Getting Started with the Recently Announced YOLOv9
YOLO v9: The Latest Evolution in Object Detection
YOLO v9, developed by Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao, is the latest iteration in the YOLO series, known for its real-time object detection capabilities. This version introduces significant innovations, including Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN), which address challenges related to information loss in deep neural networks. [Source, Source]
Programmable Gradient Information (PGI)
PGI is designed to tackle the information bottleneck problem in deep neural networks by ensuring the preservation of essential data across the network’s layers. This leads to more reliable gradient generation, which in turn improves model convergence and performance. [Source]
Generalized Efficient Layer Aggregation Network (GELAN)
GELAN is a strategic architectural advancement that enhances parameter utilization and computational efficiency. It allows for flexible integration of various computational blocks, making YOLO v9 adaptable to a wide range of applications without sacrificing speed or accuracy. [Source]
Performance and Efficiency
YOLO v9 demonstrates significant progress across its model variants:
- Lightweight Models: YOLO v9-S minimizes parameters and computational load while enhancing accuracy by 0.4 to 0.6% in AP compared to its predecessor, YOLO MS-S.
- Medium to Large Models: YOLO v9-M and YOLO v9-E balance model complexity and detection precision, significantly reducing parameters and computational demands while increasing accuracy.
- Overall Performance: The YOLO v9-C model operates with 42% fewer parameters and 21% less computational demand compared to YOLOv7 AF, achieving comparable accuracy. The YOLO v9-E model sets a new standard for large-scale models by utilizing 15% fewer parameters and 25% less computational effort than YOLOv8-X, coupled with a 1.7% improvement in AP.
YOLO v9’s innovative architecture and techniques, such as PGI and GELAN, maintain the legacy of efficiency and accuracy in the YOLO series, making it a game-changer in the field of object detection
Getting Started with YOLO v9
To begin working with YOLO v9, you’ll need to set up your environment and download the necessary model files. Here’s how you can get started:
Step 1: Environment Setup
We’ll use Google Colab, which provides a free GPU for running the model. Start by creating a new notebook in Colab.
Step 2: Install Dependencies
In your Colab notebook, install the required packages:
!pip install -q torch torchvision torchaudio
Step 3: Clone the YOLOv9 Repository
Clone the YOLOv9 GitHub repository and navigate to the cloned directory:
!git clone https://github.com/SkalskiP/yolov9.git
%cd yolov9
Step 4: Install Remaining Requirements
!pip install -r requirements.txt -q
Step 5: Download Model Weights
Download the weights for the YOLOv9-C and YOLOv9-E models:
!mkdir -p weights
!wget -P weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt
!wget -P weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt
Step 6: Run Inference
Now, you can run inference on an example image. Replace yolov9-c.pt with yolov9-e.pt for using the YOLOv9-E model, and data/dog.jpeg with the path to your image.
!python detect.py --weights weights/yolov9-c.pt --conf 0.1 --source data/dog.jpeg --device 0
For a practical demonstration of YOLOv9 object detection, check out our Google Colab notebook where we run inference on an image and a video. The methodology can be extended to live object detection, similar to what’s showcased in this Hugging Face Space.
Exploring YOLOv9 Object Detection: A Google Colab Notebook Demonstration: https://colab.research.google.com/drive/14ebY2hwakzoKuB4SxWe_-kr9tyqshud1?usp=sharing
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