README.md

metadata

library_name: transformers
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-roadwork-output-refined
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9537892791127541
          - name: Precision
            type: precision
            value: 0.9697601668404588
          - name: Recall
            type: recall
            value: 0.9779179810725552
          - name: F1
            type: f1
            value: 0.9738219895287958

vit-roadwork-output-refined

This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.1501
• Accuracy: 0.9538
• Precision: 0.9698
• Recall: 0.9779
• F1: 0.9738

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• 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: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.1145	1.0	136	0.1476	0.9445	0.9617	0.9758	0.9687
0.1057	2.0	272	0.1396	0.9455	0.9685	0.9695	0.9690
0.0862	3.0	408	0.1362	0.9501	0.9619	0.9821	0.9719
0.0819	4.0	544	0.1464	0.9436	0.9674	0.9685	0.9679
0.0913	5.0	680	0.1443	0.9473	0.9599	0.9811	0.9704
0.0584	6.0	816	0.1436	0.9501	0.9726	0.9706	0.9716
0.0520	7.0	952	0.1279	0.9658	0.9800	0.9811	0.9806
0.0655	8.0	1088	0.1628	0.9473	0.9676	0.9727	0.9701
0.0563	9.0	1224	0.1475	0.9584	0.9768	0.9758	0.9763
0.0583	10.0	1360	0.1501	0.9538	0.9698	0.9779	0.9738

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2