Once I started to install it, pip goes on an endless loop trying to install scipy with this message:
INFO: pip is looking at multiple versions of scipy to determine which version is compatible with other requirements. This could take a while.
For curiosity’s sake, I removed scipy from the required packages but the I get this error:
ERROR: Cannot install object-detection because these package versions have conflicting dependencies.
The conflict is caused by:
tf-models-official 2.4.0 depends on tensorflow-addons
tf-models-official 2.3.0 depends on tensorflow-addons
tf-models-official 2.2.2 depends on tensorflow-addons
tf-models-official 2.2.1 depends on tensorflow-addons
tf-models-official 2.2.0 depends on tensorflow-addons
To fix this you could try to:
loosen the range of package versions you’ve specified
remove package versions to allow pip attempt to solve the dependency conflict
Hi @ig-j, sorry for the delay - you will want to change the FROM statement at the top of that Dockerfile to one of the l4t-tensorflow or l4t-ml containers. Then it will use an aarch64 base container instead of x86_64 base container.
BTW you will want to pick one of the l4t-tensorflow/l4t-ml containers that has the same L4T version as you are running (which you can check with cat /etc/nv_tegra_release). For example, if you are running L4T R32.5.0 or R32.5.1, you can use nvcr.io/nvidia/l4t-tensorflow:r32.5.0-tf2.3-py3
Sorry for the delay - I’ve not yet updated these containers for the TF2.4 wheels, but if you wanted you could build the containers for TF2.4. Essentially you would replace the URL/filename of the TF2.3 wheel with the TF2.4 wheel here:
Hi i have been trying to do the same thing, running TFOD on jetson xavier. I also came across the same issue of mutiple installation of scipy. Did u find a way around this, and are u able to get TF object detection up and running on ur jetson?