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MxFit - A repcounting web-application

The MxFit is a simple web-application based on Yolov8 pose estimation that serves as exercise rep counter. Application showcases the ease of integration with using memryX runtime python API.

Overview

Property Details
Model Yolov8m-pose
Model Type Pose Estimation
Framework ONNX
Model Source Download from Ultralytics GitHub or docs
Pre-compiled DFP Download here
Model Resolution 640x640
Output Person bounding boxes and pose landmark coordinates
OS Linux
License AGPL

Demo

Demo GIF

Requirements

Before running the application, ensure that Python and required packages are installed. You can install the required packages using the following command:

The mx env created for sdk installation can be used

. mx/bin/activate
pip install -r freeze

Running the Application

Step 1: Download Pre-compiled DFP

To download and unzip the precompiled DFPs, use the following commands:

wget https://developer.memryx.com/model_explorer/2p2/YOLO_v8_medium_pose_640_640_3_onnx.zip
mkdir -p models
unzip YOLO_v8_medium_pose_640_640_3_onnx.zip -d models
(Optional) Download and compile the model yourself If you prefer, you can download and compile the model rather than using the precompiled model. Download the pre-trained YOLOv8m-pose model and export it to ONNX:

You can use the following code to download the pre-trained yolov8m-pose.pt model and export it to ONNX format:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8m-pose.pt")  # load an official model

# Export the model
model.export(format="onnx")

You can now use the MemryX Neural Compiler to compile the model and generate the DFP file required by the accelerator:

mx_nc -v -m yolov8m-pose.onnx --autocrop -c 4

Output: The MemryX compiler will generate two files:

  • yolov8m-pose.dfp: The DFP file for the main section of the model.
  • yolov8m-pose_post.onnx: The ONNX file for the cropped post-processing section of the model.

Additional Notes:

  • -v: Enables verbose output, useful for tracking the compilation process.
  • --autocrop: This option ensures that any unnecessary parts of the ONNX model (such as pre/post-processing not required by the chip) are cropped out.

Step 2: Run the Application

With the compiled model, you can now run the application

Simply execute the following command:

cd src
python main.py

Following output will be printed on the terminal, ctrl + click on the link to open the webapp

INFO:     Started server process [17126]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

Third-Party Licenses

This project uses third-party software, models, and libraries. Below are the details of the licenses for these dependencies: