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finetuned-breast_cancer_images

Image Classification
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PyTorch
TensorBoard
vit
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finetuned-breast_cancer_images

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

  • Train Accuracy: 0.9025
  • Train Loss: 0.0125
  • Accuracy: 0.9589
  • Loss: 0.0175

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 103

Training results

Training Loss Epoch Step Accuracy Loss Validation Loss
No log 5.06 200 0.9114 0.0241 0.0172
No log 5.06 200 0.9114 0.0241 0.0172
No log 10.13 400 0.9335 0.0209 0.0157
No log 10.13 400 0.9335 0.0209 0.0157
No log 15.19 600 0.9241 0.0189 0.0172
No log 15.19 600 0.9241 0.0189 0.0172
No log 20.25 800 0.9430 0.0182 0.0161
No log 20.25 800 0.9430 0.0182 0.0161
No log 25.32 1000 0.9399 0.0165 0.0186
No log 25.32 1000 0.9399 0.0165 0.0186
No log 30.38 1200 0.9557 0.0155 0.0137
No log 30.38 1200 0.9557 0.0155 0.0137
No log 35.44 1400 0.9335 0.0136 0.0180
No log 35.44 1400 0.9335 0.0136 0.0180
No log 40.51 1600 0.9525 0.0152 0.0139
No log 40.51 1600 0.9525 0.0152 0.0139
No log 45.57 1800 0.9494 0.0144 0.0149
No log 45.57 1800 0.9494 0.0144 0.0149
No log 50.63 2000 0.9430 0.0153 0.0187
No log 50.63 2000 0.9430 0.0153 0.0187
No log 55.7 2200 0.9557 0.0132 0.0149
No log 55.7 2200 0.9557 0.0132 0.0149
No log 60.76 2400 0.9430 0.0124 0.0149
No log 60.76 2400 0.9430 0.0124 0.0149
No log 65.82 2600 0.9525 0.0134 0.0164
No log 65.82 2600 0.9525 0.0134 0.0164
No log 70.89 2800 0.9557 0.0117 0.0143
No log 70.89 2800 0.9557 0.0117 0.0143
No log 75.95 3000 0.9557 0.0112 0.0166
No log 75.95 3000 0.9557 0.0112 0.0166
No log 81.01 3200 0.9589 0.0118 0.0163
No log 81.01 3200 0.9589 0.0118 0.0163
No log 86.08 3400 0.9494 0.0106 0.0188
No log 86.08 3400 0.9494 0.0106 0.0188
No log 91.14 3600 0.9462 0.0121 0.0186
No log 91.14 3600 0.9462 0.0121 0.0186
No log 96.2 3800 0.9525 0.0129 0.0171
No log 96.2 3800 0.9525 0.0129 0.0171
No log 101.27 4000 0.9589 0.0125 0.0175
No log 101.27 4000 0.9589 0.0125 0.0175

Framework versions

  • Transformers 4.30.0
  • Pytorch 2.3.0+cu121
  • Tokenizers 0.13.3
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Evaluation results

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