---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ViT_Cucumber
  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. -->

# ViT_Cucumber

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0155
- Accuracy: 0.9976

## 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: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1694        | 0.3571 | 100  | 0.1965          | 0.9607   |
| 0.1409        | 0.7143 | 200  | 0.2409          | 0.9261   |
| 0.1024        | 1.0714 | 300  | 0.0903          | 0.9780   |
| 0.0326        | 1.4286 | 400  | 0.0630          | 0.9866   |
| 0.0338        | 1.7857 | 500  | 0.0675          | 0.9843   |
| 0.0082        | 2.1429 | 600  | 0.0508          | 0.9882   |
| 0.0072        | 2.5    | 700  | 0.0609          | 0.9874   |
| 0.0056        | 2.8571 | 800  | 0.0175          | 0.9976   |
| 0.0044        | 3.2143 | 900  | 0.0154          | 0.9976   |
| 0.0042        | 3.5714 | 1000 | 0.0151          | 0.9976   |
| 0.0045        | 3.9286 | 1100 | 0.0155          | 0.9976   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.3.0+cu118
- Datasets 3.2.0
- Tokenizers 0.19.1