---
base_model: google/vit-base-patch16-224-in21k
library_name: peft
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: vit-base-patch16-224-in21k-finetuned-tekno23
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-in21k-finetuned-tekno23

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: 1.2583
- Accuracy: 0.4014
- F1: 0.3092
- Precision: 0.3870
- Recall: 0.4014

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3662        | 1.0   | 421  | 1.3619          | 0.3190   | 0.1618 | 0.2852    | 0.3190 |
| 1.3095        | 2.0   | 842  | 1.2970          | 0.3962   | 0.2933 | 0.4359    | 0.3962 |
| 1.2683        | 3.0   | 1263 | 1.2687          | 0.4020   | 0.3081 | 0.3550    | 0.4020 |
| 1.2524        | 4.0   | 1684 | 1.2576          | 0.4057   | 0.3098 | 0.3951    | 0.4057 |
| 1.2781        | 5.0   | 2105 | 1.2583          | 0.4014   | 0.3092 | 0.3870    | 0.4014 |


### Framework versions

- PEFT 0.12.1.dev0
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1