D-Drone_v2
notebooks
| D-Drone_v2 | notebooks | |
|---|---|---|
| 1 | 19 | |
| 10 | 4,164 | |
| - | 3.2% | |
| 10.0 | 8.3 | |
| almost 2 years ago | 20 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 only | - |
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D-Drone_v2
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An ad-hoc idea for defeating the Shahed-136 suicide drone
Visual detection: https://github.com/5a7man/D-Drone_v2
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
What are some alternatives?
SeeAI - Enabling computers to perform NLP on data obtained from advanced computer vision
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
Human-pose-estimation - A quick tutorial on multi-pose estimation with OpenCV, Tensorflow and MoveNet lightning.
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
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.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
samples - WebRTC Web demos and samples
glami-1m - The largest multilingual image-text classification dataset. It contains fashion products.
Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment - This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach.
make-sense - Free to use online tool for labelling photos. https://makesense.ai
timm-flutter-pytorch-lite-blogpost - PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter.