Optimize Tensorflow Object Detection Model V2 Centernet Model for Evaluation - tensorflow

Question

I am using the tensorflow centernet_resnet50_v2_512x512_kpts_coco17_tpu-8 object detection model on a Nvidia Tesla P100 to extract bounding boxes and keypoints for detecting people in a video. Using the pre-trained from tensorflow.org, I am able to process about 16 frames per second. Is there any way I can imporve the evaluation speed for this model? Here are some ideas I have been looking into:
Pruning the model graph since I am only detecting 1 type of object (people)
Have not been successful in doing this. Changing the label_map when building the model does not seem to improve performance.
Hard coding the input size
Have not found a good way to do this.
Compiling the model to an optimized form using something like TensorRT
Initial attempts to convert to TensorRT did not have any performance improvements.
Batching predictions
It looks like the pre-trained model has the batch size hard coded to 1, and so far when I try to change this using the model_builder I see a drop in performance.
My GPU utilization is about ~75% so I don't know if there is much to gain here.

Answer 1

TensorRT should in most cases give a large increase in frames per second compared to Tensorflow.
centernet_resnet50_v2_512x512_kpts_coco17_tpu-8 can be found in the TensorFlow Model Zoo.
Nvidia has released a blog post describing how to optimize models from the TensorFlow Model Zoo using Deepstream and TensorRT:
https://developer.nvidia.com/blog/deploying-models-from-tensorflow-model-zoo-using-deepstream-and-triton-inference-server/
Now regarding your suggestions:
Pruning the model graph: Pruning the model graph can be done by converting your tensorflow model to a TF-TRT model.
Hardcoding the input size: Use the static mode in TF-TRT. This is the default mode and enabled by: is_dynamic_op=False
Compiling the model: My advise would be to convert you model to TF-TRT or first to ONNX and then to TensorRT.
Batching: Specifying the batch size is also covered in the NVIDIA blog post.
Lastly, for my model a big increase in performance came from using FP16 in my inference engine. (mixed precision) You could even try INT8 although then you first have to callibrate.