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End of training
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metadata
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: SWIN_finetuned_frozen_v3_cont
    results: []

SWIN_finetuned_frozen_v3_cont

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9857
  • Accuracy: 0.6785

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: 0.0004
  • train_batch_size: 512
  • eval_batch_size: 512
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15.0

Training results

Training Loss Epoch Step Accuracy Validation Loss
1.6863 1.0 1313 0.5757 1.9559
1.3275 2.0 2626 0.6061 1.8276
1.151 3.0 3939 0.6130 1.8857
1.0336 4.0 5252 0.6322 1.8160
0.947 5.0 6565 0.6317 1.8051
0.8595 6.0 7878 0.6443 1.7996
0.801 7.0 9191 0.6534 1.7987
0.7508 8.0 10504 0.6522 1.7864
0.694 9.0 11817 0.6526 1.8871
0.6523 10.0 13130 0.6648 1.8057
0.5976 11.0 14443 0.6707 1.8514
0.5743 12.0 15756 0.6629 1.9271
0.5426 13.0 17069 0.6692 1.9221
0.5092 14.0 18382 0.6752 1.9164
0.4808 15.0 19695 0.6743 1.9259
0.4611 16.0 21008 0.6785 1.9857

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.1.2
  • Datasets 2.16.1
  • Tokenizers 0.13.3