Zoom_network
The codebase for Zoom Out-and-In Network with Recursive Training for Object Proposal
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Ssd Tensorflow | 4,049 | 3 years ago | 298 | Jupyter Notebook | ||||||
| Single Shot MultiBox Detector in TensorFlow | ||||||||||
| Tensorflow2.0 Examples | 1,692 | a year ago | 99 | mit | Jupyter Notebook | |||||
| 🙄 Difficult algorithm, Simple code. | ||||||||||
| Yolov3 Model Pruning | 1,149 | 5 years ago | 63 | mit | Python | |||||
| 在 oxford hand 数据集上对 YOLOv3 做模型剪枝（network slimming） | ||||||||||
| Cv Pretrained Model | 991 | 3 years ago | mit | |||||||
| A collection of computer vision pre-trained models. | ||||||||||
| Android Yolo | 608 | 3 years ago | 13 | apache-2.0 | C++ | |||||
| Real-time object detection on Android using the YOLO network with TensorFlow | ||||||||||
| Rexnet | 416 | 2 years ago | 2 | mit | Python | |||||
| Official Pytorch implementation of ReXNet (Rank eXpansion Network) with pretrained models | ||||||||||
| Edgenets | 340 | 3 years ago | 2 | mit | Python | |||||
| This repository contains the source code of our work on designing efficient CNNs for computer vision | ||||||||||
| Jeelizar | 338 | 2 years ago | apache-2.0 | JavaScript | ||||||
| JavaScript object detection lightweight library for augmented reality (WebXR demos included). It uses convolutional neural networks running on the GPU with WebGL. | ||||||||||
| 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. | ||||||||||
| Spoonn | 156 | 4 years ago | 8 | agpl-3.0 | Jupyter Notebook | |||||
| FPGA-based neural network inference project with an end-to-end approach (from training to implementation to deployment) | ||||||||||
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Deep Learning
Network
Matlab
Object Detection
Proposal