rsna – AI Model by dqgeng | AlphaNeural AI

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dqgeng/
rsna

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image-classification

generated_from_trainer

microsoft/resnet-50

endpoints_compatible

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rsna

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0842
Accuracy: 0.9697
Auc: 0.9606
F1: 0.6931

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
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc	F1
0.1066	1.0	18964	0.1029	0.9629	0.9349	0.6084
0.0872	2.0	37928	0.0921	0.9670	0.9502	0.6681
0.0900	3.0	56892	0.0872	0.9686	0.9563	0.6803
0.0902	4.0	75856	0.0847	0.9694	0.9594	0.6934
0.0824	5.0	94820	0.0842	0.9697	0.9606	0.6931

Framework versions

Transformers 5.0.0.dev0
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.2