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smids_10x_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_10x_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: 0.8019966722129783

smids_10x_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: 2.1068
  • Accuracy: 0.8020

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
0.8332 1.0 750 0.7567 0.6090
0.7395 2.0 1500 0.7599 0.6123
0.682 3.0 2250 0.6859 0.6905
0.725 4.0 3000 0.6463 0.7171
0.6632 5.0 3750 0.6560 0.7238
0.5777 6.0 4500 0.6347 0.7072
0.6357 7.0 5250 0.6141 0.7321
0.595 8.0 6000 0.6313 0.7121
0.5551 9.0 6750 0.6406 0.6955
0.5544 10.0 7500 0.5482 0.7720
0.5611 11.0 8250 0.5288 0.7704
0.6632 12.0 9000 0.5868 0.7537
0.5709 13.0 9750 0.6149 0.7288
0.4511 14.0 10500 0.4977 0.8020
0.4295 15.0 11250 0.5625 0.7770
0.4618 16.0 12000 0.5273 0.7837
0.4342 17.0 12750 0.5207 0.7804
0.4253 18.0 13500 0.5301 0.7720
0.4352 19.0 14250 0.5236 0.7754
0.418 20.0 15000 0.5318 0.7804
0.4496 21.0 15750 0.5216 0.7970
0.4003 22.0 16500 0.5391 0.7720
0.4411 23.0 17250 0.4904 0.8003
0.3266 24.0 18000 0.5436 0.7854
0.3733 25.0 18750 0.6780 0.7521
0.3536 26.0 19500 0.5100 0.8003
0.4154 27.0 20250 0.5545 0.8020
0.414 28.0 21000 0.5841 0.7937
0.3146 29.0 21750 0.5867 0.7887
0.3401 30.0 22500 0.5923 0.7987
0.2331 31.0 23250 0.6367 0.7837
0.238 32.0 24000 0.6276 0.8070
0.209 33.0 24750 0.6337 0.8070
0.2121 34.0 25500 0.6961 0.7854
0.2544 35.0 26250 0.7936 0.7870
0.2442 36.0 27000 0.7270 0.7970
0.2459 37.0 27750 0.7553 0.8020
0.1428 38.0 28500 0.8600 0.7987
0.0788 39.0 29250 0.9727 0.7937
0.1811 40.0 30000 1.0324 0.7937
0.1405 41.0 30750 1.0037 0.8103
0.1282 42.0 31500 1.1830 0.7937
0.0664 43.0 32250 1.2624 0.7970
0.04 44.0 33000 1.4942 0.7987
0.0582 45.0 33750 1.4631 0.8103
0.0738 46.0 34500 1.6687 0.8120
0.0282 47.0 35250 1.8321 0.8087
0.0021 48.0 36000 1.9181 0.8087
0.01 49.0 36750 2.0036 0.8037
0.0004 50.0 37500 2.1068 0.8020

Framework versions

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