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metadata
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: convnextv2-tiny-1k-224-finetuned-two-four
    results: []

convnextv2-tiny-1k-224-finetuned-two-four

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5662
  • Accuracy: 0.7352
  • F1: 0.7327
  • Precision: 0.7370
  • Recall: 0.7352

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.6983 0.9655 14 0.6720 0.5974 0.5694 0.6054 0.5974
0.6804 2.0 29 0.6609 0.6324 0.6190 0.6374 0.6324
0.6796 2.9655 43 0.6634 0.6083 0.6084 0.6084 0.6083
0.6886 4.0 58 0.6547 0.6171 0.6104 0.6161 0.6171
0.6577 4.9655 72 0.6577 0.6127 0.5724 0.6407 0.6127
0.6439 6.0 87 0.6196 0.6477 0.6339 0.6559 0.6477
0.602 6.9655 101 0.6125 0.6652 0.6585 0.6986 0.6652
0.5974 8.0 116 0.6224 0.6696 0.6601 0.7141 0.6696
0.5841 8.9655 130 0.5800 0.7002 0.7005 0.7011 0.7002
0.581 10.0 145 0.5822 0.7265 0.7262 0.7262 0.7265
0.5716 10.9655 159 0.5812 0.7068 0.7035 0.7083 0.7068
0.5611 12.0 174 0.5778 0.7221 0.7150 0.7319 0.7221
0.5411 12.9655 188 0.5652 0.7352 0.7341 0.7351 0.7352
0.5361 14.0 203 0.5670 0.7374 0.7347 0.7395 0.7374
0.5416 14.4828 210 0.5662 0.7352 0.7327 0.7370 0.7352

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1