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: vit_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: 0.9680307796238046

vit_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: 1.1208
• Accuracy: 0.9654
• F1 Score: 0.9686
• Recall: 0.9680

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
2.7928	1.0	19	2.8746	0.3080	0.2543	0.2785
2.6676	2.0	38	2.6793	0.3910	0.3269	0.3445
2.3938	3.0	57	2.3879	0.5190	0.4584	0.4678
2.0704	4.0	76	2.0268	0.7128	0.7070	0.6885
1.6362	5.0	95	1.6508	0.8651	0.8722	0.8623
1.3687	6.0	114	1.3848	0.8997	0.9090	0.9068
1.2580	7.0	133	1.2794	0.9100	0.9172	0.9094
1.1350	8.0	152	1.2075	0.9308	0.9372	0.9339
1.0887	9.0	171	1.1870	0.9343	0.9402	0.9363
1.0795	10.0	190	1.1688	0.9481	0.9531	0.9497
1.0515	11.0	209	1.1478	0.9516	0.9564	0.9547
1.0498	12.0	228	1.1544	0.9481	0.9524	0.9525
1.0147	13.0	247	1.1355	0.9585	0.9625	0.9609
1.0256	14.0	266	1.1517	0.9481	0.9529	0.9484
1.0107	15.0	285	1.1487	0.9481	0.9527	0.9511
1.0068	16.0	304	1.1287	0.9550	0.9594	0.9583
0.9904	17.0	323	1.1279	0.9550	0.9589	0.9584
0.9846	18.0	342	1.1228	0.9585	0.9620	0.9620
0.9907	19.0	361	1.1302	0.9550	0.9588	0.9572
0.9867	20.0	380	1.1309	0.9585	0.9620	0.9609
0.9810	21.0	399	1.1288	0.9516	0.9556	0.9534
0.9740	22.0	418	1.1208	0.9654	0.9686	0.9680
0.9674	23.0	437	1.1232	0.9619	0.9656	0.9656
0.9809	24.0	456	1.1269	0.9585	0.9624	0.9607
0.9679	25.0	475	1.1233	0.9585	0.9624	0.9607
0.9755	26.0	494	1.1295	0.9585	0.9624	0.9607
0.9730	27.0	513	1.1253	0.9550	0.9589	0.9584
0.9661	28.0	532	1.1280	0.9550	0.9588	0.9572
0.9692	29.0	551	1.1154	0.9585	0.9620	0.9609
0.9650	30.0	570	1.1211	0.9585	0.9620	0.9609
0.9671	31.0	589	1.1155	0.9619	0.9657	0.9657
0.9640	32.0	608	1.1290	0.9585	0.9620	0.9609
0.9675	33.0	627	1.1330	0.9585	0.9621	0.9621
0.9647	34.0	646	1.1300	0.9585	0.9625	0.9620
0.9634	35.0	665	1.1283	0.9585	0.9621	0.9621
0.9696	36.0	684	1.1407	0.9481	0.9524	0.9497
0.9605	37.0	703	1.1275	0.9585	0.9621	0.9621
0.9624	38.0	722	1.1291	0.9619	0.9651	0.9646
0.9678	39.0	741	1.1230	0.9619	0.9651	0.9646

Framework versions

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