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
    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: 0.9831460674157303
          - name: Recall
            type: recall
            value: 0.9789473684210527
          - name: Precision
            type: precision
            value: 0.9893617021276596

teacher-status-van-tiny-256

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: 0.0988
Accuracy: 0.9831
F1 Score: 0.9841
Recall: 0.9789
Precision: 0.9894

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
0.6928	0.96	12	0.6904	0.6685	0.7631	1.0	0.6169
0.6893	2.0	25	0.6683	0.5393	0.6985	1.0	0.5367
0.6726	2.96	37	0.5704	0.5843	0.7197	1.0	0.5621
0.5295	4.0	50	0.4148	0.9213	0.9263	0.9263	0.9263
0.4745	4.96	62	0.3108	0.9382	0.9430	0.9579	0.9286
0.4206	6.0	75	0.2301	0.9438	0.9474	0.9474	0.9474
0.3898	6.96	87	0.1820	0.9494	0.9519	0.9368	0.9674
0.3153	8.0	100	0.1545	0.9494	0.9538	0.9789	0.93
0.3077	8.96	112	0.1521	0.9607	0.9622	0.9368	0.9889
0.3048	10.0	125	0.1331	0.9607	0.9626	0.9474	0.9783
0.3004	10.96	137	0.1314	0.9607	0.9634	0.9684	0.9583
0.2839	12.0	150	0.1272	0.9607	0.9622	0.9368	0.9889
0.286	12.96	162	0.1189	0.9607	0.9622	0.9368	0.9889
0.2473	14.0	175	0.0977	0.9719	0.9733	0.9579	0.9891
0.2774	14.96	187	0.0988	0.9831	0.9841	0.9789	0.9894
0.2541	16.0	200	0.0969	0.9719	0.9733	0.9579	0.9891
0.2383	16.96	212	0.1042	0.9719	0.9733	0.9579	0.9891
0.2552	18.0	225	0.1081	0.9719	0.9733	0.9579	0.9891
0.2223	18.96	237	0.1150	0.9663	0.9681	0.9579	0.9785
0.2561	20.0	250	0.1234	0.9551	0.9574	0.9474	0.9677
0.2462	20.96	262	0.1178	0.9607	0.9630	0.9579	0.9681
0.2294	22.0	275	0.1262	0.9382	0.9430	0.9579	0.9286
0.2296	22.96	287	0.1290	0.9438	0.9479	0.9579	0.9381
0.2224	24.0	300	0.1153	0.9494	0.9529	0.9579	0.9479
0.2205	24.96	312	0.1150	0.9494	0.9529	0.9579	0.9479
0.2169	26.0	325	0.1121	0.9551	0.9574	0.9474	0.9677
0.2212	26.96	337	0.1145	0.9494	0.9529	0.9579	0.9479
0.2188	28.0	350	0.1131	0.9494	0.9524	0.9474	0.9574
0.2015	28.8	360	0.1130	0.9494	0.9524	0.9474	0.9574

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0