YOLO-NAS

Train and Inference your custom YOLO-NAS model by Single Command Line

About YOLO-NAS

A Next-Generation, Object Detection Foundational Model generated by Deci’s Neural Architecture Search Technology
Deci is thrilled to announce the release of a new object detection model, YOLO-NAS - a game-changer in the world of object detection, providing superior real-time object detection capabilities and production-ready performance. Deci's mission is to provide AI teams with tools to remove development barriers and attain efficient inference performance more quickly.

In terms of pure numbers, YOLO-NAS is ~0.5 mAP point more accurate and 10-20% faster than equivalent variants of YOLOv8 and YOLOv7.

Model	mAP	Latency (ms)
YOLO-NAS S	47.5	3.21
YOLO-NAS M	51.55	5.85
YOLO-NAS L	52.22	7.87
YOLO-NAS S INT-8	47.03	2.36
YOLO-NAS M INT-8	51.0	3.78
YOLO-NAS L INT-8	52.1	4.78

mAP numbers in table reported for COCO 2017 Val dataset and latency benchmarked for 640x640 images on Nvidia T4 GPU.

YOLO-NAS's architecture employs quantization-aware blocks and selective quantization for optimized performance. When converted to its INT8 quantized version, YOLO-NAS experiences a smaller precision drop (0.51, 0.65, and 0.45 points of mAP for S, M, and L variants) compared to other models that lose 1-2 mAP points during quantization. These techniques culminate in innovative architecture with superior object detection capabilities and top-notch performance.

Build your first custom YOLO-NAS model

• Clone the Repository
• Install dependencies
• Prepare dataset
• Train your custom YOLO-NAS model
• Inference your custom YOLO-NAS model on Camera/Video/RTSP

😎 Custom YOLO-NAS Model

Clone this Repository

git clone https://github.com/naseemap47/YOLO-NAS.git
cd YOLO-NAS

Install dependencies

Recommended:

conda create -n yolo-nas python=3.9 -y
conda activate yolo-nas
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch -y
pip install super-gradients==3.1.3

OR

pip3 install -r requirements.txt

🎒 Prepare Dataset

Your custom dataset should be in COCO data format.
COCO Data Format:

├── Dataset
│   ├── train
│   │   ├── images
│   │   │   ├── 1.jpg
│   │   │   ├── abc.png
|   |   |   ├── ....
│   │   ├── labels
│   │   │   ├── 1.txt
│   │   │   ├── abc.txt
|   |   |   ├── ....
│   ├── val
│   │   ├── images
│   │   │   ├── 2.jpg
│   │   │   ├── fram.png
|   |   |   ├── ....
│   │   ├── labels
│   │   │   ├── 2.txt
│   │   │   ├── fram.txt
|   |   |   ├── ....
│   ├── test
│   │   ├── images
│   │   │   ├── img23.jpeg
│   │   │   ├── 50.jpg
|   |   |   ├── ....
│   │   ├── labels
│   │   │   ├── img23.txt
│   │   │   ├── 50.txt
|   |   |   ├── ....

To training custom model using your custom data. You need to create data.yaml Example:

names:
- class1
- class2
- class3
Dir: '/home/user/my_data'  # path to your custom data
images:
  test: test/images
  train: train/images
  val: valid/images
labels:
  test: test/labels
  train: train/labels
  val: valid/labels

🤖 Train

You can train your YOLO-NAS model with Single Command Line

Args

-i, --data: path to data.yaml
-n, --name: Checkpoint dir name
-b, --batch: Training batch size
-e, --epoch: number of training epochs.
-s, --size: Input image size
-j, --worker: Training number of workers
-m, --model: Model type (Choices: yolo_nas_s, yolo_nas_m, yolo_nas_l)
-w, --weight: path to pre-trained model weight (default: coco weight)
--gpus: Train on multiple gpus
--cpu: Train on CPU

Other Training Parameters:
--warmup_mode: Warmup Mode, eg: Linear Epoch Step
--warmup_initial_lr: Warmup Initial LR
--lr_warmup_epochs: LR Warmup Epochs
--initial_lr: Inital LR
--lr_mode: LR Mode, eg: cosine
--cosine_final_lr_ratio: Cosine Final LR Ratio
--optimizer: Optimizer, eg: Adam
--weight_decay: Weight Decay

Example:

python3 train.py --data /dir/dataset/data.yaml --batch 6 --epoch 100 --model yolo_nas_m --size 640

📺 Inference

You can Inference your YOLO-NAS model with Single Command Line

Support

• Image
• Video
• Camera
• RTSP

Args

-n, --num: Number of classes the model trained on
-m, --model: Model type (choices: yolo_nas_s, yolo_nas_m, yolo_nas_l)
-w, --weight: path to trained model weight
-s, --source: video path/cam-id/RTSP
-c, --conf: model prediction confidence (0<conf<1)
--save: to save video
--hide: hide video window

Example:

python3 inference.py --num 3 --model yolo_nas_m --weight /runs/train4/ckpt_best.pth --source /test/video.mp4 --conf 0.66           # video
                                                                                    --source /test/sample.jpg --conf 0.5 --save    # Image save
                                                                                    --source /test/video.mp4 --conf 0.75 --hide    # to save and hide video window
                                                                                    --source 0 --conf 0.45                         # Camera
                                                                                    --source 'rtsp://link' --conf 0.25 --save      # save RTSP video stream

📺 Inference Batching

Support

• Video
• Camera
• RTSP

Args

-n, --num: Number of classes the model trained on
-m, --model: Model type (choices: yolo_nas_s, yolo_nas_m, yolo_nas_l)
-w, --weight: path to trained model weight
-s, --source: paths to videos/cam-ids/RTSPs
--full: to enable full screen

python3 batch.py --num 3 --model yolo_nas_m --weight /runs/train4/ckpt_best.pth --source '/test/video.mp4' '/test/video23.mp4'      # videos
                                                                                --source 0 2 --conf 0.45 --full                     # web-Cameras with full screen
                                                                                --source 'rtsp://link' 'rtsp://link3' --conf 0.25   # RTSPs video stream