README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
model-index:
  - name: fold_4
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: None
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9653979238754326
          - name: Recall
            type: recall
            value: 0.9683506090481117

fold_4

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.1328
• Accuracy: 0.9654
• F1 Score: 0.9688
• Recall: 0.9684

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: 1e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 100
• label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.7279	1.0	19	2.6961	0.3287	0.3257	0.3264
2.6497	2.0	38	2.5197	0.4775	0.4752	0.4600
2.3856	3.0	57	2.2554	0.6574	0.6632	0.6472
2.0467	4.0	76	1.9435	0.8062	0.8186	0.8137
1.6696	5.0	95	1.6132	0.8720	0.8800	0.8750
1.3967	6.0	114	1.3746	0.9170	0.9249	0.9244
1.2758	7.0	133	1.2437	0.9412	0.9473	0.9422
1.1517	8.0	152	1.1767	0.9550	0.9593	0.9559
1.1069	9.0	171	1.1511	0.9516	0.9556	0.9512
1.0847	10.0	190	1.1386	0.9550	0.9589	0.9584
1.0566	11.0	209	1.1328	0.9654	0.9688	0.9684
1.0535	12.0	228	1.1346	0.9550	0.9597	0.9609
1.0154	13.0	247	1.1250	0.9550	0.9595	0.9595
1.0196	14.0	266	1.1252	0.9585	0.9625	0.9609
1.0003	15.0	285	1.1256	0.9481	0.9529	0.9524
0.9951	16.0	304	1.1191	0.9619	0.9653	0.9659
0.9877	17.0	323	1.1133	0.9619	0.9651	0.9646
0.9841	18.0	342	1.1038	0.9619	0.9651	0.9646
0.9887	19.0	361	1.1080	0.9619	0.9650	0.9635
0.9794	20.0	380	1.1105	0.9619	0.9651	0.9646
0.9786	21.0	399	1.1086	0.9654	0.9681	0.9671
0.9719	22.0	418	1.1084	0.9550	0.9591	0.9597
0.9723	23.0	437	1.1242	0.9516	0.9562	0.9584
0.9813	24.0	456	1.0986	0.9654	0.9681	0.9671
0.9682	25.0	475	1.0959	0.9654	0.9681	0.9671
0.9750	26.0	494	1.0990	0.9654	0.9681	0.9671
0.9716	27.0	513	1.0995	0.9619	0.9651	0.9646
0.9701	28.0	532	1.0949	0.9654	0.9681	0.9671
0.9694	29.0	551	1.0997	0.9654	0.9681	0.9671
0.9637	30.0	570	1.1002	0.9654	0.9681	0.9671
0.9647	31.0	589	1.0992	0.9654	0.9681	0.9671
0.9658	32.0	608	1.1084	0.9619	0.9650	0.9635
0.9686	33.0	627	1.1156	0.9619	0.9652	0.9659
0.9632	34.0	646	1.1068	0.9619	0.9650	0.9634
0.9630	35.0	665	1.1040	0.9619	0.9650	0.9634
0.9680	36.0	684	1.1131	0.9619	0.9650	0.9634
0.9579	37.0	703	1.1063	0.9619	0.9650	0.9634
0.9615	38.0	722	1.1063	0.9619	0.9650	0.9634

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2