---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: beit-base-patch16-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.85
    - name: Precision
      type: precision
      value: 0.8455590062111802
    - name: Recall
      type: recall
      value: 0.85
---

<!-- 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. -->

# beit-base-patch16-224

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: 0.4871
- Accuracy: 0.85
- Precision: 0.8456
- Recall: 0.85
- F1 Score: 0.8464

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.5784          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 2.0   | 8    | 0.5813          | 0.7375   | 0.7030    | 0.7375 | 0.6441   |
| No log        | 3.0   | 12   | 0.5486          | 0.7417   | 0.7297    | 0.7417 | 0.7343   |
| No log        | 4.0   | 16   | 0.5394          | 0.7542   | 0.7333    | 0.7542 | 0.7370   |
| No log        | 5.0   | 20   | 0.5067          | 0.775    | 0.7658    | 0.775  | 0.7321   |
| No log        | 6.0   | 24   | 0.5542          | 0.7958   | 0.7966    | 0.7958 | 0.7613   |
| No log        | 7.0   | 28   | 0.4753          | 0.7958   | 0.7834    | 0.7958 | 0.7758   |
| 0.5325        | 8.0   | 32   | 0.5265          | 0.7792   | 0.7661    | 0.7792 | 0.7448   |
| 0.5325        | 9.0   | 36   | 0.4789          | 0.8208   | 0.8134    | 0.8208 | 0.8067   |
| 0.5325        | 10.0  | 40   | 0.4939          | 0.7875   | 0.7932    | 0.7875 | 0.7900   |
| 0.5325        | 11.0  | 44   | 0.4917          | 0.8042   | 0.8032    | 0.8042 | 0.8037   |
| 0.5325        | 12.0  | 48   | 0.5001          | 0.8083   | 0.8019    | 0.8083 | 0.8041   |
| 0.5325        | 13.0  | 52   | 0.4742          | 0.8      | 0.7897    | 0.8    | 0.7915   |
| 0.5325        | 14.0  | 56   | 0.5439          | 0.7875   | 0.8037    | 0.7875 | 0.7932   |
| 0.3381        | 15.0  | 60   | 0.5436          | 0.8333   | 0.8265    | 0.8333 | 0.8263   |
| 0.3381        | 16.0  | 64   | 0.4989          | 0.8375   | 0.8312    | 0.8375 | 0.8288   |
| 0.3381        | 17.0  | 68   | 0.4949          | 0.8333   | 0.8282    | 0.8333 | 0.8296   |
| 0.3381        | 18.0  | 72   | 0.4709          | 0.8292   | 0.8283    | 0.8292 | 0.8287   |
| 0.3381        | 19.0  | 76   | 0.4680          | 0.8167   | 0.8133    | 0.8167 | 0.8147   |
| 0.3381        | 20.0  | 80   | 0.5053          | 0.8417   | 0.8362    | 0.8417 | 0.8371   |
| 0.3381        | 21.0  | 84   | 0.5480          | 0.8458   | 0.8459    | 0.8458 | 0.8322   |
| 0.3381        | 22.0  | 88   | 0.4548          | 0.8542   | 0.8512    | 0.8542 | 0.8522   |
| 0.2076        | 23.0  | 92   | 0.4891          | 0.8458   | 0.8407    | 0.8458 | 0.8376   |
| 0.2076        | 24.0  | 96   | 0.4981          | 0.85     | 0.8486    | 0.85   | 0.8492   |
| 0.2076        | 25.0  | 100  | 0.4993          | 0.8458   | 0.8426    | 0.8458 | 0.8438   |
| 0.2076        | 26.0  | 104  | 0.5026          | 0.8542   | 0.8503    | 0.8542 | 0.8514   |
| 0.2076        | 27.0  | 108  | 0.4944          | 0.8542   | 0.8522    | 0.8542 | 0.8530   |
| 0.2076        | 28.0  | 112  | 0.4821          | 0.8542   | 0.8549    | 0.8542 | 0.8545   |
| 0.2076        | 29.0  | 116  | 0.4714          | 0.8583   | 0.8559    | 0.8583 | 0.8568   |
| 0.138         | 30.0  | 120  | 0.4705          | 0.8583   | 0.8559    | 0.8583 | 0.8568   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3