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-256-0
    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: 0.9777777777777777
          - name: Recall
            type: recall
            value: 0.9893162393162394
          - name: Precision
            type: precision
            value: 0.9788583509513742

teacher-status-van-tiny-256-0

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: 0.0672
Accuracy: 0.9778
F1 Score: 0.9841
Recall: 0.9893
Precision: 0.9789

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
0.6788	0.99	47	0.6437	0.6933	0.8189	1.0	0.6933
0.463	2.0	95	0.3406	0.8756	0.9162	0.9808	0.8596
0.3596	2.99	142	0.2072	0.9304	0.9504	0.9615	0.9395
0.3505	4.0	190	0.1564	0.9526	0.9661	0.9744	0.9580
0.2962	4.99	237	0.1262	0.9556	0.9681	0.9722	0.9640
0.2762	6.0	285	0.1038	0.9644	0.9745	0.9808	0.9684
0.2604	6.99	332	0.0932	0.9719	0.9798	0.9829	0.9766
0.2427	8.0	380	0.0928	0.9719	0.9797	0.9786	0.9807
0.2465	8.99	427	0.0898	0.9719	0.9797	0.9786	0.9807
0.2519	10.0	475	0.0913	0.9689	0.9775	0.9765	0.9786
0.2258	10.99	522	0.0847	0.9733	0.9809	0.9872	0.9747
0.2184	12.0	570	0.0812	0.9793	0.9851	0.9893	0.9809
0.2208	12.99	617	0.0693	0.9807	0.9861	0.9872	0.9851
0.2201	14.0	665	0.0628	0.9763	0.9829	0.9850	0.9809
0.2251	14.99	712	0.0811	0.9733	0.9810	0.9915	0.9707
0.2135	16.0	760	0.0718	0.9763	0.9829	0.9850	0.9809
0.1851	16.99	807	0.0791	0.9763	0.9830	0.9872	0.9788
0.2152	18.0	855	0.0737	0.9748	0.9818	0.9808	0.9829
0.1871	18.99	902	0.0814	0.9763	0.9830	0.9872	0.9788
0.1714	20.0	950	0.0692	0.9763	0.9830	0.9893	0.9768
0.188	20.99	997	0.0641	0.9778	0.9840	0.9850	0.9829
0.191	22.0	1045	0.0644	0.9793	0.9851	0.9872	0.9830
0.2025	22.99	1092	0.0675	0.9793	0.9850	0.9829	0.9871
0.1753	24.0	1140	0.0655	0.9822	0.9872	0.9893	0.9851
0.1857	24.99	1187	0.0731	0.9793	0.9851	0.9915	0.9789
0.2007	26.0	1235	0.0677	0.9793	0.9851	0.9915	0.9789
0.2086	26.99	1282	0.0640	0.9793	0.9851	0.9893	0.9809
0.1666	28.0	1330	0.0712	0.9778	0.9841	0.9893	0.9789
0.157	28.99	1377	0.0661	0.9807	0.9862	0.9893	0.9830
0.1758	29.68	1410	0.0672	0.9778	0.9841	0.9893	0.9789

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0