notebooks
yolov5
notebooks | yolov5 | |
---|---|---|
19 | 129 | |
4,250 | 47,546 | |
5.2% | 2.8% | |
8.3 | 8.8 | |
5 days ago | 3 days ago | |
Jupyter Notebook | Python | |
- | GNU Affero General Public License v3.0 |
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notebooks
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Supervision: Reusable Computer Vision
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
[3] https://inference.roboflow.com/foundation/about/
[4] https://docs.roboflow.com/deploy/hosted-api
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Roboflow Notebooks: 30+ tutorials on using SOTA models and vision techniques
We (the Roboflow open source team) actively write open source Google Colab notebooks showing how to use new SOTA models. Our library covers SAM, CLIP, Detectron2, YOLOv8, RTMDet, DINOv2, and more. These notebooks helped me cross the chasm from "how do I use X model?" to being able to both write and understand inference code.
- Notebooks: How to tutorials for computer vision models and techniques
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Training Instance Segmentation models on custom dataset
Here's an open source SegFormer notebook and guide: https://github.com/roboflow/notebooks/blob/main/notebooks/train-segformer-segmentation-on-custom-data.ipynb
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[Advice request] How on earth am I supposed to break into machine learning research as an undergraduate?
Great ways to get some experience in general ML: * https://kaggle.com/learn to up your skill-set, practice a bit, and improve breadth of knowledge in topics like deep learning and computer vision * https://huggingface.co/learn free NLP courses that will really beef up your skillset * https://madewithml.com - robust tutorials for the end-to-end deep learning MLOps process * https://roboflow.com/learn - intro course material and some advanced topics in computer vision; tutorial walkthroughs for model training: https://github.com/roboflow/notebooks
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Generate Synthetic Computer Vision Data with Stable Diffusion Image-to-Image
Repo: https://github.com/roboflow/notebooks/blob/main/notebooks/sa...
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Rich Jupyter Notebook Diffs on GitHub... Finally.
Here are the notebooks I spend day and night refining: https://github.com/roboflow/notebooks
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Tools for object detection on satellite images
You’ll just need to have labeled solar panel images, and pick a model architecture and tutorial to train with: https://github.com/roboflow/notebooks
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[OC] Football Players Tracking with YOLOv5 + ByteTrack + OpenCV
dataset: https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc/dataset/4 code: https://github.com/roboflow/notebooks/blob/main/notebooks/how-to-track-football-players.ipynb video: https://youtu.be/QCG8QMhga9k
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Should I get a Google Coral USB Accelerator for my RPI4 or should I just buy a Nvidia Jetson Nano?
Have fun! Great field. Just also try out the first few OpenCV tutorials, and train a few custom model to deploy to see what you think. Here’s a ton of free open source notebooks: https://github.com/roboflow/notebooks
yolov5
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จำแนกสายพันธ์ุหมากับแมวง่ายๆด้วยYoLoV5
Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data
- 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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Building a Drowsiness Detection Web App from scratch - pt2
!git clone https://github.com/ultralytics/yolov5.git ## Navigate to the model %cd yolov5/ ## Install requirements !pip install -r requirements.txt ## Download the YOLOv5 model !wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt
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[Help: Project] Transfer Learning on YOLOv8
Specifically what I did was take the coco128.yaml, added 6 new classes from Dataset A (which have already been converted to YOLO Darknet TXT), from index 0-5 and subsequently adjusted the indices of the other COCO classes. The I proceeded to train and validate on Dataset A for 20 epochs.
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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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First time working with computer vision, need help figuring out a problem in my model
You should add them without annotations. Go through this.
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AI Camera?
You are correct and if you check the firmware, it's yet another famous 3rd party project without attribution, namely https://github.com/ultralytics/yolov5
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First non-default print on K1 - success
On one side, being a Linux user for 24 years now, it annoys me that they rip off code and claiming it as theirs again, thus violating licenses, but on the other thanks to k3d's exploit I'm able to tinker more with the machine and if needed do (selective) updates by hand then with a closed source system. It's not just "klipper", with klipper, fluidd and moonraker, it's also ffmpeg and mjpegstreamer. It's gonna be interesting since they also use a project that isn't just GPL, but APGL (in short "If your software gives service online, you have to publish the source code of it and any library that it borrows functions from.") - they use yolov5 (for AI).
- How does the background class work in object detection?
What are some alternatives?
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
mmdetection - OpenMMLab Detection Toolbox and Benchmark
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
glami-1m - The largest multilingual image-text classification dataset. It contains fashion products.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
make-sense - Free to use online tool for labelling photos. https://makesense.ai
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
uav-detection - Drone / Unmanned Aerial Vehicle (UAV) Detection is a very safety critical project. It takes in Infrared (IR) video streams and detects drones in it with high accuracy.
OpenCV - Open Source Computer Vision Library