yolo_e2e

Implementation of End-to-End YOLO Models for TensorRT

This repository supports segmentation and detection models from the YOLO series - YOLO11, YOLOv10, YOLOv9, YOLOv8, and YOLOv7.

Use the repository https://github.com/levipereira/ultralytics specifically for exporting the YOLO11, YOLOv10, and YOLOv8 models.

All YOLO series models are implemented with End2End Deep Learning, incorporating three key features:

1. Dynamic Shapes - TensorRT enables the creation of network resolutions different from the original exported ONNX.
2. Dynamic Batch Size - Dynamically adjusts the batch size to maximize model performance according to the GPU's capabilities.
3. NMS-Free - Achieved in two different ways:
   1. Native NMS-Free - Models natively support NMS-Free, available for some YOLOv9 models and all YOLOv10 detection models.
   2. TensorRT Plugins - Utilizes TensorRT EfficientNMS plugin for detection models, and EfficientNMSX and ROIAlign for segmentation models.

All detection models across the YOLO series adhere to standardized output layers:

• num_det: Represents the number of detections.
• det_boxes: Provides the bounding boxes coordinates for each detected object.
• det_scores: Indicates the confidence score associated with each detected object.
• det_classes: Specifies the class label or category assigned to each detected object.

Similarly, instance segmentation models from the YOLO series also maintain standardized output layers:

• num_det: Represents the count of detected instances.
• det_boxes: Provides bounding box coordinates for each detected instance.
• det_scores: Indicates the confidence score associated with each detected instance.
• det_classes: Specifies the class label assigned to each detected instance.
• det_masks: Provides the segmentation masks corresponding to each detected instance.