Closed ashnarayan13 closed 6 years ago
I don't think there is a limit to the number of objects tracked in each frame, and the current implementation can track objects of different classes (by giving a list of class names to the trackObj FLAG) but it will not keep the name of the classes while tracking , it will just show the tracking number ID
Thanks! Also, what weights do I use, using YOLO weights is giving a tensor flow error. The yolo weights from the official darknet website if yolo weights for the C and CUDA implementation, where can I get the Tensorflow weights and cfg?
FLAGS.pbLoad = "tiny-yolo-voc-traffic.pb" # tensorflow model FLAGS.metaLoad = "tiny-yolo-voc-traffic.meta" # tensorflow weights
Thanks again!
you can get YOLO weights from here, the cfg are already there by default
Thanks. The weights don't seem to be a problem. Tensorflow is not built for CUDA 9 yet and hence the run.py is not working. Even after setting GPU to 0. I get a tensorflow error
Traceback (most recent call last):
File "run.py", line 3, in
Failed to load the native TensorFlow runtime.
the error is because you are using tensorflow-gpu , and yes tensorflow does not support cuda-9 yet , you can install cuda-8 simply using apt-get install cuda-8.0
Got it working on CPU instead. Thank you so much for the quick response!
I wanted to know how many objects can be tracked in each frame as the README doesn't clearly state it.
Also, I wanted to know if this can be extended to track objects of different classes. Like, people, animals simultaneously. (Yolo can do that, but can the tracking keep the IDs?)
Thanks