Hugging Face's logo

hkivancoral
/
smids_5x_deit_small_sgd_0001_fold2

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
smids_5x_deit_small_sgd_0001_fold2 / README.md
hkivancoral's picture
hkivancoral
End of training
9c6f188
preview code
|
raw
history blame contribute delete
4.87 kB
metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_sgd_0001_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: 0.8036605657237936

smids_5x_deit_small_sgd_0001_fold2

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

  • Loss: 0.5006
  • Accuracy: 0.8037

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
  • 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
1.0629 1.0 375 1.0383 0.4592
1.0151 2.0 750 1.0009 0.4925
0.9588 3.0 1125 0.9619 0.5574
0.924 4.0 1500 0.9255 0.5890
0.8743 5.0 1875 0.8899 0.6290
0.8177 6.0 2250 0.8563 0.6522
0.7888 7.0 2625 0.8262 0.6755
0.7921 8.0 3000 0.7964 0.7005
0.7372 9.0 3375 0.7699 0.7138
0.7291 10.0 3750 0.7453 0.7221
0.7295 11.0 4125 0.7221 0.7255
0.6995 12.0 4500 0.7007 0.7288
0.621 13.0 4875 0.6811 0.7388
0.6398 14.0 5250 0.6638 0.7504
0.6383 15.0 5625 0.6483 0.7587
0.5747 16.0 6000 0.6341 0.7587
0.6097 17.0 6375 0.6214 0.7604
0.594 18.0 6750 0.6099 0.7604
0.5533 19.0 7125 0.5997 0.7654
0.5984 20.0 7500 0.5904 0.7687
0.5406 21.0 7875 0.5822 0.7720
0.525 22.0 8250 0.5743 0.7704
0.5434 23.0 8625 0.5673 0.7720
0.5253 24.0 9000 0.5609 0.7737
0.5143 25.0 9375 0.5549 0.7754
0.5351 26.0 9750 0.5494 0.7787
0.5716 27.0 10125 0.5444 0.7787
0.4849 28.0 10500 0.5399 0.7820
0.4878 29.0 10875 0.5357 0.7887
0.4887 30.0 11250 0.5319 0.7920
0.4866 31.0 11625 0.5283 0.7920
0.5025 32.0 12000 0.5250 0.7937
0.4672 33.0 12375 0.5219 0.7903
0.4395 34.0 12750 0.5192 0.7887
0.473 35.0 13125 0.5166 0.7920
0.4458 36.0 13500 0.5143 0.7920
0.4639 37.0 13875 0.5122 0.7937
0.4488 38.0 14250 0.5103 0.7953
0.4766 39.0 14625 0.5086 0.7970
0.4603 40.0 15000 0.5071 0.7987
0.4461 41.0 15375 0.5058 0.8003
0.4671 42.0 15750 0.5046 0.8003
0.4415 43.0 16125 0.5036 0.8020
0.4496 44.0 16500 0.5027 0.8020
0.4327 45.0 16875 0.5020 0.8020
0.5062 46.0 17250 0.5015 0.8020
0.4692 47.0 17625 0.5010 0.8037
0.426 48.0 18000 0.5008 0.8037
0.518 49.0 18375 0.5006 0.8037
0.4765 50.0 18750 0.5006 0.8037

Framework versions

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