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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: image_classification_for_fracture
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.85

image_classification_for_fracture

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4783
  • Accuracy: 0.85

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.8 2 0.6696 0.75
No log 2.0 5 0.6296 0.7
No log 2.8 7 0.5853 0.775
0.639 4.0 10 0.5731 0.8
0.639 4.8 12 0.5430 0.825
0.639 6.0 15 0.5223 0.85
0.639 6.8 17 0.5036 0.8
0.5453 8.0 20 0.4783 0.85

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.2.1+cpu
  • Datasets 2.18.0
  • Tokenizers 0.15.2