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YOLO-NAS usage

NOTE: The yaml file is not required.

Convert model

1. Download the YOLO-NAS repo and install the requirements

git clone https://github.com/Deci-AI/super-gradients.git
cd super-gradients
pip3 install -r requirements.txt
python3 setup.py install
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_yolonas.py file from models-convertion/yolo/onnx directory to the super-gradients folder.

3. Download the model

Download the pth file from YOLO-NAS releases (example for YOLO-NAS S)

wget https://sghub.deci.ai/models/yolo_nas_s_coco.pth

NOTE: You can use instead your trained model weights.

4. Convert model

Generate the ONNX model file (example for YOLO-NAS S)

python3 export_yolonas.py -m yolo_nas_s -w yolo_nas_s_coco.pth --dynamic

NOTE: Model names

-m yolo_nas_s

or

-m yolo_nas_m

or

-m yolo_nas_l

NOTE: Number of classes (example for 80 classes)

-n 80

or

--classes 80

NOTE: To change the inference size (defaut: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model

--simplify

NOTE: To use dynamic batch-size

--dynamic

NOTE: To use implicit batch-size (example for batch-size = 4)

--batch 4

NOTE: The default opset is 14.

--opset 12

5. Upload the generated ONNX file

On Lumeo's Console click Design -> AI Models -> Add model and fill the form using those settings:

Types & Weights tab

  • Format: ONNX Lumeo YOLO
  • Capability: Detection
  • Architecture: YOLO-NAS
  • Labels: Type the labels or import the labels file (each line represents a different model's class)
  • Weights: Upload the previously generated ONNX file

Parameters tab

  • Net scale factor: 0.0039215698 (click in 1/256)
  • Color format: RGB
  • Network precision: Float16
  • Clustering algorithm: NMS