transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_001_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: 0.7953410981697171
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_5x_beit_base_rms_001_fold2

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5865
- Accuracy: 0.7953

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0449        | 1.0   | 375   | 0.9946          | 0.4576   |
| 0.9062        | 2.0   | 750   | 0.8678          | 0.5341   |
| 0.8013        | 3.0   | 1125  | 1.1322          | 0.4709   |
| 0.7159        | 4.0   | 1500  | 0.7319          | 0.6373   |
| 0.717         | 5.0   | 1875  | 0.7090          | 0.6672   |
| 0.6942        | 6.0   | 2250  | 0.6958          | 0.6356   |
| 0.7767        | 7.0   | 2625  | 0.6812          | 0.7022   |
| 0.7025        | 8.0   | 3000  | 0.6844          | 0.6406   |
| 0.731         | 9.0   | 3375  | 0.6703          | 0.6872   |
| 0.712         | 10.0  | 3750  | 0.7094          | 0.6789   |
| 0.6865        | 11.0  | 4125  | 0.6498          | 0.6972   |
| 0.7524        | 12.0  | 4500  | 0.6865          | 0.6955   |
| 0.6624        | 13.0  | 4875  | 0.6872          | 0.6772   |
| 0.6979        | 14.0  | 5250  | 0.6496          | 0.6972   |
| 0.6174        | 15.0  | 5625  | 0.6736          | 0.6805   |
| 0.6379        | 16.0  | 6000  | 0.6464          | 0.6889   |
| 0.6532        | 17.0  | 6375  | 0.6449          | 0.7271   |
| 0.6218        | 18.0  | 6750  | 0.6026          | 0.7421   |
| 0.6018        | 19.0  | 7125  | 0.6684          | 0.6988   |
| 0.6058        | 20.0  | 7500  | 0.6198          | 0.7205   |
| 0.6269        | 21.0  | 7875  | 0.6185          | 0.7338   |
| 0.586         | 22.0  | 8250  | 0.5945          | 0.7571   |
| 0.6047        | 23.0  | 8625  | 0.5838          | 0.7404   |
| 0.5645        | 24.0  | 9000  | 0.5895          | 0.7304   |
| 0.5266        | 25.0  | 9375  | 0.6076          | 0.7554   |
| 0.5433        | 26.0  | 9750  | 0.6078          | 0.7205   |
| 0.6677        | 27.0  | 10125 | 0.5591          | 0.7837   |
| 0.5463        | 28.0  | 10500 | 0.6091          | 0.7488   |
| 0.5494        | 29.0  | 10875 | 0.5955          | 0.7471   |
| 0.4887        | 30.0  | 11250 | 0.5393          | 0.7987   |
| 0.5572        | 31.0  | 11625 | 0.5935          | 0.7537   |
| 0.5382        | 32.0  | 12000 | 0.6529          | 0.7288   |
| 0.5356        | 33.0  | 12375 | 0.5723          | 0.7787   |
| 0.5102        | 34.0  | 12750 | 0.5659          | 0.7720   |
| 0.5047        | 35.0  | 13125 | 0.5433          | 0.7887   |
| 0.4869        | 36.0  | 13500 | 0.5564          | 0.7687   |
| 0.4821        | 37.0  | 13875 | 0.5581          | 0.7754   |
| 0.455         | 38.0  | 14250 | 0.5595          | 0.7837   |
| 0.4345        | 39.0  | 14625 | 0.5481          | 0.7854   |
| 0.4695        | 40.0  | 15000 | 0.5459          | 0.8003   |
| 0.4129        | 41.0  | 15375 | 0.5458          | 0.8020   |
| 0.4369        | 42.0  | 15750 | 0.5508          | 0.7953   |
| 0.4043        | 43.0  | 16125 | 0.5495          | 0.7854   |
| 0.4715        | 44.0  | 16500 | 0.5470          | 0.7987   |
| 0.4036        | 45.0  | 16875 | 0.5777          | 0.7887   |
| 0.3786        | 46.0  | 17250 | 0.5867          | 0.8003   |
| 0.4177        | 47.0  | 17625 | 0.5806          | 0.7770   |
| 0.3538        | 48.0  | 18000 | 0.5857          | 0.7937   |
| 0.3987        | 49.0  | 18375 | 0.5813          | 0.8020   |
| 0.3452        | 50.0  | 18750 | 0.5865          | 0.7953   |


### Framework versions

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