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Top 23 instance-segmentation Open-Source Projects
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Project mention: Intuituvely Understanding Harris Corner Detector | news.ycombinator.com | 2023-09-11
The most widely used algorithms for classical feature detection today are "whatever opencv implements"
In terms of tech that's advancing at the moment? https://co-tracker.github.io/ if you want to track individual points, https://github.com/matterport/Mask_RCNN and its descendents if you want to detect, say, the cover of a book.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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You can always slice the images into smaller ones, run detection on each tile, and combine results. Supervision has a utility for this - https://supervision.roboflow.com/latest/detection/tools/infe..., but it only works with detections. You can get a much more accurate result this way. Here is some side-by-side comparison: https://github.com/roboflow/supervision/releases/tag/0.14.0.
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labelme
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
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PaddleDetection
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
Project mention: [R]DETRs Beat YOLOs on Real-time Object Detection | /r/MachineLearning | 2023-04-19Our RTDETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RTDETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR->R50 by 2.2% AP in accuracy and by about 21 times in FPS. Source code and pretrained models will be available at PaddleDetection1 (https://github.com/PaddlePaddle/PaddleDetection) .
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Hi All, I am trying to detect defects in the images using YOLOv8where some of the classes (defectType1, defectType2) have very small bounding boxes and some of them have large bounding boxes associated with the, (defectType3, defectType4). Also, real-time operation is desired (at least 5Hz on Jetson Xavier) What I have done till now: I am primarily trying to use the SAHI technique (Slicing Aided Hyper Inference)
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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AdelaiDet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
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yolov7_d2
🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
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autodistill
Images to inference with no labeling (use foundation models to train supervised models).
Roboflow | Open Source Software Engineer, Web Designer / Developer, and more. | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0224
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].
We have several openings available but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping us figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)
We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.
[1]: https://roboflow.com/?ref=whoishiring0224
[2]: https://roboflow.com/universe?ref=whoishiring0224
[3]: https://github.com/autodistill/autodistill
[4]: https://github.com/roboflow/supervision
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SparK
[ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling" (by keyu-tian)
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involution
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
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yolact_edge
The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
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inference
A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models. (by roboflow)
Yeah, inference[1] is our open source package for running locally (either directly in Python or via a Docker container). It works with all the models on Universe, models you train yourself (assuming we support the architecture; we have a bunch of notebooks available[2]), or train in our platform, plus several more general foundation models[3] (for things like embeddings, zero-shot detection, question answering, OCR, etc).
We also have a hosted API[4] you can hit for most models we support (except some of the large vision models that are really GPU-heavy) if you prefer.
[1] https://github.com/roboflow/inference
[2] https://github.com/roboflow/notebooks
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MaskDINO
[CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"
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multimodal-maestro
Effective prompting for Large Multimodal Models like GPT-4 Vision, LLaVA or CogVLM. 🔥
Project mention: Show HN: Multimodal Maestro – Prompt tools for use with LMMs | news.ycombinator.com | 2023-11-29 -
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VNext
Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR 2023), SeqFormer(ECCV Oral), and IDOL(ECCV Oral))
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
instance-segmentation related posts
- Supervision: Reusable Computer Vision
- Supervision – reusable computer vision tools
- Intuituvely Understanding Harris Corner Detector
- Small-Object Detection using YOLOv8
- Which Azure service to host this ML model
- Library for chopping image in pieces for training
- [R]DETRs Beat YOLOs on Real-time Object Detection
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A note from our sponsor - WorkOS
workos.com | 17 Apr 2024
Index
What are some of the best open-source instance-segmentation projects? This list will help you:
Project | Stars | |
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1 | mmdetection | 27,658 |
2 | Mask_RCNN | 24,096 |
3 | supervision | 13,740 |
4 | labelme | 12,257 |
5 | PaddleDetection | 12,008 |
6 | yolact | 4,919 |
7 | sahi | 3,534 |
8 | AdelaiDet | 3,323 |
9 | yolov7_d2 | 3,130 |
10 | lanenet-lane-detection | 2,249 |
11 | SOLO | 1,664 |
12 | autodistill | 1,508 |
13 | UNINEXT | 1,435 |
14 | SparK | 1,384 |
15 | OneFormer | 1,329 |
16 | involution | 1,306 |
17 | yolact_edge | 1,245 |
18 | inference | 1,015 |
19 | PixelLib | 1,008 |
20 | MaskDINO | 1,004 |
21 | multimodal-maestro | 937 |
22 | Entity | 657 |
23 | VNext | 591 |