Hi NXP Team!
I am aware that I can import example from SDK called "tensorflow_lite_label_image" (which is example for image classification). I followed the lab called "eIQ Transfer Learning Lab" which is based on this SDK example (https://community.nxp.com/docs/DOC-343827 )
However, the question is: what about object detection on i.MXRT? Do you provide any examples, guides?
as eIQ toolkit is avialable now, and it supports working with Classification and Detection models
could you provide any guidance on the workflow how to use Detection models and then deploy on embedded side?
with eIQ Portal there aren't any huge differences in the flow between classification and object detection. The main difference from a user perspective is labeling the various objects (instead of whole images) in the dataset curator. When your dataset is ready, you just need to choose an object detection model and afterwards the workflow is the same.
If you have any specific questions, please create a new standalone thread so that we don't keep reviving this old one. Let's discuss everything in a fresh one with a more specific subject so that it's easier to find for others who look for the same information.
Hi, we are also working on a similar object detection example for tensorflow lite in which we are facing an issue, which i have posted in the forum (https://community.nxp.com/t5/eIQ-Machine-Learning-Software/How-to-add-custom-operators-in-tensorflow...). It would be a great help if you can find any solution for this, so that we all can benefit. Thanks
I'd like to raise the same question again, since it's been over 1 year since the last post. Does NXP have an object detection example for the i.MX RT devices now? Thanks.