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: 0.1209
Accuracy: 0.965

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: 0.0002
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0919	1.0	200	0.7780	0.76
0.6157	2.0	400	0.5695	0.7925
0.4894	3.0	600	0.3667	0.8775
0.3786	4.0	800	0.4436	0.8625
0.3142	5.0	1000	0.4412	0.8625
0.2636	6.0	1200	0.4430	0.86
0.198	7.0	1400	0.2760	0.9175
0.1456	8.0	1600	0.2211	0.93
0.1586	9.0	1800	0.3520	0.905
0.1307	10.0	2000	0.3188	0.9175
0.106	11.0	2200	0.3167	0.925
0.0975	12.0	2400	0.2633	0.92
0.0734	13.0	2600	0.1813	0.9525
0.0994	14.0	2800	0.2150	0.945
0.0622	15.0	3000	0.1757	0.955
0.0609	16.0	3200	0.1209	0.965

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Hemg/Wound-Image-classification

Wound-Image-classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hemg/
Wound-Image-classification