---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_adamax_00001_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: 0.8951747088186356
---

<!-- 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_3x_beit_base_adamax_00001_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: 0.8293
- Accuracy: 0.8952

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3036        | 1.0   | 375   | 0.2703          | 0.8918   |
| 0.2118        | 2.0   | 750   | 0.2674          | 0.8968   |
| 0.1557        | 3.0   | 1125  | 0.2889          | 0.8918   |
| 0.074         | 4.0   | 1500  | 0.2842          | 0.9002   |
| 0.0616        | 5.0   | 1875  | 0.3403          | 0.8935   |
| 0.036         | 6.0   | 2250  | 0.3534          | 0.9101   |
| 0.0382        | 7.0   | 2625  | 0.4309          | 0.8985   |
| 0.0686        | 8.0   | 3000  | 0.4834          | 0.8985   |
| 0.022         | 9.0   | 3375  | 0.5298          | 0.8935   |
| 0.0159        | 10.0  | 3750  | 0.5866          | 0.8985   |
| 0.0173        | 11.0  | 4125  | 0.5611          | 0.8968   |
| 0.0241        | 12.0  | 4500  | 0.6961          | 0.8869   |
| 0.0125        | 13.0  | 4875  | 0.6251          | 0.8952   |
| 0.0052        | 14.0  | 5250  | 0.6175          | 0.9002   |
| 0.0252        | 15.0  | 5625  | 0.6443          | 0.8935   |
| 0.0003        | 16.0  | 6000  | 0.6752          | 0.8952   |
| 0.0517        | 17.0  | 6375  | 0.6928          | 0.8985   |
| 0.0082        | 18.0  | 6750  | 0.6809          | 0.8985   |
| 0.0008        | 19.0  | 7125  | 0.7189          | 0.8935   |
| 0.012         | 20.0  | 7500  | 0.7838          | 0.9002   |
| 0.0206        | 21.0  | 7875  | 0.7183          | 0.8968   |
| 0.0006        | 22.0  | 8250  | 0.7126          | 0.9085   |
| 0.0002        | 23.0  | 8625  | 0.7379          | 0.8985   |
| 0.0002        | 24.0  | 9000  | 0.7747          | 0.8952   |
| 0.0001        | 25.0  | 9375  | 0.7907          | 0.8869   |
| 0.0001        | 26.0  | 9750  | 0.7652          | 0.8985   |
| 0.0153        | 27.0  | 10125 | 0.8239          | 0.8935   |
| 0.002         | 28.0  | 10500 | 0.7554          | 0.9018   |
| 0.0228        | 29.0  | 10875 | 0.8026          | 0.9002   |
| 0.0049        | 30.0  | 11250 | 0.7927          | 0.9052   |
| 0.003         | 31.0  | 11625 | 0.8114          | 0.8968   |
| 0.0086        | 32.0  | 12000 | 0.8422          | 0.8935   |
| 0.0066        | 33.0  | 12375 | 0.8193          | 0.8935   |
| 0.0014        | 34.0  | 12750 | 0.8462          | 0.9002   |
| 0.0005        | 35.0  | 13125 | 0.8418          | 0.8902   |
| 0.0031        | 36.0  | 13500 | 0.8633          | 0.8918   |
| 0.0051        | 37.0  | 13875 | 0.8436          | 0.8918   |
| 0.0003        | 38.0  | 14250 | 0.8576          | 0.8902   |
| 0.0037        | 39.0  | 14625 | 0.8301          | 0.8902   |
| 0.0238        | 40.0  | 15000 | 0.8339          | 0.8952   |
| 0.0147        | 41.0  | 15375 | 0.8449          | 0.8968   |
| 0.011         | 42.0  | 15750 | 0.8207          | 0.8968   |
| 0.0179        | 43.0  | 16125 | 0.8212          | 0.8968   |
| 0.0045        | 44.0  | 16500 | 0.8067          | 0.9018   |
| 0.023         | 45.0  | 16875 | 0.8396          | 0.8918   |
| 0.027         | 46.0  | 17250 | 0.8319          | 0.8935   |
| 0.0247        | 47.0  | 17625 | 0.8325          | 0.8935   |
| 0.019         | 48.0  | 18000 | 0.8318          | 0.8952   |
| 0.0136        | 49.0  | 18375 | 0.8279          | 0.8952   |
| 0.0009        | 50.0  | 18750 | 0.8293          | 0.8952   |


### Framework versions

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