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_00001_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: 0.6316666666666667
---

<!-- 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_00001_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: 0.8246
- Accuracy: 0.6317

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2284        | 1.0   | 750   | 1.2241          | 0.3517   |
| 1.1457        | 2.0   | 1500  | 1.1930          | 0.365    |
| 1.1396        | 3.0   | 2250  | 1.1661          | 0.3783   |
| 1.0897        | 4.0   | 3000  | 1.1425          | 0.385    |
| 1.025         | 5.0   | 3750  | 1.1215          | 0.3883   |
| 1.0158        | 6.0   | 4500  | 1.1022          | 0.3883   |
| 0.9975        | 7.0   | 5250  | 1.0842          | 0.4017   |
| 1.0278        | 8.0   | 6000  | 1.0673          | 0.4067   |
| 0.9784        | 9.0   | 6750  | 1.0514          | 0.4133   |
| 0.9157        | 10.0  | 7500  | 1.0366          | 0.4317   |
| 0.9554        | 11.0  | 8250  | 1.0228          | 0.4467   |
| 0.8899        | 12.0  | 9000  | 1.0096          | 0.4667   |
| 0.9379        | 13.0  | 9750  | 0.9973          | 0.4767   |
| 0.944         | 14.0  | 10500 | 0.9856          | 0.4867   |
| 0.9071        | 15.0  | 11250 | 0.9745          | 0.4983   |
| 0.8922        | 16.0  | 12000 | 0.9641          | 0.505    |
| 0.8643        | 17.0  | 12750 | 0.9544          | 0.5133   |
| 0.8278        | 18.0  | 13500 | 0.9449          | 0.52     |
| 0.9039        | 19.0  | 14250 | 0.9361          | 0.5317   |
| 0.8559        | 20.0  | 15000 | 0.9279          | 0.5383   |
| 0.8179        | 21.0  | 15750 | 0.9199          | 0.545    |
| 0.8248        | 22.0  | 16500 | 0.9124          | 0.56     |
| 0.8379        | 23.0  | 17250 | 0.9052          | 0.56     |
| 0.864         | 24.0  | 18000 | 0.8985          | 0.565    |
| 0.8458        | 25.0  | 18750 | 0.8922          | 0.575    |
| 0.8014        | 26.0  | 19500 | 0.8861          | 0.5783   |
| 0.7589        | 27.0  | 20250 | 0.8805          | 0.5883   |
| 0.8089        | 28.0  | 21000 | 0.8752          | 0.595    |
| 0.8337        | 29.0  | 21750 | 0.8701          | 0.5983   |
| 0.7734        | 30.0  | 22500 | 0.8654          | 0.6033   |
| 0.7463        | 31.0  | 23250 | 0.8610          | 0.6033   |
| 0.7746        | 32.0  | 24000 | 0.8569          | 0.6067   |
| 0.8126        | 33.0  | 24750 | 0.8532          | 0.6117   |
| 0.7894        | 34.0  | 25500 | 0.8496          | 0.615    |
| 0.7634        | 35.0  | 26250 | 0.8463          | 0.615    |
| 0.7765        | 36.0  | 27000 | 0.8433          | 0.6167   |
| 0.8136        | 37.0  | 27750 | 0.8405          | 0.6217   |
| 0.8117        | 38.0  | 28500 | 0.8380          | 0.6217   |
| 0.7707        | 39.0  | 29250 | 0.8357          | 0.6217   |
| 0.7678        | 40.0  | 30000 | 0.8337          | 0.6267   |
| 0.7823        | 41.0  | 30750 | 0.8319          | 0.6283   |
| 0.7728        | 42.0  | 31500 | 0.8303          | 0.63     |
| 0.7705        | 43.0  | 32250 | 0.8289          | 0.6283   |
| 0.7342        | 44.0  | 33000 | 0.8277          | 0.6283   |
| 0.7107        | 45.0  | 33750 | 0.8267          | 0.6283   |
| 0.7263        | 46.0  | 34500 | 0.8259          | 0.63     |
| 0.7101        | 47.0  | 35250 | 0.8253          | 0.63     |
| 0.7724        | 48.0  | 36000 | 0.8249          | 0.6317   |
| 0.7714        | 49.0  | 36750 | 0.8247          | 0.6317   |
| 0.7461        | 50.0  | 37500 | 0.8246          | 0.6317   |


### Framework versions

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