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CheXpert-5-convnextv2-tiny-384

This model is a fine-tuned version of facebook/convnextv2-tiny-22k-384 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1009
  • Auroc Atelectasis: 0.7943
  • Auroc Cardiomegaly: 0.8187
  • Auroc Consolidation: 0.9269
  • Auroc Edema: 0.9233
  • Auroc Pleural effusion: 0.9315
  • Specificity Atelectasis: 0.7891
  • Specificity Cardiomegaly: 1.0
  • Specificity Consolidation: 0.9948
  • Specificity Edema: 0.8407
  • Specificity Pleural effusion: 0.8038
  • Exact Match: 0.4464
  • Hamming Distance: 0.1804

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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 2500
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Auroc Atelectasis Auroc Cardiomegaly Auroc Consolidation Auroc Edema Auroc Pleural effusion Specificity Atelectasis Specificity Cardiomegaly Specificity Consolidation Specificity Edema Specificity Pleural effusion Exact Match Hamming Distance
0.0891 1.0 6120 0.0893 0.7323 0.8366 0.7020 0.8387 0.8702 0.4510 0.9661 1.0 0.6596 0.5392 0.2616 0.2444
0.0854 2.0 12240 0.0831 0.7535 0.8556 0.7350 0.8651 0.8881 0.7293 0.9042 0.9936 0.7083 0.6259 0.3571 0.1973
0.082 3.0 18360 0.0824 0.7683 0.8696 0.7473 0.8720 0.8961 0.6956 0.8196 0.9881 0.6087 0.6611 0.3298 0.2177
0.0799 4.0 24480 0.0802 0.7749 0.8720 0.7562 0.8783 0.9005 0.7450 0.8831 0.9608 0.7341 0.6984 0.3802 0.1880
0.0759 5.0 30600 0.0793 0.7795 0.8746 0.7583 0.8818 0.9030 0.7277 0.8948 0.9711 0.7618 0.7045 0.3869 0.1823
0.0739 6.0 36720 0.0798 0.7787 0.8727 0.7561 0.8812 0.9031 0.7461 0.8921 0.9690 0.7487 0.7074 0.3886 0.1824

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.1.2
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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