CenterSnap VS SynthDet

Compare CenterSnap vs SynthDet and see what are their differences.

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CenterSnap SynthDet
4 3
270 351
- 0.9%
4.0 2.9
2 months ago 10 months ago
Jupyter Notebook C#
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

CenterSnap

Posts with mentions or reviews of CenterSnap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-07.

SynthDet

Posts with mentions or reviews of SynthDet. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing CenterSnap and SynthDet you can also consider the following projects:

text2mesh - 3D mesh stylization driven by a text input in PyTorch

AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.

text2voxels - Generate 3D voxels from text with AI

Deep-Learning-Push-Up-Counter - Deep Learning approach to count the number of repetitions in a video of push ups or pull ups.

virtual_drawing_board - Virtual whiteboard with hand pose estimation

yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0

Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.

Clip-Forge

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

GPV_Pose - pytorch implementation of GPV-Pose

make-sense - Free to use online tool for labelling photos. https://makesense.ai