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Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold3

Image Classification
Transformers
Safetensors
swin
generated_from_trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold3
    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: 0.6651338923451448

Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold3

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3110
  • Accuracy: 0.6651

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: 1e-05
  • 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
1.0919 1.0 923 1.1489 0.6081
0.9736 2.0 1846 1.0024 0.6559
0.975 3.0 2769 0.9707 0.6684
0.7334 4.0 3692 0.9560 0.6781
0.485 5.0 4615 1.0348 0.6662
0.316 6.0 5538 1.0693 0.6719
0.3512 7.0 6461 1.1640 0.6659
0.2952 8.0 7384 1.3227 0.6546
0.2423 9.0 8307 1.3837 0.6643
0.1889 10.0 9230 1.4884 0.6619
0.1312 11.0 10153 1.6309 0.6632
0.102 12.0 11076 1.7865 0.6622
0.0735 13.0 11999 1.8485 0.6586
0.1241 14.0 12922 2.0117 0.6600
0.1724 15.0 13845 2.0571 0.6627
0.0383 16.0 14768 2.1327 0.6603
0.0264 17.0 15691 2.2234 0.6641
0.0681 18.0 16614 2.2755 0.6681
0.0199 19.0 17537 2.3061 0.6643
0.1052 20.0 18460 2.3110 0.6651

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1