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_5
    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: 0.9515570934256056
          - name: Recall
            type: recall
            value: 0.955763200802709

fold_5

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: 1.1556
• Accuracy: 0.9516
• F1 Score: 0.9547
• Recall: 0.9558

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
3.0831	1.0	19	2.9877	0.1834	0.1506	0.1750
2.9160	2.0	38	2.7975	0.2734	0.2517	0.2583
2.5816	3.0	57	2.5431	0.4671	0.4271	0.4325
2.1999	4.0	76	2.2368	0.6263	0.6019	0.6120
1.8110	5.0	95	1.9213	0.7301	0.7278	0.7223
1.4822	6.0	114	1.6147	0.8304	0.8407	0.8357
1.2515	7.0	133	1.4442	0.8581	0.8646	0.8541
1.1471	8.0	152	1.3540	0.8789	0.8857	0.8784
1.0819	9.0	171	1.2801	0.8962	0.9021	0.8978
1.0613	10.0	190	1.2465	0.9100	0.9142	0.9124
1.0395	11.0	209	1.2235	0.9343	0.9386	0.9387
1.0240	12.0	228	1.2106	0.9377	0.9410	0.9397
1.0257	13.0	247	1.1934	0.9308	0.9358	0.9361
1.0162	14.0	266	1.1946	0.9377	0.9402	0.9384
0.9940	15.0	285	1.1879	0.9377	0.9411	0.9397
1.0097	16.0	304	1.1848	0.9412	0.9449	0.9423
0.9937	17.0	323	1.1863	0.9377	0.9408	0.9384
0.9864	18.0	342	1.1935	0.9446	0.9471	0.9446
0.9830	19.0	361	1.1802	0.9446	0.9474	0.9459
0.9825	20.0	380	1.1508	0.9446	0.9490	0.9485
0.9733	21.0	399	1.1568	0.9343	0.9385	0.9385
0.9778	22.0	418	1.1570	0.9412	0.9455	0.9460
0.9657	23.0	437	1.1588	0.9446	0.9487	0.9497
0.9771	24.0	456	1.1637	0.9446	0.9495	0.9510
0.9647	25.0	475	1.1640	0.9481	0.9509	0.9509
0.9649	26.0	494	1.1572	0.9516	0.9546	0.9546
0.9686	27.0	513	1.1667	0.9377	0.9411	0.9397
0.9681	28.0	532	1.1614	0.9377	0.9417	0.9423
0.9642	29.0	551	1.1599	0.9412	0.9444	0.9434
0.9677	30.0	570	1.1556	0.9516	0.9547	0.9558

Framework versions

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