Peripheral blood smear examination is one of the basic steps in the evaluation of different blood cells. It is a confirmatory step after an automated complete blood count analysis. Manual microscopy is time-consuming and requires professional laboratory expertise. Therefore, the turn-around time for peripheral smear in a health care center is approximately 3-4 hours. To avoid the traditional method of manual counting under the microscope a computerized automation of peripheral blood smear examination has been adopted, which is a challenging task in medical diagnostics. In recent times, deep learning techniques have overcome the challenges associated with human microscopic evaluation of peripheral smears and this has led to reduced cost and precise diagnosis. However, their application can be significantly improved by the availability of annotated datasets. This study presents a large customized annotated blood cell dataset (named the Bio-Net dataset from healthy individuals) and blood cell detection and counting in the peripheral blood smear images. A mini-version of the dataset for specialized WBC-based image processing tasks is also equipped to classify the healthy and mature WBCs in their respective classes. An object detection algorithm called You Only Look Once (YOLO) with a refashion disposition has been trained on the novel dataset to automatically detect and classify blood cells into RBCs, WBCs, and platelets and compare the results with other publicly available datasets to highlight the versatility. In short the introduction of the Bio-Net dataset and AI-powered detection and counting offers a significant potential for advancement in biomedical research for analyzing and understanding biological data.