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sign_language_classification_v1

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3445
  • Accuracy: 0.8056

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • 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
3.2889 1.0 8 3.2714 0.0556
3.2492 2.0 16 3.2615 0.125
3.2263 3.0 24 3.2034 0.125
3.1271 4.0 32 3.1297 0.2083
2.9592 5.0 40 3.0655 0.2639
2.9414 6.0 48 2.9282 0.3472
2.7337 7.0 56 2.8254 0.4028
2.6683 8.0 64 2.6909 0.4583
2.5837 9.0 72 2.5904 0.5417
2.4566 10.0 80 2.5380 0.5833
2.2188 11.0 88 2.4682 0.5417
2.2885 12.0 96 2.3196 0.5833
2.005 13.0 104 2.2824 0.6667
1.9293 14.0 112 2.1967 0.6389
1.8396 15.0 120 2.0287 0.7361
1.7066 16.0 128 2.0357 0.7361
1.6911 17.0 136 1.9670 0.7361
1.6285 18.0 144 1.9186 0.7361
1.6064 19.0 152 1.9239 0.6944
1.6067 20.0 160 1.7723 0.7778
1.4094 21.0 168 1.7701 0.75
1.4664 22.0 176 1.7453 0.75
1.3255 23.0 184 1.7103 0.7083
1.3253 24.0 192 1.7216 0.7778
1.2416 25.0 200 1.5770 0.7778
1.1696 26.0 208 1.5099 0.7917
1.1645 27.0 216 1.4630 0.7917
1.0646 28.0 224 1.4989 0.7917
1.0149 29.0 232 1.5569 0.7222
1.0799 30.0 240 1.3602 0.8333
0.9528 31.0 248 1.3782 0.8472
1.0461 32.0 256 1.3698 0.8333
0.9019 33.0 264 1.3251 0.8611
0.9494 34.0 272 1.3586 0.8472
0.9439 35.0 280 1.3526 0.8333
0.9089 36.0 288 1.2728 0.8333
0.8962 37.0 296 1.3006 0.7917
0.9482 38.0 304 1.2592 0.8611
0.8804 39.0 312 1.3527 0.7778
0.8348 40.0 320 1.2759 0.8056
0.7823 41.0 328 1.3071 0.8194
0.8944 42.0 336 1.2428 0.8194
0.9677 43.0 344 1.2903 0.7778
0.9584 44.0 352 1.3119 0.7639
0.8342 45.0 360 1.3502 0.7778
0.7878 46.0 368 1.1941 0.8333
0.7817 47.0 376 1.2670 0.8056
0.812 48.0 384 1.2068 0.8194
0.9714 49.0 392 1.3480 0.75
0.9362 50.0 400 1.4028 0.7083

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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85.8M params
Tensor type
F32
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Finetuned from