---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper2_mesum5
  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. -->

# exper2_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: 3.4589
- Accuracy: 0.1308

## 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: 0.002
- 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: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.4265        | 0.23  | 100  | 4.3676          | 0.0296   |
| 4.1144        | 0.47  | 200  | 4.1606          | 0.0544   |
| 4.0912        | 0.7   | 300  | 4.1071          | 0.0509   |
| 4.0361        | 0.93  | 400  | 4.0625          | 0.0669   |
| 4.0257        | 1.16  | 500  | 3.9682          | 0.0822   |
| 3.8846        | 1.4   | 600  | 3.9311          | 0.0834   |
| 3.9504        | 1.63  | 700  | 3.9255          | 0.0698   |
| 3.9884        | 1.86  | 800  | 3.9404          | 0.0722   |
| 3.7191        | 2.09  | 900  | 3.8262          | 0.0935   |
| 3.7952        | 2.33  | 1000 | 3.8236          | 0.0734   |
| 3.8085        | 2.56  | 1100 | 3.7694          | 0.0964   |
| 3.7535        | 2.79  | 1200 | 3.6757          | 0.1059   |
| 3.4218        | 3.02  | 1300 | 3.6474          | 0.1095   |
| 3.5172        | 3.26  | 1400 | 3.5621          | 0.1166   |
| 3.5173        | 3.49  | 1500 | 3.5579          | 0.1207   |
| 3.4346        | 3.72  | 1600 | 3.4817          | 0.1249   |
| 3.3995        | 3.95  | 1700 | 3.4589          | 0.1308   |


### Framework versions

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