---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-tekno24
  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. -->

# beit-base-patch16-224-pt22k-ft22k-finetuned-tekno24

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0072
- Accuracy: 0.5785
- F1: 0.5643
- Precision: 0.5602
- Recall: 0.5785

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.4008        | 0.9855  | 17   | 1.2967          | 0.4059   | 0.3220 | 0.3791    | 0.4059 |
| 1.2363        | 1.9710  | 34   | 1.1309          | 0.5032   | 0.4187 | 0.4871    | 0.5032 |
| 1.1716        | 2.9565  | 51   | 1.0983          | 0.5161   | 0.4385 | 0.4610    | 0.5161 |
| 1.1479        | 4.0     | 69   | 1.0550          | 0.5409   | 0.5014 | 0.5067    | 0.5409 |
| 1.1058        | 4.9855  | 86   | 1.0397          | 0.5500   | 0.4942 | 0.5208    | 0.5500 |
| 1.0656        | 5.9710  | 103  | 1.0558          | 0.5556   | 0.5396 | 0.5486    | 0.5556 |
| 1.0328        | 6.9565  | 120  | 1.0216          | 0.5730   | 0.5465 | 0.5513    | 0.5730 |
| 1.0116        | 8.0     | 138  | 1.0469          | 0.5363   | 0.5187 | 0.5119    | 0.5363 |
| 1.012         | 8.9855  | 155  | 1.0216          | 0.5629   | 0.5226 | 0.5344    | 0.5629 |
| 1.0076        | 9.9710  | 172  | 1.0186          | 0.5675   | 0.5275 | 0.5379    | 0.5675 |
| 0.9714        | 10.9565 | 189  | 1.0205          | 0.5638   | 0.5499 | 0.5549    | 0.5638 |
| 0.9843        | 12.0    | 207  | 1.0117          | 0.5657   | 0.5488 | 0.5495    | 0.5657 |
| 0.9427        | 12.9855 | 224  | 1.0072          | 0.5785   | 0.5643 | 0.5602    | 0.5785 |
| 0.9268        | 13.9710 | 241  | 1.0068          | 0.5785   | 0.5652 | 0.5621    | 0.5785 |
| 0.9525        | 14.7826 | 255  | 1.0073          | 0.5785   | 0.5641 | 0.5641    | 0.5785 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1



![image/png](https://cdn-uploads.huggingface.co/production/uploads/662eb39820de310d1558dd55/c2oZGFL696PUZQSsirk0-.png)


![image/png](https://cdn-uploads.huggingface.co/production/uploads/662eb39820de310d1558dd55/hSI48y0RC_62394MbCCRl.png)