This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1954
Accuracy: 0.9395

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 16
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5451	0.99	68	0.2960	0.8936
0.2488	1.99	137	0.2254	0.9105
0.1986	3.0	206	0.1913	0.9282
0.1714	4.0	275	0.1906	0.9264
0.1576	4.99	343	0.1825	0.9323
0.1359	5.99	412	0.1973	0.9318
0.1193	7.0	481	0.1756	0.9368
0.1062	8.0	550	0.1743	0.9382
0.0983	8.99	618	0.1885	0.9395
0.0797	9.99	687	0.1931	0.9309
0.0698	11.0	756	0.1895	0.9359
0.0657	12.0	825	0.1861	0.9368
0.0587	12.99	893	0.1837	0.9414
0.056	13.99	962	0.1936	0.9377
0.0592	15.0	1031	0.1958	0.935
0.0508	15.83	1088	0.1954	0.9395

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Hemg/Melanoma-Cancer-Image-Classification

Melanoma-Cancer-Image-Classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hemg/
Melanoma-Cancer-Image-Classification