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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_16_e4
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_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
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