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metadata
library_name: transformers
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
model-index:
  - name: signet
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.7853930177768259
            name: Accuracy
          - type: f1
            value: 0.8643011917659805
            name: F1

signet

This model is a fine-tuned version of on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4517
  • Accuracy: 0.7854
  • F1: 0.8643

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: 0.0002
  • train_batch_size: 128
  • eval_batch_size: 256
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 512
  • 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
  • lr_scheduler_warmup_steps: 0.05
  • num_epochs: 12
  • mixed_precision_training: Native AMP
  • label_smoothing_factor: 0.05

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.0 37 0.5892 0.7383 0.8479
No log 2.0 74 0.5829 0.7372 0.8464
2.4270 3.0 111 0.5547 0.7419 0.8504
2.4270 4.0 148 0.5311 0.7344 0.8415
2.4270 5.0 185 0.5291 0.7404 0.8472
2.1988 6.0 222 0.5170 0.7432 0.8496
2.1988 7.0 259 0.4517 0.7854 0.8643
2.1988 8.0 296 0.4865 0.7653 0.8406
1.9616 9.0 333 0.4380 0.7875 0.8614
1.9616 10.0 370 0.4546 0.7698 0.8367
1.7220 11.0 407 0.4355 0.7901 0.8552
1.7220 12.0 444 0.4334 0.7963 0.8622

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.9.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2