This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0434
Accuracy: 0.9908

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-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.9
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9688	1.0	122	1.8425	0.2775
1.4822	2.0	244	1.3833	0.5457
1.1239	3.0	366	0.9321	0.6680
0.8686	4.0	488	0.6691	0.7698
0.5234	5.0	610	0.4872	0.8335
0.5246	6.0	732	0.3586	0.8736
0.3691	7.0	854	0.3134	0.8993
0.4708	8.0	976	0.2069	0.9394
0.1694	9.0	1098	0.1832	0.9414
0.2749	10.0	1220	0.1198	0.9640
0.1777	11.0	1342	0.0845	0.9733
0.1529	12.0	1464	0.0434	0.9908

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

ALM-AHME/beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20

beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ALM-AHME/
beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20