beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled – AI Model by ALM-AHME | AlphaNeural AI

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

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image-classification

generated_from_trainer

microsoft/beit-large-patch16-224

autotrain_compatible

endpoints_compatible

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

This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0487
Accuracy: 0.9893

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
2.1055	1.0	114	2.0091	0.1601
1.6582	2.0	229	1.5953	0.4187
1.2399	3.0	343	1.1053	0.5977
0.8417	4.0	458	0.7602	0.7241
0.5517	5.0	572	0.5651	0.8013
0.5777	6.0	687	0.3980	0.8768
0.408	7.0	801	0.2912	0.9154
0.2395	8.0	916	0.2185	0.9417
0.3613	9.0	1030	0.1753	0.9475
0.2408	10.0	1145	0.1353	0.9614
0.2777	11.0	1259	0.0699	0.9860
0.1528	11.95	1368	0.0487	0.9893

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3