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hushem_1x_deit_tiny_adamax_lr0001_fold4

Image Classification
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metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_adamax_lr0001_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: 0.8333333333333334

hushem_1x_deit_tiny_adamax_lr0001_fold4

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

  • Loss: 0.5599
  • Accuracy: 0.8333

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: 0.0001
  • 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: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.67 1 1.6749 0.3095
No log 2.0 3 1.3545 0.3333
No log 2.67 4 1.3451 0.2857
No log 4.0 6 1.2535 0.5238
No log 4.67 7 1.2290 0.4286
No log 6.0 9 1.1555 0.5
1.2457 6.67 10 1.0938 0.5
1.2457 8.0 12 0.9608 0.4762
1.2457 8.67 13 0.8825 0.5952
1.2457 10.0 15 0.7678 0.7143
1.2457 10.67 16 0.7184 0.7857
1.2457 12.0 18 0.6658 0.7619
1.2457 12.67 19 0.6361 0.7619
0.4167 14.0 21 0.6247 0.8095
0.4167 14.67 22 0.6111 0.7857
0.4167 16.0 24 0.5896 0.7857
0.4167 16.67 25 0.5886 0.7381
0.4167 18.0 27 0.6107 0.7619
0.4167 18.67 28 0.6198 0.7619
0.0627 20.0 30 0.6194 0.7619
0.0627 20.67 31 0.6092 0.7619
0.0627 22.0 33 0.5917 0.7857
0.0627 22.67 34 0.5871 0.7857
0.0627 24.0 36 0.5872 0.8095
0.0627 24.67 37 0.5896 0.8095
0.0627 26.0 39 0.5921 0.8095
0.0081 26.67 40 0.5908 0.8095
0.0081 28.0 42 0.5818 0.8095
0.0081 28.67 43 0.5772 0.8095
0.0081 30.0 45 0.5685 0.8095
0.0081 30.67 46 0.5654 0.8095
0.0081 32.0 48 0.5614 0.8333
0.0081 32.67 49 0.5603 0.8333
0.0038 33.33 50 0.5599 0.8333

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1