transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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_sgd_0001_fold5
  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: 0.8733333333333333
---

<!-- 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_sgd_0001_fold5

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: 0.3210
- Accuracy: 0.8733

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9567        | 1.0   | 750   | 1.0187          | 0.4617   |
| 0.813         | 2.0   | 1500  | 0.8588          | 0.6033   |
| 0.7071        | 3.0   | 2250  | 0.7412          | 0.6717   |
| 0.6056        | 4.0   | 3000  | 0.6548          | 0.7317   |
| 0.553         | 5.0   | 3750  | 0.5916          | 0.7767   |
| 0.5415        | 6.0   | 4500  | 0.5456          | 0.7983   |
| 0.4714        | 7.0   | 5250  | 0.5118          | 0.8083   |
| 0.4919        | 8.0   | 6000  | 0.4844          | 0.8133   |
| 0.4714        | 9.0   | 6750  | 0.4633          | 0.8167   |
| 0.408         | 10.0  | 7500  | 0.4458          | 0.8267   |
| 0.416         | 11.0  | 8250  | 0.4326          | 0.8317   |
| 0.4057        | 12.0  | 9000  | 0.4197          | 0.84     |
| 0.4411        | 13.0  | 9750  | 0.4091          | 0.8383   |
| 0.3787        | 14.0  | 10500 | 0.3999          | 0.84     |
| 0.4112        | 15.0  | 11250 | 0.3917          | 0.8433   |
| 0.3272        | 16.0  | 12000 | 0.3857          | 0.8433   |
| 0.3453        | 17.0  | 12750 | 0.3795          | 0.8467   |
| 0.2978        | 18.0  | 13500 | 0.3732          | 0.8467   |
| 0.3695        | 19.0  | 14250 | 0.3692          | 0.8533   |
| 0.3546        | 20.0  | 15000 | 0.3643          | 0.855    |
| 0.3274        | 21.0  | 15750 | 0.3603          | 0.8583   |
| 0.3708        | 22.0  | 16500 | 0.3566          | 0.8583   |
| 0.3177        | 23.0  | 17250 | 0.3530          | 0.8617   |
| 0.3259        | 24.0  | 18000 | 0.3501          | 0.865    |
| 0.3343        | 25.0  | 18750 | 0.3473          | 0.8683   |
| 0.3365        | 26.0  | 19500 | 0.3445          | 0.865    |
| 0.2524        | 27.0  | 20250 | 0.3419          | 0.865    |
| 0.3298        | 28.0  | 21000 | 0.3396          | 0.8667   |
| 0.3375        | 29.0  | 21750 | 0.3374          | 0.8667   |
| 0.3203        | 30.0  | 22500 | 0.3355          | 0.8683   |
| 0.2843        | 31.0  | 23250 | 0.3334          | 0.8683   |
| 0.3065        | 32.0  | 24000 | 0.3325          | 0.8667   |
| 0.3385        | 33.0  | 24750 | 0.3310          | 0.8717   |
| 0.2656        | 34.0  | 25500 | 0.3296          | 0.8717   |
| 0.3103        | 35.0  | 26250 | 0.3282          | 0.8733   |
| 0.3336        | 36.0  | 27000 | 0.3274          | 0.8717   |
| 0.2743        | 37.0  | 27750 | 0.3265          | 0.8733   |
| 0.3245        | 38.0  | 28500 | 0.3255          | 0.8717   |
| 0.321         | 39.0  | 29250 | 0.3249          | 0.8733   |
| 0.2652        | 40.0  | 30000 | 0.3240          | 0.8733   |
| 0.2925        | 41.0  | 30750 | 0.3236          | 0.875    |
| 0.3072        | 42.0  | 31500 | 0.3229          | 0.875    |
| 0.3317        | 43.0  | 32250 | 0.3226          | 0.875    |
| 0.2932        | 44.0  | 33000 | 0.3221          | 0.875    |
| 0.3178        | 45.0  | 33750 | 0.3218          | 0.8733   |
| 0.2606        | 46.0  | 34500 | 0.3214          | 0.875    |
| 0.3688        | 47.0  | 35250 | 0.3212          | 0.875    |
| 0.2811        | 48.0  | 36000 | 0.3211          | 0.8733   |
| 0.3003        | 49.0  | 36750 | 0.3211          | 0.8733   |
| 0.2418        | 50.0  | 37500 | 0.3210          | 0.8733   |


### Framework versions

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