Hailo8 Application – YOLO
YOLO: The Most Widely Used Object Detection Algorithm
YOLO (You Only Look Once) is currently the most widely used object detection algorithm in computer vision. From its name, we can see that it belongs to "One-Stage Detectors". Compared with two-stage detectors, one-stage detectors are faster.
Source: Concept of YOLOv1: The Evolution of Real-Time Object Detection
The YOLO algorithm has evolved through multiple versions. YOLOv1 was inspired by GoogLeNet; before YOLOv4, it was mainly based on Darknet, and from YOLOv5 onward, PyTorch became the mainstream implementation. PyTorch and TensorFlow are the two leading deep learning frameworks today, so the industry mostly uses YOLOv5 and later versions.
Additionally, starting from YOLOv5, models are provided in different sizes without needing to manually adjust parameters as was required in YOLOv4.
Source: https://pytorch.org/hub/ultralytics_yolov5/
SUNIX Hailo-8 AI Accelerator – Optimal YOLO Inference Environment
The SUNIX Hailo-8 AI Accelerator provides a powerful inference runtime for YOLO, supporting most YOLO models such as YOLOv5, YOLOv7, and YOLOv8. In addition to the .hef files provided by the Hailo Model Zoo, users can also use the Hailo Dataflow Compiler to convert YOLO models from other runtimes into .hef files.
Using the SUNIX AIEH1000 (single Hailo8) to run YOLOv5m achieves FPS up to 241.88 Frames/sec, demonstrating that the SUNIX Hailo AI Accelerator provides inference performance comparable to mainstream GPUs.
Hailo8 Application – YOLO
YOLO: The Most Widely Used Object Detection Algorithm
YOLO (You Only Look Once) is currently the most widely used object detection algorithm in computer vision. From its name, we can see that it belongs to "One-Stage Detectors". Compared with two-stage detectors, one-stage detectors are faster.
Source: Concept of YOLOv1: The Evolution of Real-Time Object Detection
The YOLO algorithm has evolved through multiple versions. YOLOv1 was inspired by GoogLeNet; before YOLOv4, it was mainly based on Darknet, and from YOLOv5 onward, PyTorch became the mainstream implementation. PyTorch and TensorFlow are the two leading deep learning frameworks today, so the industry mostly uses YOLOv5 and later versions.
Additionally, starting from YOLOv5, models are provided in different sizes without needing to manually adjust parameters as was required in YOLOv4.
Source: https://pytorch.org/hub/ultralytics_yolov5/
SUNIX Hailo-8 AI Accelerator – Optimal YOLO Inference Environment
The SUNIX Hailo-8 AI Accelerator provides a powerful inference runtime for YOLO, supporting most YOLO models such as YOLOv5, YOLOv7, and YOLOv8. In addition to the .hef files provided by the Hailo Model Zoo, users can also use the Hailo Dataflow Compiler to convert YOLO models from other runtimes into .hef files.
Using the SUNIX AIEH1000 (single Hailo8) to run YOLOv5m achieves FPS up to 241.88 Frames/sec, demonstrating that the SUNIX Hailo AI Accelerator provides inference performance comparable to mainstream GPUs.
Hailo8 Application – YOLO
YOLO: The Most Widely Used Object Detection Algorithm
YOLO (You Only Look Once) is currently the most widely used object detection algorithm in computer vision. From its name, we can see that it belongs to "One-Stage Detectors". Compared with two-stage detectors, one-stage detectors are faster.
Source: Concept of YOLOv1: The Evolution of Real-Time Object Detection
The YOLO algorithm has evolved through multiple versions. YOLOv1 was inspired by GoogLeNet; before YOLOv4, it was mainly based on Darknet, and from YOLOv5 onward, PyTorch became the mainstream implementation. PyTorch and TensorFlow are the two leading deep learning frameworks today, so the industry mostly uses YOLOv5 and later versions.
Additionally, starting from YOLOv5, models are provided in different sizes without needing to manually adjust parameters as was required in YOLOv4.
Source: https://pytorch.org/hub/ultralytics_yolov5/
SUNIX Hailo-8 AI Accelerator – Optimal YOLO Inference Environment
The SUNIX Hailo-8 AI Accelerator provides a powerful inference runtime for YOLO, supporting most YOLO models such as YOLOv5, YOLOv7, and YOLOv8. In addition to the .hef files provided by the Hailo Model Zoo, users can also use the Hailo Dataflow Compiler to convert YOLO models from other runtimes into .hef files.
Using the SUNIX AIEH1000 (single Hailo8) to run YOLOv5m achieves FPS up to 241.88 Frames/sec, demonstrating that the SUNIX Hailo AI Accelerator provides inference performance comparable to mainstream GPUs.
Hailo8 Application – YOLO
YOLO: The Most Widely Used Object Detection Algorithm
YOLO (You Only Look Once) is currently the most widely used object detection algorithm in computer vision. From its name, we can see that it belongs to "One-Stage Detectors". Compared with two-stage detectors, one-stage detectors are faster.
Source: Concept of YOLOv1: The Evolution of Real-Time Object Detection
The YOLO algorithm has evolved through multiple versions. YOLOv1 was inspired by GoogLeNet; before YOLOv4, it was mainly based on Darknet, and from YOLOv5 onward, PyTorch became the mainstream implementation. PyTorch and TensorFlow are the two leading deep learning frameworks today, so the industry mostly uses YOLOv5 and later versions.
Additionally, starting from YOLOv5, models are provided in different sizes without needing to manually adjust parameters as was required in YOLOv4.
Source: https://pytorch.org/hub/ultralytics_yolov5/
SUNIX Hailo-8 AI Accelerator – Optimal YOLO Inference Environment
The SUNIX Hailo-8 AI Accelerator provides a powerful inference runtime for YOLO, supporting most YOLO models such as YOLOv5, YOLOv7, and YOLOv8. In addition to the .hef files provided by the Hailo Model Zoo, users can also use the Hailo Dataflow Compiler to convert YOLO models from other runtimes into .hef files.
Using the SUNIX AIEH1000 (single Hailo8) to run YOLOv5m achieves FPS up to 241.88 Frames/sec, demonstrating that the SUNIX Hailo AI Accelerator provides inference performance comparable to mainstream GPUs.
