---
license: apache-2.0
base_model: facebook/levit-128
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: levit-128-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: 0.9506352087114338
    - name: Precision
      type: precision
      value: 0.950988634564862
    - name: Recall
      type: recall
      value: 0.9506352087114338
    - name: F1
      type: f1
      value: 0.9505680872971296
---

<!-- 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-128-finetuned-flower

This model is a fine-tuned version of [facebook/levit-128](https://huggingface.co/facebook/levit-128) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1807
- Accuracy: 0.9506
- Precision: 0.9510
- Recall: 0.9506
- F1: 0.9506

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6679        | 1.0   | 40   | 0.6957          | 0.8076   | 0.8492    | 0.8076 | 0.8060 |
| 0.7188        | 2.0   | 80   | 0.7094          | 0.7822   | 0.7997    | 0.7822 | 0.7789 |
| 0.7277        | 3.0   | 120  | 0.7803          | 0.7477   | 0.7912    | 0.7477 | 0.7480 |
| 0.561         | 4.0   | 160  | 0.5489          | 0.8352   | 0.8462    | 0.8352 | 0.8292 |
| 0.4958        | 5.0   | 200  | 0.4067          | 0.8770   | 0.8852    | 0.8770 | 0.8766 |
| 0.4681        | 6.0   | 240  | 0.4801          | 0.8457   | 0.8570    | 0.8457 | 0.8423 |
| 0.368         | 7.0   | 280  | 0.4348          | 0.8617   | 0.8697    | 0.8617 | 0.8618 |
| 0.355         | 8.0   | 320  | 0.3401          | 0.8926   | 0.8971    | 0.8926 | 0.8924 |
| 0.3164        | 9.0   | 360  | 0.3510          | 0.8871   | 0.8935    | 0.8871 | 0.8871 |
| 0.2972        | 10.0  | 400  | 0.2877          | 0.9140   | 0.9159    | 0.9140 | 0.9133 |
| 0.2639        | 11.0  | 440  | 0.2588          | 0.9245   | 0.9246    | 0.9245 | 0.9233 |
| 0.264         | 12.0  | 480  | 0.2811          | 0.9096   | 0.9155    | 0.9096 | 0.9097 |
| 0.2082        | 13.0  | 520  | 0.2368          | 0.9238   | 0.9244    | 0.9238 | 0.9225 |
| 0.1506        | 14.0  | 560  | 0.2552          | 0.9205   | 0.9244    | 0.9205 | 0.9200 |
| 0.179         | 15.0  | 600  | 0.2133          | 0.9401   | 0.9421    | 0.9401 | 0.9399 |
| 0.1388        | 16.0  | 640  | 0.2170          | 0.9376   | 0.9388    | 0.9376 | 0.9377 |
| 0.116         | 17.0  | 680  | 0.1817          | 0.9466   | 0.9468    | 0.9466 | 0.9464 |
| 0.0976        | 18.0  | 720  | 0.1915          | 0.9470   | 0.9477    | 0.9470 | 0.9473 |
| 0.0806        | 19.0  | 760  | 0.1876          | 0.9492   | 0.9501    | 0.9492 | 0.9493 |
| 0.0911        | 20.0  | 800  | 0.1807          | 0.9506   | 0.9510    | 0.9506 | 0.9506 |


### Framework versions

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