metadata

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

levit-128-finetuned-flower

This model is a fine-tuned version of 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