---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_adamax_00001_fold4
  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: 0.8933333333333333
---

<!-- 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_10x_beit_large_adamax_00001_fold4

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1774
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.174         | 1.0   | 750   | 0.3318          | 0.8817   |
| 0.0694        | 2.0   | 1500  | 0.3979          | 0.8833   |
| 0.0385        | 3.0   | 2250  | 0.6069          | 0.8817   |
| 0.0028        | 4.0   | 3000  | 0.7041          | 0.8767   |
| 0.0151        | 5.0   | 3750  | 0.8263          | 0.8783   |
| 0.017         | 6.0   | 4500  | 0.8468          | 0.8917   |
| 0.0004        | 7.0   | 5250  | 0.9156          | 0.8817   |
| 0.0149        | 8.0   | 6000  | 0.9947          | 0.8883   |
| 0.0019        | 9.0   | 6750  | 0.9986          | 0.8833   |
| 0.0           | 10.0  | 7500  | 1.0174          | 0.89     |
| 0.0002        | 11.0  | 8250  | 1.0347          | 0.8983   |
| 0.0006        | 12.0  | 9000  | 1.1212          | 0.8883   |
| 0.0007        | 13.0  | 9750  | 1.1145          | 0.9      |
| 0.002         | 14.0  | 10500 | 1.1511          | 0.895    |
| 0.0113        | 15.0  | 11250 | 1.1891          | 0.8833   |
| 0.0193        | 16.0  | 12000 | 1.1467          | 0.8833   |
| 0.0           | 17.0  | 12750 | 1.2067          | 0.8833   |
| 0.0           | 18.0  | 13500 | 1.1030          | 0.8917   |
| 0.0           | 19.0  | 14250 | 1.2269          | 0.8817   |
| 0.0           | 20.0  | 15000 | 1.2142          | 0.8983   |
| 0.0           | 21.0  | 15750 | 1.2333          | 0.8833   |
| 0.0           | 22.0  | 16500 | 1.2215          | 0.89     |
| 0.0           | 23.0  | 17250 | 1.1755          | 0.88     |
| 0.0001        | 24.0  | 18000 | 1.2025          | 0.89     |
| 0.0           | 25.0  | 18750 | 1.1234          | 0.8967   |
| 0.0           | 26.0  | 19500 | 1.1299          | 0.8933   |
| 0.0           | 27.0  | 20250 | 1.1278          | 0.8933   |
| 0.0           | 28.0  | 21000 | 1.1853          | 0.89     |
| 0.0           | 29.0  | 21750 | 1.1366          | 0.8967   |
| 0.0           | 30.0  | 22500 | 1.2109          | 0.8817   |
| 0.0           | 31.0  | 23250 | 1.2247          | 0.88     |
| 0.0124        | 32.0  | 24000 | 1.2057          | 0.885    |
| 0.0           | 33.0  | 24750 | 1.2082          | 0.8933   |
| 0.0           | 34.0  | 25500 | 1.1875          | 0.8933   |
| 0.0           | 35.0  | 26250 | 1.1823          | 0.8983   |
| 0.0           | 36.0  | 27000 | 1.1794          | 0.8883   |
| 0.0           | 37.0  | 27750 | 1.1760          | 0.8917   |
| 0.0           | 38.0  | 28500 | 1.1363          | 0.895    |
| 0.0           | 39.0  | 29250 | 1.1574          | 0.895    |
| 0.0           | 40.0  | 30000 | 1.1725          | 0.8933   |
| 0.0           | 41.0  | 30750 | 1.1844          | 0.8867   |
| 0.0           | 42.0  | 31500 | 1.1542          | 0.8933   |
| 0.0           | 43.0  | 32250 | 1.1472          | 0.895    |
| 0.0           | 44.0  | 33000 | 1.1640          | 0.8917   |
| 0.0           | 45.0  | 33750 | 1.1642          | 0.89     |
| 0.0           | 46.0  | 34500 | 1.1680          | 0.8933   |
| 0.0           | 47.0  | 35250 | 1.1880          | 0.895    |
| 0.0           | 48.0  | 36000 | 1.1744          | 0.8933   |
| 0.0           | 49.0  | 36750 | 1.1763          | 0.8933   |
| 0.0008        | 50.0  | 37500 | 1.1774          | 0.8933   |


### Framework versions

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