notebooks
yolov5js
notebooks | yolov5js | |
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19 | 3 | |
4,164 | 44 | |
3.2% | - | |
8.3 | 10.0 | |
17 days ago | over 1 year ago | |
Jupyter Notebook | TypeScript | |
- | MIT License |
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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
Youβll 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. Hereβs a ton of free open source notebooks: https://github.com/roboflow/notebooks
yolov5js
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Football Players Tracking with YOLOv5 + ByteTRACK Tutorial
I love this! TF.js is big! And to answer your question - sure, we can run that model in the browser. This is the YOLOv5 model it can be converted from PyTorch to TF.js with this script: https://github.com/ultralytics/yolov5/blob/master/export.py And then run it with my NPM package https://github.com/SkalskiP/yolov5js. ML in Java Script is the future! The problem is I don't know anything about any good tracker implemented in JS.
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Use YOLOv5 tensorflow.js models to speed up annotation
makesense.ia is certainly the largest one. I just recently started https://github.com/SkalskiP/yolov5js with the aim to make it much easier for frontend developers without computer vision background to use object detection in their projects. Apart from that, I have https://github.com/SkalskiP/ILearnDeepLearning.py which is a repository containing examples related to my blog posts on Medium https://medium.com/@piotr.skalski92.
By the way, I have created an NPM package, which can also make it easier for you to deploy YOLOv5 in the browser. https://github.com/SkalskiP/yolov5js
What are some alternatives?
ultralytics - NEW - YOLOv8 π in PyTorch > ONNX > OpenVINO > CoreML > TFLite
yolov5 - YOLOv5 π in PyTorch > ONNX > CoreML > TFLite
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
TensorFlowTTS-ts - This project implements TensorflowTTS in Tensorflow.js using Typescript, enabling real-time text-to-speech in the browser. With pre-trained model for English language, you can generate high-quality speech from text input.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
ILearnDeepLearning.py - This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well as to check how the code works in the action.
glami-1m - The largest multilingual image-text classification dataset. It contains fashion products.
links-detector - π ππ» Links Detector makes printed links clickable via your smartphone camera. No need to type a link in, just scan and click on it.
make-sense - Free to use online tool for labelling photos. https://makesense.ai
IDP - IDP is an open source AI IDE for data scientists and big data engineers.
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.
tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.