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---
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
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