Introducing YOLO-NAS: The New Standard for Object Detection Models

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Attention all computer vision enthusiasts!!👀🤖 ⭐Object detection has just got easier! 🏎️ A new YOLO-based architecture, YOLO-NAS, has just been open-sourced! And it's setting a new standard for SOTA object detection models 🧵👇

What's the NAS all about?🤔 Researchers at Deci used Neural Architecture Search (NAS) to automate the discovery of an optimal object detection architecture. YOLO-NAS, generated by Deci's AutoNAC engine, outperforms competitors like YOLOv5, YOLOv6, YOLOv7, and YOLOv8.

🤖Key points about the YOLO-NAS model: 1) It's pre-trained on well-known datasets (COCO, Objects365, Roboflow 100), making it ideal for downstream object detection tasks in production environments.

2) Enhanced detection of small objects, improved localization accuracy, and higher performance-per-compute ratio. 3) Ideal for real-time edge-device applications due to its ease of inference

3) Training with SuperGradients, Deci's open-source, PyTorch-based computer vision library, enables advanced techniques like Distributed Data-Parallel, Exponential Moving Average, Automatic mixed precision, and Quantization Aware Training.

4) It is also easy to customize the model on the custom dataset using to train and fine-tune the YOLO-NAS model making it applicable to a wide range of use cases. A strong pre-trained model with SOTA performance leads to higher accuracy for custom datasets which is a big plus.

Excited to learn more about this? Try out this starter notebook to get introduced to the SuperGradients library + YOLONAS model Starter NB Link: bit.ly/3ARPD9b

You can also build a project using the notebook given below👇 Fine-tine the model on any custom dataset of your choice bit.ly/3njRznR

Also to get more details and examples of this SOTA model, make sure to check out their github repository and ⭐the repo for future reference. Click the link below to learn more👇 bit.ly/41bI7B7