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Boya1_3Class_SGD_1-e3_20Epoch_Beit-large-224_fold1

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
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Boya1_3Class_SGD_1-e3_20Epoch_Beit-large-224_fold1 / README.md
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metadata
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Boya1_3Class_SGD_1-e3_20Epoch_Beit-large-224_fold1
    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: 0.7654086342655444

Boya1_3Class_SGD_1-e3_20Epoch_Beit-large-224_fold1

This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5544
  • Accuracy: 0.7654

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: 0.001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.9015 1.0 924 0.9341 0.6112
0.8409 2.0 1848 0.7990 0.6780
0.787 3.0 2772 0.7073 0.7116
0.638 4.0 3696 0.6605 0.7269
0.7348 5.0 4620 0.6355 0.7355
0.5951 6.0 5544 0.6177 0.7396
0.6333 7.0 6468 0.6036 0.7456
0.5506 8.0 7392 0.5938 0.7491
0.5249 9.0 8316 0.5879 0.7526
0.7137 10.0 9240 0.5788 0.7570
0.5106 11.0 10164 0.5775 0.7575
0.6297 12.0 11088 0.5685 0.7608
0.5637 13.0 12012 0.5647 0.7613
0.5879 14.0 12936 0.5617 0.7632
0.4454 15.0 13860 0.5588 0.7649
0.522 16.0 14784 0.5607 0.7630
0.551 17.0 15708 0.5549 0.7660
0.6177 18.0 16632 0.5560 0.7641
0.5656 19.0 17556 0.5549 0.7641
0.6413 20.0 18480 0.5544 0.7654

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1
  • Datasets 2.12.0
  • Tokenizers 0.13.2