---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_beit_base_f5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9512195121951219
---

<!-- 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. -->

# hushem_40x_beit_base_f5

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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2681
- Accuracy: 0.9512

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0802        | 1.0   | 110  | 0.6945          | 0.8049   |
| 0.028         | 2.0   | 220  | 0.5751          | 0.9024   |
| 0.002         | 3.0   | 330  | 0.3641          | 0.9268   |
| 0.0009        | 4.0   | 440  | 0.5616          | 0.8780   |
| 0.0004        | 5.0   | 550  | 0.2822          | 0.9024   |
| 0.0003        | 6.0   | 660  | 0.7387          | 0.8537   |
| 0.0018        | 7.0   | 770  | 0.1999          | 0.9512   |
| 0.0001        | 8.0   | 880  | 0.3046          | 0.9512   |
| 0.0011        | 9.0   | 990  | 0.2897          | 0.9268   |
| 0.0002        | 10.0  | 1100 | 0.2681          | 0.9512   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1