NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
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Updated
Jun 11, 2024 - Python
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
Images to inference with no labeling (use foundation models to train supervised models).
Simple process for camera installation, software and hardware setup, and object detection using Yolov5 and openCV on NVIDIA Jetson Nano.
Simplify camera trap image analysis with ML species recognition models based around the MegaDetector model
Real-Time Person Detection with Landmark Detection and Depth Estimation
Zone Evaluation: Revealing Spatial Bias in Object Detection (TPAMI 2024)
A Real time Vehicle Detection and Tracking using YOLOv5 and SORT - Simple, online, and real time tracking of multiple objects in a video sequence.
Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
This repository contains code for detecting sign language gestures using YOLOv5. It includes data collection, model training, and detection scripts.
《Pytorch实用教程》(第二版)无论是零基础入门,还是CV、NLP、LLM项目应用,或是进阶工程化部署落地,在这里都有。相信在本书的帮助下,读者将能够轻松掌握 PyTorch 的使用,成为一名优秀的深度学习工程师。
MkDocs plugin for Ultralytics Docs at https://docs.ultralytics.com
Profile PyTorch models for FLOPs and parameters, helping to evaluate computational efficiency and memory usage.
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
A python based helmet detection system
Detection models and Python scripts for automated insect monitoring with the Insect Detect DIY camera trap.
This project uses YOLOv5 and YOLOv8 models to analyze traffic patterns in Bangladesh, trained on the RSUD20k dataset. It explores various image augmentation techniques like random cropping, scaling, and photometric distortions, assessing their impact on model performance and real-world applications in autonomous vehicle deployment.
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