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smids_5x_deit_tiny_adamax_001_fold1

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_tiny_adamax_001_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: 0.8898163606010017

smids_5x_deit_tiny_adamax_001_fold1

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

  • Loss: 0.9489
  • Accuracy: 0.8898

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: 32
  • eval_batch_size: 32
  • 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: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3549 1.0 376 0.4844 0.8264
0.2678 2.0 752 0.3259 0.8798
0.3098 3.0 1128 0.3469 0.8548
0.2057 4.0 1504 0.3089 0.8831
0.15 5.0 1880 0.4280 0.8748
0.0947 6.0 2256 0.5773 0.8581
0.1544 7.0 2632 0.3805 0.8881
0.1085 8.0 3008 0.4878 0.8731
0.0399 9.0 3384 0.4495 0.8965
0.0251 10.0 3760 0.5573 0.8681
0.0684 11.0 4136 0.4467 0.8648
0.0506 12.0 4512 0.5126 0.8982
0.0075 13.0 4888 0.8575 0.8715
0.0481 14.0 5264 0.7463 0.8664
0.0077 15.0 5640 0.6816 0.8865
0.0098 16.0 6016 0.6312 0.8831
0.0003 17.0 6392 0.7022 0.8965
0.0075 18.0 6768 0.6976 0.8731
0.0042 19.0 7144 0.6012 0.8881
0.0311 20.0 7520 0.7693 0.8932
0.003 21.0 7896 0.6254 0.8915
0.0101 22.0 8272 0.6004 0.8998
0.0209 23.0 8648 0.7643 0.8815
0.0001 24.0 9024 0.8262 0.8848
0.0007 25.0 9400 0.6944 0.8898
0.0034 26.0 9776 0.7140 0.8915
0.0071 27.0 10152 0.8088 0.8798
0.0001 28.0 10528 0.7766 0.9032
0.0039 29.0 10904 0.8084 0.8948
0.0045 30.0 11280 0.7741 0.8831
0.0006 31.0 11656 0.8264 0.8932
0.0 32.0 12032 0.8432 0.8865
0.0 33.0 12408 0.8641 0.8848
0.0 34.0 12784 0.8447 0.8865
0.0 35.0 13160 0.8402 0.8848
0.0 36.0 13536 0.8232 0.8948
0.0 37.0 13912 0.8382 0.8915
0.0 38.0 14288 0.8652 0.8898
0.0 39.0 14664 0.8733 0.8848
0.0 40.0 15040 0.8254 0.8881
0.0 41.0 15416 0.8627 0.8848
0.0 42.0 15792 0.8799 0.8881
0.0 43.0 16168 0.8887 0.8915
0.0 44.0 16544 0.9046 0.8932
0.0 45.0 16920 0.9092 0.8932
0.0031 46.0 17296 0.9143 0.8881
0.0 47.0 17672 0.9293 0.8915
0.0 48.0 18048 0.9378 0.8898
0.0 49.0 18424 0.9447 0.8898
0.0023 50.0 18800 0.9489 0.8898

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2