metadata

license: apache-2.0
base_model: Visual-Attention-Network/van-tiny
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - precision
model-index:
  - name: teacher-status-van-tiny-256-2
    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: 0.9759358288770054
          - name: Recall
            type: recall
            value: 0.9756944444444444
          - name: Precision
            type: precision
            value: 0.9929328621908127

teacher-status-van-tiny-256-2

This model is a fine-tuned version of Visual-Attention-Network/van-tiny on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0916
Accuracy: 0.9759
F1 Score: 0.9842
Recall: 0.9757
Precision: 0.9929

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall	Precision
0.6896	0.99	26	0.6707	0.7701	0.8701	1.0	0.7701
0.5438	1.98	52	0.4302	0.7701	0.8701	1.0	0.7701
0.3756	2.97	78	0.2762	0.8850	0.9285	0.9688	0.8914
0.3017	4.0	105	0.2002	0.9225	0.9503	0.9618	0.9390
0.257	4.99	131	0.1794	0.9385	0.9605	0.9722	0.9492
0.2345	5.98	157	0.1485	0.9358	0.9582	0.9549	0.9615
0.2318	6.97	183	0.1302	0.9439	0.9631	0.9514	0.9751
0.2173	8.0	210	0.1277	0.9519	0.9689	0.9722	0.9655
0.2058	8.99	236	0.1269	0.9572	0.9722	0.9722	0.9722
0.1955	9.98	262	0.1146	0.9572	0.9724	0.9792	0.9658
0.2083	10.97	288	0.1083	0.9652	0.9772	0.9688	0.9859
0.1886	12.0	315	0.1048	0.9599	0.9741	0.9792	0.9691
0.1618	12.99	341	0.1033	0.9626	0.9757	0.9757	0.9757
0.1908	13.98	367	0.1044	0.9599	0.9739	0.9722	0.9756
0.1594	14.97	393	0.0915	0.9626	0.9758	0.9792	0.9724
0.1474	16.0	420	0.0916	0.9759	0.9842	0.9757	0.9929
0.1734	16.99	446	0.0951	0.9652	0.9773	0.9722	0.9825
0.1484	17.98	472	0.1049	0.9706	0.9809	0.9792	0.9826
0.1495	18.97	498	0.0930	0.9679	0.9791	0.9757	0.9825
0.1385	20.0	525	0.0955	0.9626	0.9759	0.9826	0.9692
0.1492	20.99	551	0.0911	0.9599	0.9741	0.9792	0.9691
0.1401	21.98	577	0.0927	0.9706	0.9809	0.9792	0.9826
0.1288	22.97	603	0.0940	0.9706	0.9809	0.9792	0.9826
0.1304	24.0	630	0.0913	0.9652	0.9775	0.9826	0.9725
0.14	24.99	656	0.0979	0.9652	0.9776	0.9861	0.9693
0.1461	25.98	682	0.0874	0.9706	0.9810	0.9861	0.9759
0.1429	26.97	708	0.0837	0.9706	0.9808	0.9757	0.9860
0.1444	28.0	735	0.0876	0.9679	0.9792	0.9792	0.9792
0.145	28.99	761	0.0903	0.9706	0.9809	0.9792	0.9826
0.1445	29.71	780	0.0882	0.9679	0.9791	0.9757	0.9825

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0