models VS tensorboard

Uh, maybe they didn't feel the need to look. I already pointed you to the ONNX project. Here are some ONNX-based. These are just the ones being shared with the community. The limit of AMD's responsibility is writing the low-level libraries to support ONNX.

I am new to object detection models and I need help running my object detection Darknet YOLOv4-Tiny Model In Unity Barracuda. I trained my model and then i converted it to ONNX format with 2 methods. One method was using pytorch-YOLOv4 from github and the other by converting my model to tensorflow and then to onnx and shown here: "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb"

Then i tried to convert it again using another method that i found here "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb" in order to convert it from darknet to tensorflow and then to onnx but i didn't have any luck.

Here we use the GPT-2 model distributed by the ONNX official. Download GPT-2-LM-HEAD from the link.

Download pre-trained models from the ONNX Model Zoo

Instructions to reproduce the problem: I am trying to convert a proprietary model at work but for now i'll use mobilenetv2-7.onnx to explain/reproduce the issue.

One approach could be run a pre-trained object detector (like one of these) on each frame and then a simple object tracker on top of it (like this).

Some form of 3D histograms? And then "discretized"/binned for each layer too. Apparently Tensorboard has them: https://github.com/tensorflow/tensorboard/blob/master/docs/r1/histograms.md

Consider filing an issue? https://github.com/tensorflow/tensorboard/issues

The async code is in our server.rs and cli.rs, because this exposes a Tonic server and Tonic is all-in on async.

A quick google search is telling me that this is possible but very poorly documented / communicated: https://github.com/tensorflow/tensorboard/issues/767

The cool part about TensorBoard is that we may run it directly in Google Colab. However, if you're running the notebook in your local installation of Jupyter you may also install it as Python package and launch it from the terminal.