- (MD) A346DKX4282 - Fernandico Geovardo
- (ML) M049DSX0588 - Dhuha Ardha Saputra
- (ML) M181DSX3641 - Vincent Yovian
- (CC) C368DSX2892 - I Ketut Teguh Wibawa Lessmana P. T.
- (CC) C220DSY0626 - Audy Revi Nugraha
- (CC) C303DKY3970 - Vanessa Evlin
We create a deep neural network for classifying seven types of injuries that often happen in accidents.
We collected our Dataset various wound type from kaggle. We got 431 wound type image data in total and there is the distribution.
- Abrasions: 85
- Bruises: 122
- Burns: 59
- Cuts: 50
- Ingrown Nails: 31
- Lacerations: 61
- Stab Wounds: 23
We use the technique of transfer learning by adding multiple layers of fully connected networks on top of an InceptionV3 model pre-trained on the imagenet dataset. We freeze all the layers in the pre-trained InceptionV3 except for the last 12. We saved our best performing model in .h5 format and its weights in a .ckpt file, which so far has reached a validation accuracy of over 0.83 for the injury classification task and over 0.89 for the accident classification task. The dataset, latest checkpoints, and various model formats can be found here. Legacy versions of the model can be found here.