| Brand Name: | Shi Zun |
| Model Number: | JP-1126 |
| MOQ: | Negotiable |
| Price: | Negotiable |
| Delivery Time: | 10 working days |
| Payment Terms: | T/T |
JP1126 Intelligent Dual-Lens Camera Module Up to 2.0 Tops performance, supports INT8/INT16 5V/1A
JP1126 Intelligent Dual-Lens Camera Module Features:
JP1126 Intelligent Dual-Lens Camera Module Parameter:
|
Processor:
|
Quad-core ARM Cortex-A7 32-bit, 1.5GHz, with integrated NEON and FPU
Each core has a 32KB I-cache and 32KB D-cache, plus 512KB shared L2 cache
Based on RISC-V MCU
|
|
NPU:
|
Up to 2.0 Tops performance, supports INT8/INT16, strong network model compatibility,
RKNN model conversion tool available for converting common AI framework models (e.g.,
Caffe, Darknet, MXNet, ONNX, PyTorch, TensorFlow, TFLite) and algorithm support
|
|
Memory:
|
1GB/2GBDDR4
|
|
Storage:
|
8GB/16GB eMMC
|
|
Video encoding:
|
4KH.264/H.26530fps
3840x2160@30fps+720p@30fpsencoding
|
|
Video Decoding:
|
4KH.264/H.26530fps
3840x2160@30encoding+3840x2160@30fpsdecoding
|
|
System support:
|
Linux
|
|
Power:
|
5V/1A
|
|
Image Sensors:
|
GC2053
GC2093
|
| Module Board Dimensions: | 80* 16* 17.6mm (L* W* H) |
|
Resolution:
|
1920*1080
|
|
Pixel Size:
|
2.8 μm
|
|
Interface:
|
MIPI
|
|
Focal Length:
|
F2.0/4.3mm
|
| Maximum Database: |
100,000
|
|
Face Recognition
Accuracy:
|
Standard Testing Environment, 10,000-person Database:
Without Mask:
False Acceptance Rate: 0.01%; Recognition Accuracy: 99%
With Mask:
False Acceptance Rate: 0.01%; Recognition Accuracy: 95%
|
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
| Brand Name: | Shi Zun |
| Model Number: | JP-1126 |
| MOQ: | Negotiable |
| Price: | Negotiable |
| Packaging Details: | 84.0mm × 22.45mm × 19.35mm |
| Payment Terms: | T/T |
JP1126 Intelligent Dual-Lens Camera Module Up to 2.0 Tops performance, supports INT8/INT16 5V/1A
JP1126 Intelligent Dual-Lens Camera Module Features:
JP1126 Intelligent Dual-Lens Camera Module Parameter:
|
Processor:
|
Quad-core ARM Cortex-A7 32-bit, 1.5GHz, with integrated NEON and FPU
Each core has a 32KB I-cache and 32KB D-cache, plus 512KB shared L2 cache
Based on RISC-V MCU
|
|
NPU:
|
Up to 2.0 Tops performance, supports INT8/INT16, strong network model compatibility,
RKNN model conversion tool available for converting common AI framework models (e.g.,
Caffe, Darknet, MXNet, ONNX, PyTorch, TensorFlow, TFLite) and algorithm support
|
|
Memory:
|
1GB/2GBDDR4
|
|
Storage:
|
8GB/16GB eMMC
|
|
Video encoding:
|
4KH.264/H.26530fps
3840x2160@30fps+720p@30fpsencoding
|
|
Video Decoding:
|
4KH.264/H.26530fps
3840x2160@30encoding+3840x2160@30fpsdecoding
|
|
System support:
|
Linux
|
|
Power:
|
5V/1A
|
|
Image Sensors:
|
GC2053
GC2093
|
| Module Board Dimensions: | 80* 16* 17.6mm (L* W* H) |
|
Resolution:
|
1920*1080
|
|
Pixel Size:
|
2.8 μm
|
|
Interface:
|
MIPI
|
|
Focal Length:
|
F2.0/4.3mm
|
| Maximum Database: |
100,000
|
|
Face Recognition
Accuracy:
|
Standard Testing Environment, 10,000-person Database:
Without Mask:
False Acceptance Rate: 0.01%; Recognition Accuracy: 99%
With Mask:
False Acceptance Rate: 0.01%; Recognition Accuracy: 95%
|
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()
![]()