Custom YOLO object detector for edge devices
Budget: ₹37,500 – ₹75,000 INR
Title: Custom YOLO object detector for edge devices
Target Model: ONNX format for maximum portability.
Input for the Model: Frame
Output: The model should detect Person, Smart Phone, Cigarette, Bicycle, Car,
Motorcycle, Bus, Truck, Indian auto rickshaw, Traffic lights, Traffic Sign Boards, Cat, Dog,
Sheep, Cow, Bottle.
Project Output: Training Code, Inference Code in Python, list of datasets used
Hardware Optimization:
● Primarily target NVIDIA Jetson Nano for edge deployment.
● Minimise CPU and RAM usage for efficient resource utilisation.
● Consider optimisations for both CPU and GPU (NVIDIA CUDA) execution.
Use Case: The above model will detect objects in the frame.
Target Model: ONNX format for maximum portability.
Input for the Model: Frame
Output: The model should detect Person, Smart Phone, Cigarette, Bicycle, Car,
Motorcycle, Bus, Truck, Indian auto rickshaw, Traffic lights, Traffic Sign Boards, Cat, Dog,
Sheep, Cow, Bottle.
Project Output: Training Code, Inference Code in Python, list of datasets used
Hardware Optimization:
● Primarily target NVIDIA Jetson Nano for edge deployment.
● Minimise CPU and RAM usage for efficient resource utilisation.
● Consider optimisations for both CPU and GPU (NVIDIA CUDA) execution.
Use Case: The above model will detect objects in the frame.