 1.2243          | 0.6550   |
| 0.8242        | 1.33  | 1700  | 1.1371          | 0.6735   |
| 1.0141        | 1.41  | 1800  | 1.0536          | 0.7024   |
| 0.9855        | 1.49  | 1900  | 0.9885          | 0.7205   |
| 0.805         | 1.57  | 2000  | 0.9048          | 0.7479   |
| 0.7207        | 1.64  | 2100  | 0.8842          | 0.7490   |
| 0.7101        | 1.72  | 2200  | 0.8954          | 0.7436   |
| 0.5946        | 1.8   | 2300  | 0.9174          | 0.7386   |
| 0.6937        | 1.88  | 2400  | 0.7818          | 0.7760   |
| 0.5593        | 1.96  | 2500  | 0.7449          | 0.7934   |
| 0.4139        | 2.04  | 2600  | 0.7787          | 0.7830   |
| 0.2929        | 2.11  | 2700  | 0.7122          | 0.7945   |
| 0.4159        | 2.19  | 2800  | 0.7446          | 0.7907   |
| 0.4079        | 2.27  | 2900  | 0.7354          | 0.7938   |
| 0.516         | 2.35  | 3000  | 0.7499          | 0.8007   |
| 0.2728        | 2.43  | 3100  | 0.6851          | 0.8061   |
| 0.4159        | 2.51  | 3200  | 0.7258          | 0.7999   |
| 0.3396        | 2.58  | 3300  | 0.7455          | 0.7972   |
| 0.1918        | 2.66  | 3400  | 0.6793          | 0.8119   |
| 0.1228        | 2.74  | 3500  | 0.6696          | 0.8134   |
| 0.2671        | 2.82  | 3600  | 0.6306          | 0.8285   |
| 0.4986        | 2.9   | 3700  | 0.6111          | 0.8296   |
| 0.3699        | 2.98  | 3800  | 0.5600          | 0.8508   |
| 0.0444        | 3.05  | 3900  | 0.6021          | 0.8331   |
| 0.1489        | 3.13  | 4000  | 0.5599          | 0.8516   |
| 0.15          | 3.21  | 4100  | 0.6377          | 0.8365   |
| 0.2535        | 3.29  | 4200  | 0.5752          | 0.8543   |
| 0.2679        | 3.37  | 4300  | 0.5677          | 0.8608   |
| 0.0989        | 3.45  | 4400  | 0.6325          | 0.8396   |
| 0.0825        | 3.52  | 4500  | 0.5979          | 0.8524   |
| 0.0427        | 3.6   | 4600  | 0.5903          | 0.8516   |
| 0.1806        | 3.68  | 4700  | 0.5323          | 0.8628   |
| 0.2672        | 3.76  | 4800  | 0.5688          | 0.8604   |
| 0.2674        | 3.84  | 4900  | 0.5369          | 0.8635   |
| 0.2185        | 3.92  | 5000  | 0.4743          | 0.8820   |
| 0.2195        | 3.99  | 5100  | 0.5340          | 0.8709   |
| 0.0049        | 4.07  | 5200  | 0.5883          | 0.8608   |
| 0.0204        | 4.15  | 5300  | 0.6102          | 0.8539   |
| 0.0652        | 4.23  | 5400  | 0.5659          | 0.8670   |
| 0.028         | 4.31  | 5500  | 0.4916          | 0.8840   |
| 0.0423        | 4.39  | 5600  | 0.5706          | 0.8736   |
| 0.0087        | 4.46  | 5700  | 0.5653          | 0.8697   |
| 0.0964        | 4.54  | 5800  | 0.5423          | 0.8755   |
| 0.0841        | 4.62  | 5900  | 0.5160          | 0.8743   |
| 0.0945        | 4.7   | 6000  | 0.5532          | 0.8697   |
| 0.0311        | 4.78  | 6100  | 0.4947          | 0.8867   |
| 0.0423        | 4.86  | 6200  | 0.5063          | 0.8843   |
| 0.1348        | 4.93  | 6300  | 0.5619          | 0.8743   |
| 0.049         | 5.01  | 6400  | 0.5800          | 0.8732   |
| 0.0053        | 5.09  | 6500  | 0.5499          | 0.8770   |
| 0.0234        | 5.17  | 6600  | 0.5102          | 0.8874   |
| 0.0192        | 5.25  | 6700  | 0.5447          | 0.8836   |
| 0.0029        | 5.32  | 6800  | 0.4787          | 0.8936   |
| 0.0249        | 5.4   | 6900  | 0.5232          | 0.8870   |
| 0.0671        | 5.48  | 7000  | 0.4766          | 0.8975   |
| 0.0056        | 5.56  | 7100  | 0.5136          | 0.8894   |
| 0.003         | 5.64  | 7200  | 0.5085          | 0.8882   |
| 0.0015        | 5.72  | 7300  | 0.4832          | 0.8971   |
| 0.0014        | 5.79  | 7400  | 0.4648          | 0.8998   |
| 0.0065        | 5.87  | 7500  | 0.4739          | 0.8978   |
| 0.0011        | 5.95  | 7600  | 0.5349          | 0.8867   |
| 0.0021        | 6.03  | 7700  | 0.5460          | 0.8847   |
| 0.0012        | 6.11  | 7800  | 0.5309          | 0.8890   |
| 0.0011        | 6.19  | 7900  | 0.4852          | 0.8998   |
| 0.0093        | 6.26  | 8000  | 0.4751          | 0.8998   |
| 0.003         | 6.34  | 8100  | 0.4934          | 0.8963   |
| 0.0027        | 6.42  | 8200  | 0.4882          | 0.9029   |
| 0.0009        | 6.5   | 8300  | 0.4806          | 0.9021   |
| 0.0009        | 6.58  | 8400  | 0.4974          | 0.9029   |
| 0.0009        | 6.66  | 8500  | 0.4748          | 0.9075   |
| 0.0008        | 6.73  | 8600  | 0.4723          | 0.9094   |
| 0.001         | 6.81  | 8700  | 0.4692          | 0.9098   |
| 0.0007        | 6.89  | 8800  | 0.4726          | 0.9075   |
| 0.0011        | 6.97  | 8900  | 0.4686          | 0.9067   |
| 0.0006        | 7.05  | 9000  | 0.4653          | 0.9056   |
| 0.0006        | 7.13  | 9100  | 0.4755          | 0.9029   |
| 0.0007        | 7.2   | 9200  | 0.4633          | 0.9036   |
| 0.0067        | 7.28  | 9300  | 0.4611          | 0.9036   |
| 0.0007        | 7.36  | 9400  | 0.4608          | 0.9052   |
| 0.0007        | 7.44  | 9500  | 0.4623          | 0.9044   |
| 0.0005        | 7.52  | 9600  | 0.4621          | 0.9056   |
| 0.0005        | 7.6   | 9700  | 0.4615          | 0.9056   |
| 0.0005        | 7.67  | 9800  | 0.4612          | 0.9059   |
| 0.0005        | 7.75  | 9900  | 0.4626          | 0.9075   |
| 0.0004        | 7.83  | 10000 | 0.4626          | 0.9075   |
| 0.0005        | 7.91  | 10100 | 0.4626          | 0.9075   |
| 0.0006        | 7.99  | 10200 | 0.4626          | 0.9079   |


### 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_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\_8\_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: 0.4608
* Accuracy: 0.9052


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: 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: 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: 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: 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: 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: 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: 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