metadata

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

levit-256-finetuned-flower

This model is a fine-tuned version of 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