ncnn VS deepdetect

Compare ncnn vs deepdetect and see what are their differences.

Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
ncnn deepdetect
12 4
18,997 2,492
1.6% 0.0%
9.4 7.0
9 days ago about 1 month ago
C++ C++
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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.

ncnn

Posts with mentions or reviews of ncnn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-12.

deepdetect

Posts with mentions or reviews of deepdetect. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-13.
  • Exploring Open-Source Alternatives to Landing AI for Robust MLOps
    18 projects | dev.to | 13 Dec 2023
    For those seeking a lightweight solution for setting up deep learning REST APIs across platforms without the complexity of Kubernetes, Deepdetect is worth considering.
  • [D] Deep Learning Framework for C++.
    7 projects | /r/MachineLearning | 12 Jun 2022
    But you need to have good reasons to do it. Ours is that we have a multi-backend framework, and that we don't want any step in between dev & run. C++ allows for this since the same code can run on training server and edge device as needed. It also allows for building full AI applicatioms with great performances (e g. real time) We dev & use https://github.com/jolibrain/deepdetect for these purposes and it serves us very well, but it's not the faint of heart !
  • [P] Real-time AR for jewelry virtual try on that looks real, done with joliGAN, based on a few 2D videos and no 3D model
    2 projects | /r/MachineLearning | 8 Jun 2022
    - Real-time is achieved through our full C++ Open Source backend DeepDetect, https://github.com/jolibrain/deepdetect. We use CUDA along with OpenCV and TensorRT to chain multiple models (ring detection and generator mostly), and we make sure the data remain within CUDA memory at all time. This allows us to reach ~60 FPS on 1080Ti and 20% more on average on an RTX3090.

What are some alternatives?

When comparing ncnn and deepdetect you can also consider the following projects:

XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web

rife-ncnn-vulkan - RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation implemented with ncnn library

netron - Visualizer for neural network, deep learning and machine learning models

darknet - Convolutional Neural Networks

torch-mlir - The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.

RPi_64-bit_Zero-2-image - Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework.

rocm-build - build scripts for ROCm

MocapNET - We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance

PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

onnx-simplifier - Simplify your onnx model