---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_001_fold2
  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: 0.8851913477537438
---

<!-- 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. -->

# smids_5x_beit_base_sgd_001_fold2

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2987
- Accuracy: 0.8852

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6816        | 1.0   | 375   | 0.6833          | 0.7338   |
| 0.5586        | 2.0   | 750   | 0.5192          | 0.7987   |
| 0.4343        | 3.0   | 1125  | 0.4591          | 0.8037   |
| 0.3314        | 4.0   | 1500  | 0.4185          | 0.8270   |
| 0.3478        | 5.0   | 1875  | 0.4048          | 0.8369   |
| 0.3776        | 6.0   | 2250  | 0.3805          | 0.8336   |
| 0.3391        | 7.0   | 2625  | 0.3595          | 0.8403   |
| 0.3304        | 8.0   | 3000  | 0.3500          | 0.8469   |
| 0.2847        | 9.0   | 3375  | 0.3354          | 0.8602   |
| 0.2987        | 10.0  | 3750  | 0.3303          | 0.8519   |
| 0.2929        | 11.0  | 4125  | 0.3216          | 0.8669   |
| 0.3369        | 12.0  | 4500  | 0.3243          | 0.8569   |
| 0.2344        | 13.0  | 4875  | 0.3140          | 0.8619   |
| 0.2532        | 14.0  | 5250  | 0.3121          | 0.8669   |
| 0.2563        | 15.0  | 5625  | 0.3059          | 0.8769   |
| 0.2186        | 16.0  | 6000  | 0.3073          | 0.8652   |
| 0.3036        | 17.0  | 6375  | 0.3090          | 0.8669   |
| 0.2499        | 18.0  | 6750  | 0.3061          | 0.8719   |
| 0.2571        | 19.0  | 7125  | 0.2980          | 0.8752   |
| 0.287         | 20.0  | 7500  | 0.3028          | 0.8719   |
| 0.2417        | 21.0  | 7875  | 0.2951          | 0.8819   |
| 0.2578        | 22.0  | 8250  | 0.2968          | 0.8769   |
| 0.2485        | 23.0  | 8625  | 0.3021          | 0.8752   |
| 0.2288        | 24.0  | 9000  | 0.2899          | 0.8852   |
| 0.1978        | 25.0  | 9375  | 0.3001          | 0.8785   |
| 0.2856        | 26.0  | 9750  | 0.2985          | 0.8819   |
| 0.2426        | 27.0  | 10125 | 0.2936          | 0.8852   |
| 0.222         | 28.0  | 10500 | 0.2944          | 0.8852   |
| 0.2193        | 29.0  | 10875 | 0.2940          | 0.8802   |
| 0.2438        | 30.0  | 11250 | 0.2935          | 0.8802   |
| 0.1916        | 31.0  | 11625 | 0.2931          | 0.8735   |
| 0.2423        | 32.0  | 12000 | 0.2890          | 0.8869   |
| 0.207         | 33.0  | 12375 | 0.2979          | 0.8769   |
| 0.1912        | 34.0  | 12750 | 0.3002          | 0.8885   |
| 0.2247        | 35.0  | 13125 | 0.2940          | 0.8819   |
| 0.1554        | 36.0  | 13500 | 0.2936          | 0.8819   |
| 0.1927        | 37.0  | 13875 | 0.2998          | 0.8769   |
| 0.1704        | 38.0  | 14250 | 0.2978          | 0.8835   |
| 0.2119        | 39.0  | 14625 | 0.3017          | 0.8769   |
| 0.1926        | 40.0  | 15000 | 0.2970          | 0.8819   |
| 0.1898        | 41.0  | 15375 | 0.3000          | 0.8785   |
| 0.2087        | 42.0  | 15750 | 0.2990          | 0.8819   |
| 0.1865        | 43.0  | 16125 | 0.2976          | 0.8835   |
| 0.2027        | 44.0  | 16500 | 0.3000          | 0.8819   |
| 0.1873        | 45.0  | 16875 | 0.2993          | 0.8835   |
| 0.2551        | 46.0  | 17250 | 0.2983          | 0.8819   |
| 0.227         | 47.0  | 17625 | 0.2985          | 0.8852   |
| 0.2135        | 48.0  | 18000 | 0.3011          | 0.8785   |
| 0.2131        | 49.0  | 18375 | 0.2986          | 0.8852   |
| 0.2054        | 50.0  | 18750 | 0.2987          | 0.8852   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2