[D] Extracting the class labels and bounding boxes for objects, from a YOLO7 model after converting to an ONNX model

1. yolov7

   1 35 13,797 1.3 Jupyter Notebook

   Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

   

   (Please note, this is a re-post of my original question here, I think this subreddit might be more appropriate for asking this question)At work, we use Unity, we have a project that needs object detection and classification. We decided to use this YOLO7 model (for non-technical reasons, It had to be the exact same model as the company does have pre-trained weights for this exact model). However, Unity only supports ONNX so I exported the model as an ONNX model, using the code provided in the repo:

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. barracuda-release

   2 1 587 3.1 C#

   

   I removed --end2end from the export flags, which removed the warning, and for the Resize warning I followed the advice on a similar issue

4. ONNX-YOLOv7-Object-Detection

   3 2 207 0.0 Python

   Python scripts performing object detection using the YOLOv7 model in ONNX.

   

   Finally, I tried to look if someone has done similar work for the ONNX model and I found this repo which links the same repo I am trying to use, and I believe this function is doing exactly what I want to do, but I could not understand what it is doing (I don't understand how it knows exactly where the number of detections is, and where the bounding boxes are and the class labels, etc.) furthermore, I am not sure if removing end2end and the changing the version from 12 to 9 has any effect on the output shape or it has to do with the internal layers.

5. yolov5

   4 132 53,827 8.6 Python

   YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

   

   Those dimensions suggest you need to apply (i.e. roll your own) non-max suppresion to the outputs: relevant link

Suggest a related project