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_sgd_0001_fold4
  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: 0.7983333333333333
---

<!-- 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_sgd_0001_fold4

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: 0.4817
- Accuracy: 0.7983

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1553        | 1.0   | 375   | 1.2548          | 0.355    |
| 1.0974        | 2.0   | 750   | 1.1671          | 0.3783   |
| 0.9812        | 3.0   | 1125  | 1.0817          | 0.4167   |
| 0.9358        | 4.0   | 1500  | 1.0016          | 0.455    |
| 0.8746        | 5.0   | 1875  | 0.9264          | 0.53     |
| 0.8021        | 6.0   | 2250  | 0.8621          | 0.5983   |
| 0.7908        | 7.0   | 2625  | 0.8069          | 0.6717   |
| 0.763         | 8.0   | 3000  | 0.7629          | 0.69     |
| 0.6997        | 9.0   | 3375  | 0.7270          | 0.7133   |
| 0.6962        | 10.0  | 3750  | 0.6950          | 0.7167   |
| 0.6391        | 11.0  | 4125  | 0.6689          | 0.7283   |
| 0.6231        | 12.0  | 4500  | 0.6467          | 0.735    |
| 0.6127        | 13.0  | 4875  | 0.6274          | 0.7483   |
| 0.6297        | 14.0  | 5250  | 0.6106          | 0.7517   |
| 0.6056        | 15.0  | 5625  | 0.5959          | 0.7633   |
| 0.5383        | 16.0  | 6000  | 0.5842          | 0.76     |
| 0.5862        | 17.0  | 6375  | 0.5727          | 0.76     |
| 0.5466        | 18.0  | 6750  | 0.5631          | 0.7683   |
| 0.6063        | 19.0  | 7125  | 0.5554          | 0.77     |
| 0.5382        | 20.0  | 7500  | 0.5477          | 0.7733   |
| 0.5719        | 21.0  | 7875  | 0.5406          | 0.7733   |
| 0.5194        | 22.0  | 8250  | 0.5342          | 0.7833   |
| 0.5408        | 23.0  | 8625  | 0.5290          | 0.7833   |
| 0.5327        | 24.0  | 9000  | 0.5248          | 0.7817   |
| 0.5341        | 25.0  | 9375  | 0.5207          | 0.7833   |
| 0.5248        | 26.0  | 9750  | 0.5168          | 0.7833   |
| 0.5823        | 27.0  | 10125 | 0.5128          | 0.7867   |
| 0.4919        | 28.0  | 10500 | 0.5098          | 0.79     |
| 0.4902        | 29.0  | 10875 | 0.5067          | 0.7933   |
| 0.5047        | 30.0  | 11250 | 0.5038          | 0.795    |
| 0.4943        | 31.0  | 11625 | 0.5008          | 0.7983   |
| 0.5058        | 32.0  | 12000 | 0.4990          | 0.7983   |
| 0.4976        | 33.0  | 12375 | 0.4965          | 0.7967   |
| 0.5168        | 34.0  | 12750 | 0.4952          | 0.795    |
| 0.5069        | 35.0  | 13125 | 0.4933          | 0.795    |
| 0.4844        | 36.0  | 13500 | 0.4915          | 0.7967   |
| 0.5181        | 37.0  | 13875 | 0.4900          | 0.7983   |
| 0.5125        | 38.0  | 14250 | 0.4886          | 0.7983   |
| 0.5414        | 39.0  | 14625 | 0.4875          | 0.7983   |
| 0.5265        | 40.0  | 15000 | 0.4865          | 0.7983   |
| 0.5089        | 41.0  | 15375 | 0.4855          | 0.7983   |
| 0.5041        | 42.0  | 15750 | 0.4845          | 0.7983   |
| 0.5029        | 43.0  | 16125 | 0.4836          | 0.7983   |
| 0.4723        | 44.0  | 16500 | 0.4830          | 0.7983   |
| 0.4754        | 45.0  | 16875 | 0.4827          | 0.7983   |
| 0.4906        | 46.0  | 17250 | 0.4823          | 0.7983   |
| 0.5249        | 47.0  | 17625 | 0.4820          | 0.7983   |
| 0.4858        | 48.0  | 18000 | 0.4818          | 0.7983   |
| 0.4635        | 49.0  | 18375 | 0.4818          | 0.7983   |
| 0.4753        | 50.0  | 18750 | 0.4817          | 0.7983   |


### Framework versions

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