Histology is studying the microanatomy of cells, tissues, and organs as seen through a microscope. It examines the correlation between structure and function. Automated annotation of skin biopsy histopathological images provides valuable information and support for diagnosis, especially for the discrimination among diseases. Currently, computer-aid analysis of skin biopsy images mostly relies on some human-designed features, which require expensive human efforts and experiences in problem domains. The project addresses the problem of finding visual patterns in histology image collections.  The histology annotation is developed by applying PyTorch based ResNet encoder with an Intersection over Union (IoU) score above 90% for the semantic segmentation of histology cell images to segment the hair follicle, sebaceous glands, and eccrine gland.