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_adamax_00001_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: 0.8866666666666667
---

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# smids_5x_beit_base_adamax_00001_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: 1.0773
- Accuracy: 0.8867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2789        | 1.0   | 375   | 0.3658          | 0.8517   |
| 0.2051        | 2.0   | 750   | 0.3746          | 0.855    |
| 0.0849        | 3.0   | 1125  | 0.3986          | 0.87     |
| 0.1357        | 4.0   | 1500  | 0.4367          | 0.8633   |
| 0.0524        | 5.0   | 1875  | 0.4518          | 0.8867   |
| 0.0622        | 6.0   | 2250  | 0.5510          | 0.8867   |
| 0.0413        | 7.0   | 2625  | 0.6374          | 0.8767   |
| 0.0498        | 8.0   | 3000  | 0.6614          | 0.88     |
| 0.0181        | 9.0   | 3375  | 0.6937          | 0.8867   |
| 0.013         | 10.0  | 3750  | 0.7663          | 0.8817   |
| 0.0052        | 11.0  | 4125  | 0.8001          | 0.8783   |
| 0.0189        | 12.0  | 4500  | 0.8578          | 0.8833   |
| 0.0009        | 13.0  | 4875  | 0.8974          | 0.88     |
| 0.0122        | 14.0  | 5250  | 0.9013          | 0.885    |
| 0.0628        | 15.0  | 5625  | 0.9434          | 0.875    |
| 0.0114        | 16.0  | 6000  | 0.9650          | 0.8733   |
| 0.0367        | 17.0  | 6375  | 0.9453          | 0.885    |
| 0.0138        | 18.0  | 6750  | 0.9235          | 0.8833   |
| 0.0185        | 19.0  | 7125  | 0.9504          | 0.8783   |
| 0.0147        | 20.0  | 7500  | 0.9928          | 0.8883   |
| 0.0067        | 21.0  | 7875  | 0.9826          | 0.8867   |
| 0.0174        | 22.0  | 8250  | 1.0361          | 0.8817   |
| 0.0198        | 23.0  | 8625  | 1.0257          | 0.8767   |
| 0.0001        | 24.0  | 9000  | 1.0258          | 0.885    |
| 0.0333        | 25.0  | 9375  | 1.0061          | 0.885    |
| 0.0008        | 26.0  | 9750  | 1.0277          | 0.8867   |
| 0.014         | 27.0  | 10125 | 1.0251          | 0.8833   |
| 0.0001        | 28.0  | 10500 | 1.0047          | 0.89     |
| 0.0006        | 29.0  | 10875 | 0.9963          | 0.89     |
| 0.0315        | 30.0  | 11250 | 1.0164          | 0.8833   |
| 0.0036        | 31.0  | 11625 | 1.0188          | 0.8883   |
| 0.0321        | 32.0  | 12000 | 1.0423          | 0.885    |
| 0.0001        | 33.0  | 12375 | 1.0241          | 0.89     |
| 0.0007        | 34.0  | 12750 | 1.0270          | 0.8883   |
| 0.0001        | 35.0  | 13125 | 1.0742          | 0.89     |
| 0.0009        | 36.0  | 13500 | 1.0587          | 0.8883   |
| 0.0086        | 37.0  | 13875 | 1.0716          | 0.8833   |
| 0.0329        | 38.0  | 14250 | 1.0601          | 0.8917   |
| 0.0002        | 39.0  | 14625 | 1.0796          | 0.8867   |
| 0.0003        | 40.0  | 15000 | 1.0705          | 0.88     |
| 0.0076        | 41.0  | 15375 | 1.0541          | 0.8833   |
| 0.0006        | 42.0  | 15750 | 1.0775          | 0.885    |
| 0.0002        | 43.0  | 16125 | 1.0701          | 0.885    |
| 0.0068        | 44.0  | 16500 | 1.0578          | 0.8933   |
| 0.0           | 45.0  | 16875 | 1.0747          | 0.8883   |
| 0.0158        | 46.0  | 17250 | 1.0767          | 0.89     |
| 0.0066        | 47.0  | 17625 | 1.0837          | 0.89     |
| 0.0106        | 48.0  | 18000 | 1.0810          | 0.8867   |
| 0.0002        | 49.0  | 18375 | 1.0785          | 0.885    |
| 0.0001        | 50.0  | 18750 | 1.0773          | 0.8867   |


### Framework versions

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