convnextv2-tiny-22k-224-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/convnextv2-tiny-22k-224 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4373
  • Accuracy: 0.8636
  • Precision: 0.8625
  • Recall: 0.8636
  • F1: 0.8603

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
1.5665 0.99 62 1.3996 0.5287 0.5180 0.5287 0.4897
0.8598 2.0 125 0.7433 0.7463 0.7490 0.7463 0.7396
0.7163 2.99 187 0.5703 0.7948 0.7919 0.7948 0.7863
0.5858 4.0 250 0.5194 0.8269 0.8292 0.8269 0.8190
0.5382 4.99 312 0.4936 0.8309 0.8314 0.8309 0.8302
0.5546 6.0 375 0.5054 0.8292 0.8366 0.8292 0.8234
0.5067 6.99 437 0.4817 0.8281 0.8324 0.8281 0.8278
0.4617 8.0 500 0.4565 0.8501 0.8545 0.8501 0.8497
0.4619 8.99 562 0.4382 0.8534 0.8520 0.8534 0.8498
0.4416 10.0 625 0.4330 0.8529 0.8505 0.8529 0.8504
0.4267 10.99 687 0.4274 0.8574 0.8575 0.8574 0.8566
0.3919 12.0 750 0.4407 0.8585 0.8604 0.8585 0.8563
0.3929 12.99 812 0.4373 0.8636 0.8625 0.8636 0.8603
0.3989 14.0 875 0.4351 0.8585 0.8602 0.8585 0.8577
0.3426 14.99 937 0.4476 0.8495 0.8500 0.8495 0.8484
0.361 16.0 1000 0.4463 0.8517 0.8505 0.8517 0.8501
0.2996 16.99 1062 0.4694 0.8596 0.8604 0.8596 0.8579
0.3394 18.0 1125 0.4494 0.8523 0.8526 0.8523 0.8517
0.3207 18.99 1187 0.4863 0.8506 0.8502 0.8506 0.8496
0.2993 20.0 1250 0.4748 0.8551 0.8516 0.8551 0.8521
0.287 20.99 1312 0.4980 0.8467 0.8436 0.8467 0.8434
0.3331 22.0 1375 0.4829 0.8546 0.8530 0.8546 0.8519
0.2852 22.99 1437 0.4943 0.8512 0.8520 0.8512 0.8508
0.2813 24.0 1500 0.4796 0.8574 0.8574 0.8574 0.8568
0.2807 24.99 1562 0.4811 0.8596 0.8576 0.8596 0.8576
0.2609 26.0 1625 0.4786 0.8608 0.8589 0.8608 0.8592
0.2571 26.99 1687 0.4777 0.8608 0.8605 0.8608 0.8602
0.2807 28.0 1750 0.4879 0.8596 0.8580 0.8596 0.8582
0.2578 28.99 1812 0.4829 0.8557 0.8550 0.8557 0.8549
0.2543 29.76 1860 0.4833 0.8563 0.8555 0.8563 0.8554

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1
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