AI Services
I will develop an AI computer vision system, YOLO object detection and PyTorch model
Trueet Network
Verified Agency
0.00
(0 reviews)
•
0 orders completed
•
0 in queue
About This Gig
Deploy High-Speed Computer Vision & Visual Intelligence Pipelines
From quality inspection in manufacturing to security video analysis and medical imaging, visual AI transforms operational throughput. At Trueet Network, our machine learning engineers develop custom Computer Vision systems using YOLOv8, PyTorch, OpenCV, and TensorRT.
We train custom models on your image datasets, optimize for real-time inference (30+ FPS), and deploy scalable inference APIs or edge packages for NVIDIA Jetson and Raspberry Pi.
What We Engineer For Your Project:
- Custom Object Detection & Tracking: Custom-trained YOLOv8 / YOLOv10 models with DeepSORT multi-object tracking.
- Image Segmentation & Classification: Pixel-level semantic segmentation (Mask R-CNN, SAM) and defect detection.
- Video Stream Processing: Real-time RTSP/CCTV security camera ingestion with OpenCV and FFmpeg pipelines.
- Edge Hardware Optimization: TensorRT and ONNX quantization running at maximum FPS on resource-constrained devices.
- REST API & Cloud Integration: FastAPI endpoints returning bounding boxes, labels, and confidence metrics.
Why Choose Trueet Network?
- Rigorous mAP (Mean Average Precision) benchmarking and validation.
- Complete data privacy: datasets and trained weights remain 100% confidential.
- Well-documented Python codebase ready for immediate deployment.
Frequently Asked Questions
Q: How many images do I need to train a custom detector?
For simple, distinct objects, 200 to 500 annotated images per class yield high accuracy. For complex conditions (varied lighting, angles, occlusions), 1,000+ images per class are recommended.
Q: Can you help with dataset labeling and annotation?
Yes! We assist with automated labeling pipelines using Grounding DINO and Roboflow to rapidly annotate thousands of images.
Q: Can this run in real time on a live video stream?
Yes. We optimize models using TensorRT and ONNX runtime to achieve 30 to 60+ FPS on modern GPUs and edge hardware.
Q: What formats do you deliver?
We deliver trained PyTorch (.pt), ONNX (.onnx), and TensorRT (.engine) weights accompanied by clear inference scripts and Dockerfiles.
Verified Buyer Reviews (0)
No buyer reviews yet for this Gig.
