Improving Resnet Model In Safety Gear Classification Using Finest Optimizer

The Occupational accidents that occur in the work environment are increasing day by day. This is caused by workers' non-compliance with the established work safety equipment. Although the supervision of the use of work safety equipment has been carried out, it is still done manually involving less effective human resources. Therefore, it is necessary to develop an intelligent model that can classify the use of work safety equipment more accurately. This study uses the pre-trained ResNet50 model and is combined with the best optimization model to improve accuracy. The results of the study showed that the RMSProp optimization model has better performance with an accuracy value of 97.01% in the 17th epoch of 50 epochs of data training and with training loss and validation loss values ​​of 0.3268 and 0.145, respectively. Testing of 20 images with each image, 10 images using safety equipment, and 10 images not using safety equipment can be classified correctly.