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Yolov5 Alternatives
Similar projects and alternatives to yolov5
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darknet
YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet ) (by AlexeyAB)
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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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detectron2
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
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yolor
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
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Deep-SORT-YOLOv4
People detection and optional tracking with Tensorflow backend.
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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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yolov5-crowdhuman
Head and Person detection using yolov5. Detection from crowd.
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ROCm
Discontinued AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
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CenterNet
Object detection, 3D detection, and pose estimation using center point detection:
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developer-roadmap
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
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transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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yolov7
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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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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YOLOv6
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
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sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
yolov5 reviews and mentions
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Changing labels of default YOLOv5 model
I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?
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SS: Events triggered no matter the detection zone specified
What I did is hand off my detections to an Nvidia GPU running Yolov5 https://github.com/ultralytics/yolov5 which does the triggering if it's an object class I'm interested in. Since Synology is always caching at least 5 seconds of camera video the 50-100mS delay of grabbing a frame and analyzing before triggering recording is fine. Wish they'd implement something like this.
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AMD ROCm 5.5 In The Process Of Being Released
this is about all I could find, maybe you've been getting different degrees of optimized python libs that do the image resizes. https://github.com/ultralytics/yolov5/issues/11469
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good computer vision or deep learning projects in github
YOLOv5 (GitHub: https://github.com/ultralytics/yolov5) is a fast, accurate object detection model with code for training, testing, deployment, and pre-trained weights.
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[D] Extracting the class labels and bounding boxes for objects, from a YOLO7 model after converting to an ONNX model
Those dimensions suggest you need to apply (i.e. roll your own) non-max suppresion to the outputs: relevant link
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Thought Dump About Recent AI Advancements And Palantir
- YOLOv5 https://github.com/ultralytics/yolov5 (open source, so not Palantir's)
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How to build computer vision dataset labeling team in-house
A team of annotators and the infrastructure described in this article I needed to label my dataset, which was collected from cameras on the road (30k+ photos). This dataset was necessary to train an object detection model on six classes: [person, car, bus, bicycle, motorcycle, truck]. I released the dataset, created in this manner, as open source, and it can be downloaded here (link) together with trained YOLOv5s and YOLOv5x models from a popular repository (link) using this dataset. The license is simple: "Use it well"!
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Flutter Object Detection App + YOLOV5 Model.
Before you can use YOLOv5 in your Flutter application, you'll need to train the model on your specific dataset. You can use an existing dataset or create your own dataset to train the model. For this post I am using the pretrained model of yolov5 available on https://github.com/ultralytics/yolov5 as we are performing object detection we need to converts the pretrained model weights to torchscript format.
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NotImplementedError (YOLOv5)
Thank you Link to the codefor taking the time to reply. I have modified the code as you suggested. And now I see the GPU being utilized. But the precision, recall, mAP is all zero. At least it displays as zero.
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NVIDIA Jetson AGX Orin is now compatible with balena
Read more here about how Theia Scientific is currently using the newest NVIDIA Jetson AGX Orin on their fleet of microscopes. Theia Scientific and Volkov Labs, improved the Jetson AGX Orin inference speeds up to 30FPS. The Jetson AGX Orin running YOLOv5 tripled the Frames-per-Second (FPS) compared with the latest Jetson AGX Xavier.
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A note from our sponsor - WorkOS
workos.com | 28 Mar 2024
Stats
ultralytics/yolov5 is an open source project licensed under GNU Affero General Public License v3.0 which is an OSI approved license.
The primary programming language of yolov5 is Python.