Hugging Face's logo

hkivancoral
/
smids_10x_deit_tiny_sgd_00001_fold5

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
smids_10x_deit_tiny_sgd_00001_fold5 / README.md
hkivancoral's picture
hkivancoral
End of training
da2b066
preview code
|
raw
history blame contribute delete
4.87 kB
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_sgd_00001_fold5
    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: 0.5233333333333333

smids_10x_deit_tiny_sgd_00001_fold5

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: 0.9599
  • Accuracy: 0.5233

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
  • 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
1.3812 1.0 750 1.2940 0.36
1.3175 2.0 1500 1.2389 0.3683
1.2691 3.0 2250 1.1984 0.3733
1.221 4.0 3000 1.1690 0.385
1.1712 5.0 3750 1.1479 0.3967
1.1485 6.0 4500 1.1321 0.4067
1.0967 7.0 5250 1.1194 0.3917
1.0947 8.0 6000 1.1088 0.4017
1.1331 9.0 6750 1.0995 0.405
1.0758 10.0 7500 1.0911 0.4167
1.0859 11.0 8250 1.0832 0.4117
1.074 12.0 9000 1.0758 0.4217
1.0354 13.0 9750 1.0688 0.4233
1.0611 14.0 10500 1.0620 0.4217
1.0504 15.0 11250 1.0556 0.425
1.0195 16.0 12000 1.0495 0.4367
1.0374 17.0 12750 1.0437 0.4383
1.0062 18.0 13500 1.0380 0.4433
1.0602 19.0 14250 1.0326 0.45
1.024 20.0 15000 1.0275 0.4533
0.9853 21.0 15750 1.0225 0.4567
1.024 22.0 16500 1.0178 0.46
1.0062 23.0 17250 1.0132 0.4617
0.9775 24.0 18000 1.0089 0.4683
0.9615 25.0 18750 1.0048 0.4733
0.9865 26.0 19500 1.0008 0.4783
0.9677 27.0 20250 0.9971 0.4867
0.9698 28.0 21000 0.9935 0.4867
0.9829 29.0 21750 0.9901 0.49
0.9556 30.0 22500 0.9870 0.49
0.963 31.0 23250 0.9840 0.4917
0.9489 32.0 24000 0.9813 0.495
0.9694 33.0 24750 0.9787 0.4967
0.9392 34.0 25500 0.9762 0.4967
0.9586 35.0 26250 0.9740 0.5
0.9291 36.0 27000 0.9720 0.5083
0.9064 37.0 27750 0.9701 0.5117
0.9352 38.0 28500 0.9684 0.5117
0.9164 39.0 29250 0.9668 0.5133
0.9501 40.0 30000 0.9654 0.515
0.8967 41.0 30750 0.9642 0.5167
0.9489 42.0 31500 0.9632 0.5167
0.9594 43.0 32250 0.9623 0.52
0.9042 44.0 33000 0.9616 0.5217
0.9218 45.0 33750 0.9610 0.5217
0.9234 46.0 34500 0.9605 0.5217
0.9392 47.0 35250 0.9602 0.5217
0.9497 48.0 36000 0.9600 0.525
0.9139 49.0 36750 0.9599 0.5233
0.8915 50.0 37500 0.9599 0.5233

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2