netron VS models

In exploring open-source projects, I've come across several promising tools capable of managing deep-learning models for images. Significantly, tools such as NETRON provide visualization of neural networks, while SHAP can be used for evaluating the significance of outputs.

We need to find out the exact input and output tensor names. A tool like Netron makes this super easy. Open the original .tflite and/or the ONNX model in Netron and click the Model Properties button in the lower left corner.

How is this different from e.g Netron https://github.com/lutzroeder/netron

Netron (https://netron.app/) is the best and mostly used NN visualizer. Just save your model and then simply load it via netron to look its layers and weights. If you want a more complex visualization then you can also play with Zetane ( but its paid, also have a free version) engine.

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).