make-sense
notebooks
| make-sense | notebooks | |
|---|---|---|
| 7 | 19 | |
| 2,969 | 4,164 | |
| - | 3.2% | |
| 2.4 | 8.3 | |
| about 2 months ago | 19 days ago | |
| TypeScript | Jupyter Notebook | |
| GNU General Public License v3.0 only | - |
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make-sense
- Need help identifying a good open source data annotation tool
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Free instance segmentation annotation tool
Hi 👋🏻! I’m creator of https://makesense.ai. It supports Instance Segmentation. Take a look at the repo: https://github.com/SkalskiP/make-sense
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Data Labelling Software
I created tool called MakeSense: https://github.com/SkalskiP/make-sense it is completely free and open sourced on GH
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Roboflow 100: A New Object Detection Benchmark
Haven't heard of those two, but would be really awesome to see an integration. We have an open API[1] for just this reason: we really want to make it easy to use (and source) your data across all the different tools out there. We've recently launched integrations with other labeling[2] and AutoML[3] tools (and have integrations with the big-cloud AutoML tools as well[4]). We're hoping to have a bunch more integrations with other MLOps tools & platforms in 2023.
Re synthetic data specifically, we've written a couple of how-to guides for creating data from context augmentation[5], Unity Perception[6], and Stable Diffusion[7] & are talking to some others as well; it seems like a natural integration point (and someplace where we don't need to reinvent the wheel).
[1] https://docs.roboflow.com/rest-api
[2] https://github.com/SkalskiP/make-sense/pull/298
[3] https://github.com/ultralytics/yolov5/discussions/10425
[4] https://docs.roboflow.com/train/pro-third-party-training-int...
[5] https://blog.roboflow.com/how-to-create-a-synthetic-dataset-...
[6] https://blog.roboflow.com/unity-perception-synthetic-dataset...
[7] https://blog.roboflow.com/synthetic-data-with-stable-diffusi...
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[Project] Football Players Tracking with YOLOv5 + ByteTRACK
Two things that carried me the most are my blog https://medium.com/@skalskip - which gave me my first job in computer vision, and my open-source GitHub project: https://github.com/SkalskiP/make-sense - which gave me all my jobs since I created it.
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Hi everyone! I'm Piotr and for several years I have been developing a small open-source project for labeling photos - makesense.ai. I added a new feature this weekend. You can use YOLOv5 models to automatically annotate photos.
Link to GitHub project: https://github.com/SkalskiP/make-sense
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Tool for human pose estimation keypoint annotation
I have also looked into make-sense and currently the docker and the npm refuse to work. I have already opened a ticket describing the issue .
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?
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]
rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation
AID - One-Stop System for Machine Learning.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
glami-1m - The largest multilingual image-text classification dataset. It contains fashion products.
Universal Data Tool - Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
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.
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
timm-flutter-pytorch-lite-blogpost - PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter.