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smids_5x_deit_tiny_rms_001_fold2

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-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_tiny_rms_001_fold2
    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: 0.848585690515807

smids_5x_deit_tiny_rms_001_fold2

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: 1.4964
  • Accuracy: 0.8486

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
1.0328 1.0 375 0.9569 0.4459
0.8929 2.0 750 0.8978 0.5374
0.8224 3.0 1125 0.7888 0.5574
0.8327 4.0 1500 0.8571 0.5641
0.7266 5.0 1875 1.1729 0.5025
0.6507 6.0 2250 0.7875 0.6456
0.6983 7.0 2625 0.6489 0.6972
0.6312 8.0 3000 0.7326 0.6789
0.641 9.0 3375 0.5505 0.7488
0.6354 10.0 3750 0.5766 0.7354
0.5813 11.0 4125 0.4910 0.7920
0.6084 12.0 4500 0.5458 0.7720
0.4944 13.0 4875 0.4657 0.8020
0.5555 14.0 5250 0.5401 0.7621
0.526 15.0 5625 0.4958 0.7837
0.3751 16.0 6000 0.4911 0.8037
0.4264 17.0 6375 0.5204 0.7837
0.4312 18.0 6750 0.5011 0.7953
0.3686 19.0 7125 0.4979 0.7970
0.3954 20.0 7500 0.4812 0.8120
0.3782 21.0 7875 0.4706 0.8120
0.3544 22.0 8250 0.4461 0.8353
0.3759 23.0 8625 0.4516 0.8319
0.3473 24.0 9000 0.4332 0.8270
0.2572 25.0 9375 0.5951 0.8203
0.3628 26.0 9750 0.5630 0.7887
0.2737 27.0 10125 0.5304 0.8336
0.2272 28.0 10500 0.5597 0.8319
0.2226 29.0 10875 0.5680 0.8419
0.1778 30.0 11250 0.6295 0.8170
0.2382 31.0 11625 0.6223 0.8270
0.1721 32.0 12000 0.6049 0.8469
0.219 33.0 12375 0.5556 0.8569
0.0972 34.0 12750 0.6389 0.8502
0.1781 35.0 13125 0.7873 0.8253
0.1052 36.0 13500 0.8815 0.8236
0.1087 37.0 13875 0.7444 0.8453
0.09 38.0 14250 0.9779 0.8253
0.0859 39.0 14625 0.8817 0.8386
0.0521 40.0 15000 0.9849 0.8453
0.081 41.0 15375 1.0555 0.8203
0.0225 42.0 15750 1.1081 0.8303
0.0521 43.0 16125 1.2294 0.8253
0.0259 44.0 16500 1.3035 0.8336
0.0403 45.0 16875 1.3613 0.8253
0.0225 46.0 17250 1.4500 0.8103
0.0235 47.0 17625 1.5096 0.8270
0.0002 48.0 18000 1.5022 0.8469
0.0101 49.0 18375 1.4968 0.8469
0.0029 50.0 18750 1.4964 0.8486

Framework versions

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