pneumoniamnist-beit-base-finetuned

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: 0.3960
• Accuracy: 0.8446
• Precision: 0.8354
• Recall: 0.8312
• F1: 0.8332

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.5947	0.9898	73	0.5165	0.7424	0.3712	0.5	0.4261
0.4888	1.9932	147	0.3450	0.8569	0.8116	0.8190	0.8151
0.4022	2.9966	221	0.4225	0.8340	0.7914	0.8567	0.8079
0.4319	4.0	295	0.3600	0.8588	0.8123	0.8589	0.8292
0.3836	4.9898	368	0.3665	0.8511	0.8054	0.8610	0.8233
0.3887	5.9932	442	0.3667	0.8645	0.8197	0.8749	0.8383
0.3947	6.9966	516	0.3951	0.8531	0.8098	0.8744	0.8283
0.3741	8.0	590	0.3449	0.8683	0.8229	0.8678	0.8398
0.3964	8.9898	663	0.3625	0.8588	0.8128	0.8638	0.8305
0.3845	9.8983	730	0.3569	0.8569	0.8111	0.8649	0.8292

Framework versions

• PEFT 0.11.1
• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1