I have experimented with a number of different machine learning models used for pose estimation. Most of them output a heatmap and offsets for the detected person(s) in the image. I really like the performance/accuracy of the multipose estimation model here.
What I would like to do next is to create a model similar to this one, except it should label each pose of the person(s) detected. There are multiple different implementations caffe/pytorch/tensorflow to choose from. I've thought about how to approach this and I have thought of a few different ways:
- Create a completely new machine learning model and use the labeled output of the pose estimation model to train it.
- Change or add layers to the machine learning model to change the output. (Not sure how this is done)
- Ditch the pose estimate model and train a new model to directly estimate using raw images/labels of cropped people. This would rely on another method to detect each person.
I want to take the path of least resistance here but I also care about the time it takes to gather/process data, and most importantly the accuracy/performance of the model. Are there any experienced Machine Learning/Data Scientists who answer the following?
- Which approach should I take? advantages/disadvantages
- Which machine learning library offers the functions to do this.
- My assumption is that option 1 or 2 would be more accurate than option 3. Am I correct?