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hushem_1x_deit_tiny_adamax_lr00001_fold4

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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_lr00001_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: 0.5952380952380952

hushem_1x_deit_tiny_adamax_lr00001_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: 1.1208
  • Accuracy: 0.5952

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: 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 1.5521 0.1429
No log 2.0 3 1.4205 0.2857
No log 2.67 4 1.3862 0.3571
No log 4.0 6 1.3478 0.5238
No log 4.67 7 1.3332 0.5238
No log 6.0 9 1.3093 0.5238
1.4089 6.67 10 1.2970 0.5476
1.4089 8.0 12 1.2777 0.5714
1.4089 8.67 13 1.2689 0.5714
1.4089 10.0 15 1.2544 0.5714
1.4089 10.67 16 1.2478 0.5714
1.4089 12.0 18 1.2338 0.5714
1.4089 12.67 19 1.2267 0.5714
1.1506 14.0 21 1.2124 0.5714
1.1506 14.67 22 1.2049 0.5714
1.1506 16.0 24 1.1908 0.5714
1.1506 16.67 25 1.1843 0.5952
1.1506 18.0 27 1.1717 0.5952
1.1506 18.67 28 1.1659 0.5952
0.986 20.0 30 1.1576 0.5952
0.986 20.67 31 1.1537 0.5952
0.986 22.0 33 1.1470 0.5952
0.986 22.67 34 1.1439 0.5952
0.986 24.0 36 1.1385 0.5714
0.986 24.67 37 1.1362 0.5952
0.986 26.0 39 1.1320 0.5952
0.8708 26.67 40 1.1301 0.5952
0.8708 28.0 42 1.1268 0.5952
0.8708 28.67 43 1.1256 0.5952
0.8708 30.0 45 1.1234 0.5952
0.8708 30.67 46 1.1226 0.5952
0.8708 32.0 48 1.1214 0.5952
0.8708 32.67 49 1.1210 0.5952
0.8182 33.33 50 1.1208 0.5952

Framework versions

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