---
license: apache-2.0
base_model: facebook/levit-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: levit-256-finetuned-flower
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9520871143375681
    - name: Precision
      type: precision
      value: 0.9522871286223231
    - name: Recall
      type: recall
      value: 0.9520871143375681
    - name: F1
      type: f1
      value: 0.9518251458019376
---

<!-- 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. -->

# levit-256-finetuned-flower

This model is a fine-tuned version of [facebook/levit-256](https://huggingface.co/facebook/levit-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1677
- Accuracy: 0.9521
- Precision: 0.9523
- Recall: 0.9521
- F1: 0.9518

## 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: 0.005
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.599         | 1.0   | 40   | 0.5907          | 0.8207   | 0.8515    | 0.8207 | 0.8219 |
| 0.7842        | 2.0   | 80   | 1.4800          | 0.6693   | 0.7271    | 0.6693 | 0.6607 |
| 0.7716        | 3.0   | 120  | 0.8614          | 0.7554   | 0.7853    | 0.7554 | 0.7544 |
| 0.5976        | 4.0   | 160  | 0.5576          | 0.8243   | 0.8470    | 0.8243 | 0.8260 |
| 0.488         | 5.0   | 200  | 0.4656          | 0.8555   | 0.8724    | 0.8555 | 0.8546 |
| 0.4871        | 6.0   | 240  | 0.4387          | 0.8672   | 0.8823    | 0.8672 | 0.8672 |
| 0.3606        | 7.0   | 280  | 0.3041          | 0.9045   | 0.9053    | 0.9045 | 0.9034 |
| 0.3159        | 8.0   | 320  | 0.3283          | 0.8976   | 0.9022    | 0.8976 | 0.8961 |
| 0.3078        | 9.0   | 360  | 0.2848          | 0.9125   | 0.9156    | 0.9125 | 0.9124 |
| 0.2922        | 10.0  | 400  | 0.2526          | 0.9180   | 0.9212    | 0.9180 | 0.9184 |
| 0.2412        | 11.0  | 440  | 0.2367          | 0.9281   | 0.9306    | 0.9281 | 0.9280 |
| 0.2095        | 12.0  | 480  | 0.2283          | 0.9314   | 0.9323    | 0.9314 | 0.9305 |
| 0.1786        | 13.0  | 520  | 0.1890          | 0.9408   | 0.9412    | 0.9408 | 0.9408 |
| 0.123         | 14.0  | 560  | 0.2071          | 0.9383   | 0.9398    | 0.9383 | 0.9382 |
| 0.1481        | 15.0  | 600  | 0.1854          | 0.9426   | 0.9433    | 0.9426 | 0.9426 |
| 0.125         | 16.0  | 640  | 0.2051          | 0.9376   | 0.9400    | 0.9376 | 0.9373 |
| 0.1135        | 17.0  | 680  | 0.1785          | 0.9495   | 0.9496    | 0.9495 | 0.9495 |
| 0.0815        | 18.0  | 720  | 0.1655          | 0.9539   | 0.9542    | 0.9539 | 0.9538 |
| 0.0784        | 19.0  | 760  | 0.1707          | 0.9525   | 0.9527    | 0.9525 | 0.9521 |
| 0.0905        | 20.0  | 800  | 0.1677          | 0.9521   | 0.9523    | 0.9521 | 0.9518 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2