Search Results for "inference"
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Showing 13 open source projects for "inference"
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Gearset | The complete Salesforce DevOps solutionGearset is the only platform you need for unparalleled deployment success, continuous delivery, automated testing and backups.
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RentGuruz is an all-in-one vehicle rental software solution designed to streamline operations for car rental businesses worldwide.RentGuruz. The simple, intuitive, and powerful cloud application platform that manages all kinds of mobility for all kinds of rental businesses.
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SAHI
A lightweight vision library for performing large object detection
...In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
...This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. At the same time, it is possible to build a video inference loop from scratch using just Norfair and a detector. Supports moving camera, re-identification with appearance embeddings, and n-dimensional object tracking. Norfair provides several predefined distance functions to compare tracked objects and detections. The distance functions can also be defined by the user, enabling the implementation of different tracking strategies. -
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
The core idea is to remove the error sources and difficulties of Deep Learning applications by providing a safe haven of commoditized practices, all available as a single core. While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. ... -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
...Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. ... -
Transforming NetOps Through No-Code Network Automation - NetBrainNetBrain, founded in 2004, provides a powerful no-code automation platform for hybrid network observability, allowing organizations to enhance their operational efficiency through automated workflows. The platform applies automation across three key workflows: troubleshooting, change management, and assessment.
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CutLER
Code release for Cut and Learn for Unsupervised Object Detection
...The method follows a “Cut-and-LEaRn” recipe: bootstrap object proposals, refine them iteratively, and train detection/segmentation heads to discover objects across diverse datasets. The codebase provides training and inference scripts, model configs, and references to benchmarking results that report large gains over prior unsupervised baselines. It’s intended for researchers exploring self-supervised and unsupervised recognition, offering a practical path to scale beyond costly labeled corpora. The README links papers and gives a high-level overview of components and expected outputs, with pointers to demos and assets. ... -
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Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
...It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view geometry, depth estimation, and camera registration within a single transformer-based architecture. It outputs high-quality 3D scene representations from unordered or sequential views, scaling to large datasets and varied camera intrinsics. ... -
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OpenFieldAI - AI Open Field Test Tracker
OpenFieldAI is an AI based Open Field Test Rodent Tracker
OpenFieldAI use AI-CNN to track rodents movement with pretrained OFAI models , or user could create their own model with YOLOv8 for inferencing. The software generates Centroid graph, Heat map and Line path and a spreadsheet containing all calculated parameters like - Speed - Time in and out of ROI - Distance - Entries/Exits for single/multiple pre-recorded videos or live webcam video. The ROI is assigned automatically in multiple video input , and can be manually given in single... -
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Blazeface
Blazeface is a lightweight model that detects faces in images
Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed... -
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NanoDet-Plus
Lightweight anchor-free object detection model
...NanoDet provide multi-backend C++ demo including ncnn, OpenVINO and MNN. There is also an Android demo based on ncnn library. Supports various backends including ncnn, MNN and OpenVINO. Also provide Android demo based on ncnn inference framework. -
Easy management of simple and complex projectsPlan more projects with Worksection. Use Gantt chart and Kanban boards to organize your projects, get your team onboard and assign tasks and due dates.
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10
MMTracking
OpenMMLab Video Perception Toolbox
...MMTracking interacts with other OpenMMLab projects. It is built upon MMDetection that we can capitalize any detector only through modifying the configs. All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations. We reproduce state-of-the-art models and some of them even outperform the official implementations. -
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Hello AI World
Guide to deploying deep-learning inference networks
Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train your own DNN models onboard Jetson with PyTorch. ... -
12
TNN
Uniform deep learning inference framework for mobile
...As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework. -
13
DETR
End-to-end object detection with transformers
...We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. ...
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