Technology Labs
AI-ORIN-NANO - Jetson Orin Nano Developer Kit
About Product
The AI-ORIN-NANO Jetson Orin Nano Developer Kit is an advanced AI development platform designed for machine learning, computer vision, and robotics applications. Powered by NVIDIA Jetson Orin Nano, it delivers up to 40 TOPS of AI performance with support for multiple cameras and sensors. The kit enables hands-on experimentation in object detection, face recognition, gesture recognition, image processing, and real-time AI applications.
AI Jetson Orin – Jetson Orin Nano
1) The NVIDIA® Jetson Nano™ Developer Kit is a small AI computer for makers, learners, and developers.
2) This kit includes a Jetson Orin Nano 8GB module and a versatile carrier board compatible with all Orin Nano, ideal for edge-AI prototyping. Equipped with an Ampere GPU, 6-core ARM CPU, and multiple connectors (including dual camera support), it enables seamless multitasking and high-performance inference.
1) The NVIDIA® Jetson Nano™ Developer Kit is a small AI computer for makers, learners, and developers.
2) This kit includes a Jetson Orin Nano 8GB module and a versatile carrier board compatible with all Orin Nano, ideal for edge-AI prototyping. Equipped with an Ampere GPU, 6-core ARM CPU, and multiple connectors (including dual camera support), it enables seamless multitasking and high-performance inference.
Jetson Nano Core Module
1)The NVIDIA Jetson Orin Nano Super Developer Kit.
2) Core Module: Jetson Orin Nano 8GB (SUPER)
3) AI Performance : 67 TOPS (sparse),33 TOPS (dense)
4) GPU: N-VIDIA Ampere architecture with 1024 CUDA cores and 32 tensor cores
5) GPU Frequency: 1020MHz (Max)
6) CPU: 6-core Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3
7) CPU Frequency: 1.7GHz (Max)
8) Memory: 8GB 128-bit LPDDR5, 102 GB/s
9) Storage: SD card,external NVMe via M.2 Key M interface
10) Power: 7 W | 15 W | 25 W
JETSON-IO-BASE-A Carrier Board
1) TF Card Slot : TF/micro
2) SD card expansion
3) M.2 Interface : M.2 Key E
4) Wireless Support : AC8265 wireless network card
5) Fan Header : 1 × 1.25 mm, 1 × 2.54 mm fan header
6) PoE : PoE pins; external PoE module required
7) GPIO : 40-pin GPIO header
8) USB : 4 × USB 3.0
9) Ethernet : Gigabit Ethernet, 10/100/1000 BASE-T
10) HDMI : HDMI display output
11) DisplayPort : DisplayPort connector
12) Camera : 2 × MIPI CSI camera connectors
13) Power Input : DC jack, 5 V power input
14) Power/Data : Micro USB port
15) Control Interface : Multi-function 12-pin header
16) Power Button : Onboard PWR button.
1)The NVIDIA Jetson Orin Nano Super Developer Kit.
2) Core Module: Jetson Orin Nano 8GB (SUPER)
3) AI Performance : 67 TOPS (sparse),33 TOPS (dense)
4) GPU: N-VIDIA Ampere architecture with 1024 CUDA cores and 32 tensor cores
5) GPU Frequency: 1020MHz (Max)
6) CPU: 6-core Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3
7) CPU Frequency: 1.7GHz (Max)
8) Memory: 8GB 128-bit LPDDR5, 102 GB/s
9) Storage: SD card,external NVMe via M.2 Key M interface
10) Power: 7 W | 15 W | 25 W
JETSON-IO-BASE-A Carrier Board
1) TF Card Slot : TF/micro
2) SD card expansion
3) M.2 Interface : M.2 Key E
4) Wireless Support : AC8265 wireless network card
5) Fan Header : 1 × 1.25 mm, 1 × 2.54 mm fan header
6) PoE : PoE pins; external PoE module required
7) GPIO : 40-pin GPIO header
8) USB : 4 × USB 3.0
9) Ethernet : Gigabit Ethernet, 10/100/1000 BASE-T
10) HDMI : HDMI display output
11) DisplayPort : DisplayPort connector
12) Camera : 2 × MIPI CSI camera connectors
13) Power Input : DC jack, 5 V power input
14) Power/Data : Micro USB port
15) Control Interface : Multi-function 12-pin header
16) Power Button : Onboard PWR button.
1) Setting up the Jetson Orin Nano developer kit with all the libraries and software dependencies.
2) Real-Time Object Detection on Live Camera using YOLOv8n model.
3) Real-Time Face Detection on Live Camera using the Mediapipe library.
4) Real-Time Eye Detection on Live Camera using the Mediapipe library.
5) Real-Time Hand Tracking on Live Camera using the Mediapipe library.
6) Real-Time Object Tracking based on Colour on Live Camera with the HSV scheme.
7) Real-Time Multiple Object Tracking based on Colour on Live Camera with the HSV scheme.
8) Real-Time Object Contours detection based on Colour on Live Camera with the HSV scheme.
9) Real-Time Face Recognition on Live Camera using the Face Recognition library.
10) Real-Time Pose Detection on Live Camera using the Mediapipe library.
11) Real-Time Hand Detection on Live Camera using the Mediapipe library.
12) Distinguishing between the Right and Left Hands on Live Camera using the Mediapipe library.
13) Real-Time Gesture Detection on Live Camera using the Mediapipe library.
14) Storing the Trained Gestures in a pickle file to be uploaded directly on Live Camera using the Mediapipe library.
15) Real-Time Object Detection based on User prompt on Live Camera.
16) Implementation of Text Generation with Llama model using LLMs.
17) Implementation of Text + Vision with Llava model using CLIP vision encoder.
2) Real-Time Object Detection on Live Camera using YOLOv8n model.
3) Real-Time Face Detection on Live Camera using the Mediapipe library.
4) Real-Time Eye Detection on Live Camera using the Mediapipe library.
5) Real-Time Hand Tracking on Live Camera using the Mediapipe library.
6) Real-Time Object Tracking based on Colour on Live Camera with the HSV scheme.
7) Real-Time Multiple Object Tracking based on Colour on Live Camera with the HSV scheme.
8) Real-Time Object Contours detection based on Colour on Live Camera with the HSV scheme.
9) Real-Time Face Recognition on Live Camera using the Face Recognition library.
10) Real-Time Pose Detection on Live Camera using the Mediapipe library.
11) Real-Time Hand Detection on Live Camera using the Mediapipe library.
12) Distinguishing between the Right and Left Hands on Live Camera using the Mediapipe library.
13) Real-Time Gesture Detection on Live Camera using the Mediapipe library.
14) Storing the Trained Gestures in a pickle file to be uploaded directly on Live Camera using the Mediapipe library.
15) Real-Time Object Detection based on User prompt on Live Camera.
16) Implementation of Text Generation with Llama model using LLMs.
17) Implementation of Text + Vision with Llava model using CLIP vision encoder.
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