License Plate Recognition
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Darknet_ros | 1,610 | 2 years ago | 131 | bsd-3-clause | C++ | |||||
| YOLO ROS: Real-Time Object Detection for ROS | ||||||||||
| Alpr Unconstrained | 1,462 | 2 years ago | 106 | other | C | |||||
| License Plate Detection and Recognition in Unconstrained Scenarios | ||||||||||
| Yolo 9000 | 1,057 | 3 years ago | 10 | apache-2.0 | ||||||
| YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes! | ||||||||||
| Darknet Ocr | 856 | 3 years ago | 83 | mit | C | |||||
| darknet text detect and darknet cnn ocr | ||||||||||
| Msnhnet | 666 | a year ago | 1 | mit | C++ | |||||
| 🔥 (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet. | ||||||||||
| Bmw Yolov4 Training Automation | 630 | 9 months ago | 9 | bsd-3-clause | Python | |||||
| This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy. | ||||||||||
| Yolo3 4 Py | 521 | 2 years ago | 7 | March 18, 2021 | 4 | apache-2.0 | Python | |||
| A Python wrapper on Darknet. Compatible with YOLO V3. | ||||||||||
| Darknet_captcha | 340 | 2 years ago | 10 | apache-2.0 | Python | |||||
| 基于darknet实现目标检测，提供识别点选验证码的实例和训练自己数据的API | ||||||||||
| Bmw Yolov4 Inference Api Gpu | 276 | 2 years ago | bsd-3-clause | Python | ||||||
| This is a repository for an nocode object detection inference API using the Yolov3 and Yolov4 Darknet framework. | ||||||||||
| Pine | 219 | 3 years ago | 20 | mit | Python | |||||
| :evergreen_tree: Aimbot powered by real-time object detection with neural networks, GPU accelerated with Nvidia. Optimized for use with CS:GO. | ||||||||||
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