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_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: 0.8768718801996672
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_10x_beit_large_sgd_0001_fold2

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: 0.3022
- Accuracy: 0.8769

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9337        | 1.0   | 750   | 0.9902          | 0.5025   |
| 0.7559        | 2.0   | 1500  | 0.8323          | 0.6206   |
| 0.6418        | 3.0   | 2250  | 0.7119          | 0.7205   |
| 0.6498        | 4.0   | 3000  | 0.6261          | 0.7737   |
| 0.5308        | 5.0   | 3750  | 0.5616          | 0.8020   |
| 0.5189        | 6.0   | 4500  | 0.5157          | 0.8186   |
| 0.4977        | 7.0   | 5250  | 0.4808          | 0.8303   |
| 0.4495        | 8.0   | 6000  | 0.4552          | 0.8369   |
| 0.4544        | 9.0   | 6750  | 0.4332          | 0.8303   |
| 0.4325        | 10.0  | 7500  | 0.4166          | 0.8336   |
| 0.4708        | 11.0  | 8250  | 0.4025          | 0.8419   |
| 0.4375        | 12.0  | 9000  | 0.3904          | 0.8419   |
| 0.3875        | 13.0  | 9750  | 0.3796          | 0.8486   |
| 0.338         | 14.0  | 10500 | 0.3718          | 0.8486   |
| 0.3613        | 15.0  | 11250 | 0.3643          | 0.8502   |
| 0.3159        | 16.0  | 12000 | 0.3576          | 0.8569   |
| 0.313         | 17.0  | 12750 | 0.3520          | 0.8602   |
| 0.3243        | 18.0  | 13500 | 0.3466          | 0.8619   |
| 0.3747        | 19.0  | 14250 | 0.3420          | 0.8619   |
| 0.3494        | 20.0  | 15000 | 0.3382          | 0.8652   |
| 0.3628        | 21.0  | 15750 | 0.3347          | 0.8652   |
| 0.2681        | 22.0  | 16500 | 0.3313          | 0.8686   |
| 0.3103        | 23.0  | 17250 | 0.3283          | 0.8686   |
| 0.3029        | 24.0  | 18000 | 0.3255          | 0.8686   |
| 0.3439        | 25.0  | 18750 | 0.3228          | 0.8686   |
| 0.363         | 26.0  | 19500 | 0.3205          | 0.8735   |
| 0.3457        | 27.0  | 20250 | 0.3186          | 0.8735   |
| 0.3118        | 28.0  | 21000 | 0.3168          | 0.8719   |
| 0.3203        | 29.0  | 21750 | 0.3151          | 0.8719   |
| 0.2897        | 30.0  | 22500 | 0.3135          | 0.8702   |
| 0.3287        | 31.0  | 23250 | 0.3118          | 0.8702   |
| 0.3672        | 32.0  | 24000 | 0.3107          | 0.8719   |
| 0.3139        | 33.0  | 24750 | 0.3101          | 0.8702   |
| 0.3173        | 34.0  | 25500 | 0.3088          | 0.8719   |
| 0.3321        | 35.0  | 26250 | 0.3079          | 0.8735   |
| 0.3146        | 36.0  | 27000 | 0.3071          | 0.8735   |
| 0.3221        | 37.0  | 27750 | 0.3062          | 0.8735   |
| 0.2973        | 38.0  | 28500 | 0.3058          | 0.8752   |
| 0.275         | 39.0  | 29250 | 0.3050          | 0.8752   |
| 0.3603        | 40.0  | 30000 | 0.3045          | 0.8752   |
| 0.3249        | 41.0  | 30750 | 0.3040          | 0.8752   |
| 0.3107        | 42.0  | 31500 | 0.3036          | 0.8752   |
| 0.2783        | 43.0  | 32250 | 0.3032          | 0.8752   |
| 0.2901        | 44.0  | 33000 | 0.3029          | 0.8752   |
| 0.3257        | 45.0  | 33750 | 0.3026          | 0.8752   |
| 0.2732        | 46.0  | 34500 | 0.3025          | 0.8752   |
| 0.3622        | 47.0  | 35250 | 0.3024          | 0.8769   |
| 0.3082        | 48.0  | 36000 | 0.3023          | 0.8769   |
| 0.2937        | 49.0  | 36750 | 0.3022          | 0.8769   |
| 0.3097        | 50.0  | 37500 | 0.3022          | 0.8769   |


### Framework versions

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