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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/convnext-base-224-22k
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Validated_Balanced_Raw_Data_model_boost9
    results: []

Validated_Balanced_Raw_Data_model_boost9

This model is a fine-tuned version of facebook/convnext-base-224-22k on the Logiroad/Validated_Balanced_Raw_Dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2586
  • Accuracy: 0.4151

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 1337
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 25.0
  • mixed_precision_training: Native AMP
  • label_smoothing_factor: 0.05

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3942 1.0 80 1.3566 0.3349
1.3192 2.0 160 1.3104 0.3585
1.2795 3.0 240 1.2999 0.3726
1.2419 4.0 320 1.2860 0.3726
1.2213 5.0 400 1.2894 0.3679
1.2287 6.0 480 1.2863 0.3632
1.2123 7.0 560 1.2879 0.3915
1.2124 8.0 640 1.2767 0.3868
1.2144 9.0 720 1.2851 0.3726
1.2202 10.0 800 1.2683 0.3962
1.1804 11.0 880 1.2659 0.4009
1.2031 12.0 960 1.2658 0.3962
1.1428 13.0 1040 1.2621 0.4057
1.1224 14.0 1120 1.2655 0.4104
1.1486 15.0 1200 1.2606 0.3962
1.1451 16.0 1280 1.2636 0.4057
1.1717 17.0 1360 1.2596 0.4057
1.1231 18.0 1440 1.2626 0.4057
1.1468 19.0 1520 1.2617 0.3962
1.0958 20.0 1600 1.2586 0.4151
1.1456 21.0 1680 1.2587 0.4104
1.127 22.0 1760 1.2590 0.4151
1.1308 23.0 1840 1.2586 0.4151
1.1433 24.0 1920 1.2585 0.4151
1.1492 25.0 2000 1.2585 0.4151

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.3.0
  • Datasets 3.1.0
  • Tokenizers 0.20.3