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Top 23 Python object-detection Projects
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albumentations
Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
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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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Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
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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.
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pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
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ImageAI
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
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darkflow
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
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GroundingDINO
Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
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AdelaiDet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
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Docs
Project mention: Intuituvely Understanding Harris Corner Detector | news.ycombinator.com | 2023-09-11The 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.
# Clone ultralytics repo git clone https://github.com/ultralytics/ultralytics # cd to local directory cd ultralytics # Install dependencies pip install -r requirements.txt
Frigate uses models like this one for NVR:
https://frigate.video/
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.
Project mention: Recapping the AI, Machine Learning and Data Science Meetup — May 8, 2024 | dev.to | 2024-05-09In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.
Project mention: Samsung expected to report 80% profit plunge as losses mount at chip business | news.ycombinator.com | 2023-10-10> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).
There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.
I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.
And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.
[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...
[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...
[2] https://github.com/pytorch/pytorch/tree/main/aten/src
For the two examples we will be looking at, we will be using pytorch_grad_cam, an incredible open source package that makes working with GradCam very easy. There are excellent other tutorials to check out on the repo as well.
Hi. I want to implement an image server similar to Photoprism using ImageAI to tag images based on objects and context. However I don't want to spend to much time working on the frontend, at first I were thinking about using Danbooru and use Flexbooru or the web interface on my phone. But it doesn't have any encryption or password protection (since the purpose of it is to be used as a public image board).
Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO
Project mention: What's the best model to get monocular 3d angle info | /r/deeplearning | 2023-06-28There are bunch of methods in this codebase, check it out. https://github.com/open-mmlab/mmdetection3d
An alternative to this is to leverage existing object detection, apply the model to patches or slices of fixed size in our image, and then stitch the results together. This is the idea behind Slicing-Aided Hyper Inference!
Project mention: Instance segmentation of small objects in grainy drone imagery | /r/computervision | 2023-12-09
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python object-detection related posts
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YOLOv5 on FPGA with Hailo-8 and 4 Pi Cameras
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Mastering YOLOv10: A Complete Guide with Hands-On Projects
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How to Detect Small Objects
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จำแนกสายพันธ์ุหมากับแมวง่ายๆด้วยYoLoV5
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Supervision: Reusable Computer Vision
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Unable to re add my server to HAOS integration
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How would i go about having YOLO v5 return me a list from left to right of all detected objects in an image?
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A note from our sponsor - Scout Monitoring
www.scoutapm.com | 1 Jun 2024
Index
What are some of the best open-source object-detection projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | yolov5 | 47,719 |
| 2 | mmdetection | 28,107 |
| 3 | Mask_RCNN | 24,248 |
| 4 | ultralytics | 24,238 |
| 5 | frigate | 15,167 |
| 6 | supervision | 14,817 |
| 7 | albumentations | 13,542 |
| 8 | Swin-Transformer | 13,101 |
| 9 | PaddleDetection | 12,199 |
| 10 | yolov3 | 10,038 |
| 11 | pytorch-grad-cam | 9,611 |
| 12 | YOLOX | 9,076 |
| 13 | ImageAI | 8,443 |
| 14 | TensorLayer | 7,296 |
| 15 | autogluon | 7,235 |
| 16 | darkflow | 6,132 |
| 17 | gluon-cv | 5,751 |
| 18 | GroundingDINO | 5,235 |
| 19 | mmdetection3d | 4,909 |
| 20 | layout-parser | 4,558 |
| 21 | sahi | 3,656 |
| 22 | AdelaiDet | 3,332 |
| 23 | catalyst | 3,236 |
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