Yolo_mark
YoloV7-ncnn-Jetson-Nano
Our great sponsors
| Yolo_mark | YoloV7-ncnn-Jetson-Nano | |
|---|---|---|
| 6 | 1 | |
| 1,783 | 29 | |
| - | - | |
| 10.0 | 2.7 | |
| over 3 years ago | 7 months ago | |
| C++ | C++ | |
| The Unlicense | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Yolo_mark
-
Way to label yolov7 images fast
I've used Yolo_mark (https://github.com/AlexeyAB/Yolo_mark) with success when needing to label a few hundred thousand images. Its still a manual solution, but there are keyboard shortcuts for navigating between images and classes, and with some practice you can get through a ton of images quite quickly.
-
Implementation of YOLO in python or C++?
What are you talking about? Darknet -- where YOLO started! -- is written in C and C++. Check the active repo: https://github.com/AlexeyAB/darknet
-
I was excited about YOLOv7, so I built a sharable object detection application with VDP and Streamlit.
When YOLOv7 was out, I built a web app to test it against the classic YOLOv4 and shared it with my team, then deployed it online to share with the community.
-
HOW to find MOTA and MOTP for MOT evaluation metrics?
Because I need to calculate MOTA and MOTP for the tracking metrics. please anyone with this concept help me as I am beginner to the computer vision. Incase if it helps I am working based on the github https://github.com/AlexeyAB/darknet
-
How to improve a YoloV5 model after the first training?
My work heavily involves the use of the yolo algorithm such as optimising it for performance on mobile devices. Yolov5 is made by a private company that has been pushing sub bar models for a while. I've benchmarked their smallest models comparing them to Yolov4 tiny and the results were staggering, v4 being around 3-4 times faster. Yolov4 has way more resources for development, I highly suggest checking out this repo https://github.com/AlexeyAB/darknet
-
[D] What are people using to organize large groups of people for data labelling?
YOLO Mark
YoloV7-ncnn-Jetson-Nano
-
Implementation of YOLO in python or C++?
For cpu inference, we use ncnn, and this is what I’m working off of: https://github.com/Qengineering/YoloV7-ncnn-Jetson-Nano
What are some alternatives?
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
TensorRT-For-YOLO-Series - tensorrt for yolo series (YOLOv8, YOLOv7, YOLOv6, YOLOv5), nms plugin support
py-motmetrics - :bar_chart: Benchmark multiple object trackers (MOT) in Python
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
yolo-object-detector - An implementation of the YOLO method of object detection in dlib
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
deprecated-core - 🔮 Instill Core contains components for supporting Instill VDP and Instill Model
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
sort - Simple, online, and realtime tracking of multiple objects in a video sequence.