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Hemg/sign-language-classification

🤗 Hugging Face sourceimage-classificationapache-2.086M params343 MBsafetensors✓ 5 checksumsupdated today
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sign-language-classification

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: 0.1351
  • Accuracy: 0.96

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: 0.0002
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 32

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.6016 1.0 100 1.5038 0.8
1.1072 2.0 200 0.6959 0.8675
0.6195 3.0 300 0.5236 0.87
0.5559 4.0 400 0.4819 0.87
0.389 5.0 500 0.3392 0.9
0.3878 6.0 600 0.3600 0.9025
0.3309 7.0 700 0.3312 0.9075
0.3397 8.0 800 0.2596 0.9225
0.3033 9.0 900 0.2056 0.935
0.2765 10.0 1000 0.2802 0.9175
0.2846 11.0 1100 0.3276 0.9025
0.2443 12.0 1200 0.3689 0.8975
0.2682 13.0 1300 0.2805 0.915
0.2053 14.0 1400 0.2437 0.9225
0.2453 15.0 1500 0.2646 0.92
0.1896 16.0 1600 0.2489 0.925
0.1841 17.0 1700 0.2393 0.9275
0.1406 18.0 1800 0.1935 0.945
0.1573 19.0 1900 0.2544 0.92
0.155 20.0 2000 0.1940 0.9475
0.1563 21.0 2100 0.2021 0.9325
0.133 22.0 2200 0.2413 0.9325
0.117 23.0 2300 0.1939 0.9375
0.1455 24.0 2400 0.1685 0.9575
0.144 25.0 2500 0.1787 0.9475
0.1119 26.0 2600 0.1511 0.96
0.1053 27.0 2700 0.1308 0.965
0.0964 28.0 2800 0.1042 0.9725
0.0938 29.0 2900 0.1751 0.9425
0.0881 30.0 3000 0.1066 0.965
0.0854 31.0 3100 0.1116 0.97
0.1002 32.0 3200 0.1351 0.96

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2