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_fold3
  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: 0.8783333333333333
---

<!-- 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_fold3

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: 0.3045
- Accuracy: 0.8783

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9373        | 1.0   | 750   | 1.0091          | 0.4617   |
| 0.7639        | 2.0   | 1500  | 0.8536          | 0.6117   |
| 0.6803        | 3.0   | 2250  | 0.7396          | 0.6933   |
| 0.5905        | 4.0   | 3000  | 0.6588          | 0.75     |
| 0.5735        | 5.0   | 3750  | 0.5968          | 0.7833   |
| 0.5021        | 6.0   | 4500  | 0.5507          | 0.8017   |
| 0.4704        | 7.0   | 5250  | 0.5159          | 0.8133   |
| 0.4872        | 8.0   | 6000  | 0.4878          | 0.8267   |
| 0.4458        | 9.0   | 6750  | 0.4650          | 0.83     |
| 0.4154        | 10.0  | 7500  | 0.4469          | 0.8417   |
| 0.4321        | 11.0  | 8250  | 0.4318          | 0.845    |
| 0.3944        | 12.0  | 9000  | 0.4172          | 0.8433   |
| 0.3976        | 13.0  | 9750  | 0.4054          | 0.8483   |
| 0.4242        | 14.0  | 10500 | 0.3948          | 0.85     |
| 0.3817        | 15.0  | 11250 | 0.3850          | 0.8517   |
| 0.3695        | 16.0  | 12000 | 0.3777          | 0.8517   |
| 0.3394        | 17.0  | 12750 | 0.3711          | 0.8533   |
| 0.3418        | 18.0  | 13500 | 0.3639          | 0.8583   |
| 0.3927        | 19.0  | 14250 | 0.3584          | 0.8633   |
| 0.3355        | 20.0  | 15000 | 0.3536          | 0.8617   |
| 0.3182        | 21.0  | 15750 | 0.3485          | 0.86     |
| 0.3252        | 22.0  | 16500 | 0.3442          | 0.8617   |
| 0.3481        | 23.0  | 17250 | 0.3402          | 0.86     |
| 0.352         | 24.0  | 18000 | 0.3367          | 0.8617   |
| 0.3814        | 25.0  | 18750 | 0.3335          | 0.865    |
| 0.3436        | 26.0  | 19500 | 0.3305          | 0.865    |
| 0.2353        | 27.0  | 20250 | 0.3280          | 0.865    |
| 0.3097        | 28.0  | 21000 | 0.3253          | 0.8683   |
| 0.3673        | 29.0  | 21750 | 0.3232          | 0.8683   |
| 0.316         | 30.0  | 22500 | 0.3211          | 0.87     |
| 0.2736        | 31.0  | 23250 | 0.3193          | 0.8733   |
| 0.3111        | 32.0  | 24000 | 0.3172          | 0.875    |
| 0.3586        | 33.0  | 24750 | 0.3157          | 0.875    |
| 0.3482        | 34.0  | 25500 | 0.3143          | 0.875    |
| 0.2894        | 35.0  | 26250 | 0.3130          | 0.875    |
| 0.3247        | 36.0  | 27000 | 0.3121          | 0.8733   |
| 0.3266        | 37.0  | 27750 | 0.3109          | 0.8733   |
| 0.3501        | 38.0  | 28500 | 0.3098          | 0.8733   |
| 0.3018        | 39.0  | 29250 | 0.3089          | 0.875    |
| 0.3416        | 40.0  | 30000 | 0.3082          | 0.875    |
| 0.318         | 41.0  | 30750 | 0.3074          | 0.875    |
| 0.3558        | 42.0  | 31500 | 0.3067          | 0.8767   |
| 0.2993        | 43.0  | 32250 | 0.3061          | 0.8767   |
| 0.2907        | 44.0  | 33000 | 0.3056          | 0.8767   |
| 0.2783        | 45.0  | 33750 | 0.3053          | 0.8767   |
| 0.2937        | 46.0  | 34500 | 0.3050          | 0.8767   |
| 0.3037        | 47.0  | 35250 | 0.3048          | 0.8783   |
| 0.3233        | 48.0  | 36000 | 0.3046          | 0.8783   |
| 0.3074        | 49.0  | 36750 | 0.3045          | 0.8783   |
| 0.2861        | 50.0  | 37500 | 0.3045          | 0.8783   |


### Framework versions

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