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Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold4

Image Classification
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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_fold4
    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: 0.6637767542671362

Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold4

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: 2.4944
  • Accuracy: 0.6638

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
1.1419 1.0 924 1.1069 0.6193
0.8711 2.0 1848 1.0127 0.6435
0.7373 3.0 2772 0.9976 0.6565
0.8211 4.0 3696 0.9949 0.6684
0.6291 5.0 4620 1.0468 0.6735
0.3396 6.0 5544 1.1204 0.6646
0.3275 7.0 6468 1.2442 0.6586
0.3288 8.0 7392 1.3222 0.6594
0.2359 9.0 8316 1.4540 0.6657
0.2071 10.0 9240 1.5984 0.6581
0.112 11.0 10164 1.6998 0.6600
0.1118 12.0 11088 1.8535 0.6600
0.0722 13.0 12012 2.0369 0.6627
0.062 14.0 12936 2.1305 0.6567
0.0657 15.0 13860 2.2604 0.6616
0.0351 16.0 14784 2.3298 0.6608
0.0555 17.0 15708 2.4139 0.6613
0.0491 18.0 16632 2.4530 0.6638
0.0881 19.0 17556 2.4844 0.6646
0.0213 20.0 18480 2.4944 0.6638

Framework versions

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