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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_16_e8
results: []
---
<!-- 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_16_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: 0.3951
- Accuracy: 0.9129
## 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: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8115 | 0.16 | 100 | 3.7948 | 0.1862 |
| 3.1194 | 0.31 | 200 | 3.0120 | 0.3281 |
| 2.3703 | 0.47 | 300 | 2.4791 | 0.4426 |
| 2.07 | 0.63 | 400 | 2.1720 | 0.5 |
| 1.6847 | 0.78 | 500 | 1.7291 | 0.5956 |
| 1.3821 | 0.94 | 600 | 1.4777 | 0.6299 |
| 0.9498 | 1.1 | 700 | 1.2935 | 0.6681 |
| 0.8741 | 1.25 | 800 | 1.1353 | 0.7051 |
| 0.8875 | 1.41 | 900 | 0.9951 | 0.7448 |
| 0.7233 | 1.56 | 1000 | 0.9265 | 0.7487 |
| 0.6696 | 1.72 | 1100 | 0.8660 | 0.7625 |
| 0.7364 | 1.88 | 1200 | 0.8710 | 0.7579 |
| 0.3933 | 2.03 | 1300 | 0.7162 | 0.8038 |
| 0.3443 | 2.19 | 1400 | 0.6305 | 0.8300 |
| 0.3376 | 2.35 | 1500 | 0.6273 | 0.8315 |
| 0.3071 | 2.5 | 1600 | 0.5988 | 0.8319 |
| 0.2863 | 2.66 | 1700 | 0.6731 | 0.8153 |
| 0.3017 | 2.82 | 1800 | 0.6042 | 0.8315 |
| 0.2382 | 2.97 | 1900 | 0.5118 | 0.8712 |
| 0.1578 | 3.13 | 2000 | 0.4917 | 0.8736 |
| 0.1794 | 3.29 | 2100 | 0.5302 | 0.8631 |
| 0.1093 | 3.44 | 2200 | 0.5035 | 0.8635 |
| 0.1076 | 3.6 | 2300 | 0.5186 | 0.8674 |
| 0.1219 | 3.76 | 2400 | 0.4723 | 0.8801 |
| 0.1017 | 3.91 | 2500 | 0.5132 | 0.8712 |
| 0.0351 | 4.07 | 2600 | 0.4709 | 0.8728 |
| 0.0295 | 4.23 | 2700 | 0.4674 | 0.8824 |
| 0.0416 | 4.38 | 2800 | 0.4836 | 0.8805 |
| 0.0386 | 4.54 | 2900 | 0.4663 | 0.8828 |
| 0.0392 | 4.69 | 3000 | 0.4003 | 0.8990 |
| 0.0383 | 4.85 | 3100 | 0.4187 | 0.8948 |
| 0.0624 | 5.01 | 3200 | 0.4460 | 0.8874 |
| 0.0188 | 5.16 | 3300 | 0.4169 | 0.9029 |
| 0.0174 | 5.32 | 3400 | 0.4098 | 0.8951 |
| 0.0257 | 5.48 | 3500 | 0.4289 | 0.8951 |
| 0.0123 | 5.63 | 3600 | 0.4295 | 0.9029 |
| 0.0052 | 5.79 | 3700 | 0.4395 | 0.8994 |
| 0.0081 | 5.95 | 3800 | 0.4217 | 0.9082 |
| 0.0032 | 6.1 | 3900 | 0.4216 | 0.9056 |
| 0.0033 | 6.26 | 4000 | 0.4113 | 0.9082 |
| 0.0024 | 6.42 | 4100 | 0.4060 | 0.9102 |
| 0.0022 | 6.57 | 4200 | 0.4067 | 0.9090 |
| 0.0031 | 6.73 | 4300 | 0.4005 | 0.9113 |
| 0.0021 | 6.89 | 4400 | 0.4008 | 0.9129 |
| 0.0021 | 7.04 | 4500 | 0.3967 | 0.9113 |
| 0.0043 | 7.2 | 4600 | 0.3960 | 0.9121 |
| 0.0022 | 7.36 | 4700 | 0.3962 | 0.9125 |
| 0.0021 | 7.51 | 4800 | 0.3992 | 0.9121 |
| 0.002 | 7.67 | 4900 | 0.3951 | 0.9129 |
| 0.0023 | 7.82 | 5000 | 0.3952 | 0.9125 |
| 0.0021 | 7.98 | 5100 | 0.3952 | 0.9129 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
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