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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnext-tiny-224-finetuned-main-gpu-20e-final
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9875

convnext-tiny-224-finetuned-main-gpu-20e-final

This model is a fine-tuned version of facebook/convnext-tiny-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0349
  • Accuracy: 0.9875

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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
0.6197 1.0 551 0.5899 0.7440
0.3906 2.0 1102 0.3245 0.8717
0.3161 3.0 1653 0.2228 0.9135
0.2323 4.0 2204 0.1481 0.9446
0.2049 5.0 2755 0.1100 0.9589
0.1453 6.0 3306 0.0887 0.9671
0.1786 7.0 3857 0.0796 0.9702
0.1576 8.0 4408 0.0635 0.9767
0.1584 9.0 4959 0.0563 0.9798
0.122 10.0 5510 0.0570 0.9793
0.1138 11.0 6061 0.0526 0.9819
0.1116 12.0 6612 0.0498 0.9832
0.0876 13.0 7163 0.0497 0.9830
0.0956 14.0 7714 0.0403 0.9855
0.0892 15.0 8265 0.0414 0.9855
0.0807 16.0 8816 0.0425 0.9861
0.0959 17.0 9367 0.0397 0.9866
0.0847 18.0 9918 0.0373 0.9874
0.0962 19.0 10469 0.0356 0.9870
0.0731 20.0 11020 0.0349 0.9875

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2