Welcome to yolort’s documentation!

What is yolort?

yolort focus on making the training and inference of the object detection task integrate more seamlessly together. yolort now adopts the same model structure as the official YOLOv5. The significant difference is that we adopt the dynamic shape mechanism, and within this, we can embed both pre-processing (letterbox) and post-processing (nms) into the model graph, which simplifies the deployment strategy. In this sense, yolort makes it possible to be deployed more friendly on LibTorch, ONNX Runtime, TensorRT, TVM and so on.

About the code

Follow the design principle of detr:

object detection should not be more difficult than classification, and should not require complex libraries for training and inference.

yolort is very simple to implement and experiment with. You like the implementation of torchvision’s faster-rcnn, retinanet or detr? You like yolov5? You love yolort!

Quick get stated

Read a source of image(s) and detect its objects:

from yolort.models import yolov5s

# Load model
model = yolov5s(pretrained=True, score_thresh=0.45)
model.eval()

# Perform inference on an image file
predictions = model.predict("bus.jpg")
# Perform inference on a list of image files
predictions = model.predict(["bus.jpg", "zidane.jpg"])

Loading checkpoint from official yolov5

And we support loading the trained weights from YOLOv5. Please see our documents on what we share and how we differ from yolov5 for more details.

from yolort.models import YOLOv5
from yolort.v5 import attempt_download

# will downloaded from 'https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5n6.pt'
model_path = "yolov5n6.pt"
checkpoint_path = attempt_download(model_path)

model = YOLOv5.load_from_yolov5(checkpoint_path, score_thresh=0.25)

model.eval()
img_path = "bus.jpg"
predictions = model.predict(img_path)

Use Cases and Solutions

Getting Started

Tutorials

Deployment

API Reference