README.md

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-256-1-2
    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: 0.9716684155299056
          - name: Recall
            type: recall
            value: 0.9754098360655737
          - name: Precision
            type: precision
            value: 0.9802306425041186

teacher-status-van-tiny-256-1-2

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: 0.0859
• Accuracy: 0.9717
• F1 Score: 0.9778
• Recall: 0.9754
• Precision: 0.9802

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: 128
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 2
• 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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall	Precision
0.6722	0.99	33	0.6499	0.6401	0.7806	1.0	0.6401
0.5431	2.0	67	0.4164	0.7817	0.8531	0.9902	0.7494
0.393	2.99	100	0.2833	0.8877	0.9078	0.8639	0.9564
0.354	4.0	134	0.1930	0.9276	0.9436	0.9459	0.9413
0.3007	4.99	167	0.1585	0.9370	0.9511	0.9557	0.9464
0.2898	6.0	201	0.1445	0.9465	0.9581	0.9557	0.9605
0.2824	6.99	234	0.1353	0.9465	0.9580	0.9525	0.9635
0.2763	8.0	268	0.1359	0.9486	0.9603	0.9721	0.9488
0.2473	8.99	301	0.1213	0.9570	0.9664	0.9672	0.9656
0.2598	10.0	335	0.1091	0.9570	0.9665	0.9705	0.9626
0.2476	10.99	368	0.1041	0.9633	0.9714	0.9754	0.9675
0.2376	12.0	402	0.0997	0.9601	0.9686	0.9623	0.9751
0.2402	12.99	435	0.0972	0.9622	0.9704	0.9672	0.9736
0.2324	14.0	469	0.0950	0.9664	0.9739	0.9803	0.9676
0.2256	14.99	502	0.0909	0.9706	0.9770	0.9754	0.9786
0.21	16.0	536	0.0922	0.9622	0.9703	0.9656	0.9752
0.217	16.99	569	0.0933	0.9612	0.9695	0.9656	0.9736
0.2092	18.0	603	0.0891	0.9664	0.9738	0.9754	0.9722
0.2063	18.99	636	0.0913	0.9654	0.9730	0.9738	0.9722
0.2217	20.0	670	0.0917	0.9643	0.9720	0.9672	0.9768
0.1952	20.99	703	0.0859	0.9717	0.9778	0.9754	0.9802
0.2068	22.0	737	0.0907	0.9685	0.9755	0.9770	0.9739
0.1914	22.99	770	0.0847	0.9696	0.9763	0.9787	0.9739
0.1961	24.0	804	0.0870	0.9685	0.9755	0.9770	0.9739
0.1911	24.99	837	0.0884	0.9664	0.9739	0.9770	0.9707
0.1961	26.0	871	0.0870	0.9685	0.9754	0.9738	0.9770
0.1978	26.99	904	0.0871	0.9685	0.9754	0.9754	0.9754
0.1854	28.0	938	0.0858	0.9685	0.9755	0.9770	0.9739
0.1733	28.99	971	0.0860	0.9685	0.9754	0.9738	0.9770
0.1762	29.55	990	0.0858	0.9664	0.9738	0.9738	0.9738

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0