How to add NMS to last layer?

[rsundar96]

I want to add the NMS as part of the last layer and can see under models/yolo.py that the function nms is present (Line 173, models/yolo.py). How do I add this to my model? (Haven't made any changes to the model architecture present in the repo)

[github-actions]

👋 Hello @rsundar96, thank you for your interest in 🚀 YOLOv3! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@rsundar96 for a fully managed inference pipeline including preprocessing and postprocessing I would recommend our YOLOv5 PyTorch Hub models. These include the NMS module you posted above and much more that allow for the simplest integration into your workflow possible. See PyTorch Hub Tutorial below to get started.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Images
dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batch of images

# Inference
results = model(imgs)
results.print()  # or .show(), .save()

YOLOv5 Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Supervisely Ecosystem  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• ONNX and TorchScript Export
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• TensorRT Deployment

[LeoSouquet]

Hi guys,
Coming from @rsundar96 I was wondering how to add this layer to my custom trained models ?
From the hub I can only get pre-existing models.

Thanks in advance,

regards

[glenn-jocher]

@LeoCyclope see YOLOv5 PyTorch Hub tutorial for loading custom models.

YOLOv5 Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Supervisely Ecosystem  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• ONNX and TorchScript Export
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• TensorRT Deployment

Custom Models

This example loads a custom 20-class VOC-trained YOLOv5s model 'best.pt' with PyTorch Hub.

model = torch.hub.load('ultralytics/yolov5', 'custom', path_or_model='path/to/best.pt')  # custom model

[LeoSouquet]

Thanks for this :)
And if I want to add it without going through the torch.hub ?

Thanks in advance :)

[dragoncanfly]

Hi, have you got the way to add the postprocess(nms) in the whole model when export pytorch to onnx?

thanks in advance,

[glenn-jocher]

@dragoncanfly no need, you can run exported ONNX models directly using PyTorch Hub:

[dragoncanfly]

@dragoncanfly no need, you can run exported ONNX models directly using PyTorch Hub:

hi, @glenn-jocher, I just want export a pytorch model (yolov3) which was trained by myself. So if there is a way to modify the net structure to enable the convert, of course, with postprocess I mean. Or is there any reference?

[glenn-jocher]

@dragoncanfly You can directly implement non-maximum suppression (NMS) in the post-processing step of your PyTorch YOLOv3 model before exporting it to ONNX. You can refer to the official YOLOv3 implementation and specifically the nms algorithm present in the YOLO repository to understand how to incorporate NMS directly into the model architecture. This will ensure that the NMS step is included in the ONNX export. Good luck with your implementation!