beit-base-patch16-224-pt22k-ft22k-bloodmnist-fold-4

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the medmnist-v2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0823
• Accuracy: 0.9701
• Precision: 0.9658
• Recall: 0.9691
• F1: 0.9674

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: 0.005
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• 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: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.5292	1.0	196	0.2523	0.9043	0.8930	0.8908	0.8904
0.4439	2.0	392	0.2771	0.9008	0.8926	0.8870	0.8871
0.4637	3.0	588	0.3341	0.8762	0.8776	0.8340	0.8417
0.409	4.0	784	0.2713	0.9122	0.9096	0.9224	0.9082
0.3775	5.0	980	0.1678	0.9447	0.9498	0.9347	0.9404
0.326	6.0	1176	0.1915	0.9368	0.9393	0.9246	0.9301
0.2564	7.0	1372	0.1424	0.9464	0.9449	0.9400	0.9399
0.2204	8.0	1568	0.1173	0.9570	0.9528	0.9521	0.9515
0.1519	9.0	1764	0.0978	0.9596	0.9518	0.9616	0.9560
0.1691	10.0	1960	0.0823	0.9701	0.9658	0.9691	0.9674

Framework versions

• PEFT 0.15.2
• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.2