beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd – 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-3rd

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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-3rd

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: 0.0488
Accuracy: 0.9901

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
1.9835	1.0	114	1.9296	0.2315
1.6045	2.0	229	1.4334	0.5172
1.0525	3.0	343	0.9298	0.6962
0.795	4.0	458	0.6580	0.7709
0.5739	5.0	572	0.4717	0.8366
0.5821	6.0	687	0.3511	0.8851
0.4566	7.0	801	0.2705	0.9204
0.2751	8.0	916	0.2114	0.9384
0.2352	9.0	1030	0.1303	0.9688
0.1831	10.0	1145	0.1194	0.9688
0.1515	11.0	1259	0.0673	0.9869
0.204	11.95	1368	0.0488	0.9901

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3