With the increase in the number of yoga practitioners every year, the risk of injuries as a result of incorrect yoga postures has also increased. A selftraining model that can evaluate the posture of individuals is the optimal solution for this issue. This objective can be attained with the aid of computer vision and deep learning. A model that can detect theyoga pose performed by an individual, evaluate it in comparison to the pose performed by an expert, and provide the individual with instructive feedback would be an effective solution to this problem. Recently, numerous researchers have conducted experiments on the detection and performance of yoga poses in real time. This paper discusses the methods undertaken in brief and compares the tools and algorithms they used for conducting pose estimation, pose detection as well as pose assessment. Itdiscusses the accuracy, precision, and similarity of pose classification obtained by the researchers and the future scope of the research.