DenseDepth
fiftyone
DenseDepth | fiftyone | |
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5 | 21 | |
1,533 | 6,843 | |
- | 2.5% | |
0.0 | 10.0 | |
over 1 year ago | 3 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
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DenseDepth
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How to Estimate Depth from a Single Image
For a long time, the state-of-the-art models for monocular depth estimation such as DORN and DenseDepth were built with convolutional neural networks. Recently, however, both transformer-based models such as DPT and GLPN, and diffusion-based models like Marigold have achieved remarkable results!
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Turn your photo into Christmas ball. A bas-relief, not a lithophany, so no internal lightning is needed! My free programs 'Amazing STL Creator' are only at Prusa Printables
Instead of just doing grayscale for depth, you should consider using “monocular depth estimation” that actually tries to reconstruct a depth map from an RGB image. There are a variety of open-source libs available like this one.
- alô meus programadores do r/brasil preciso de uma ajuda...
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Looking for a fast monocular depth estimation library to use in a Rust project.
After that I have to do the same for Python I think, and then I have to find out how to figure out how to use a library like https://github.com/ialhashim/DenseDepth or https://github.com/nianticlabs/monodepth2 for that GStreamer plugin (or element, still trying to grasp the terminology here)
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MiDaS - Monocular Depth Estimation -- Includes an Optimized Model for ROS
Others model implemented like github.com/ialhashim/DenseDepth have constraints on input (I think only 4:3 is one of them, if I remember correctly)
fiftyone
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Anomaly Detection with FiftyOne and Anomalib
pip install -U git+https://github.com/voxel51/fiftyone.git
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May 8, 2024 AI, Machine Learning and Computer Vision Meetup
In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.
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Voxel51 Is Hiring AI Researchers and Scientists — What the New Open Science Positions Mean
My experience has been much like this. For twenty years, I’ve emphasized scientific and engineering discovery in my work as an academic researcher, publishing these findings at the top conferences in computer vision, AI, and related fields. Yet, at my company, we focus on infrastructure that enables others to unlock scientific discovery. We have built a software framework that enables its users to do better work when training models and curating datasets with large unstructured, visual data — it’s kind of like a PyTorch++ or a Snowflake for unstructured data. This software stack, called FiftyOne in its single-user open source incarnation and FiftyOne Teams in its collaborative enterprise version, has garnered millions of installations and a vibrant user community.
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How to Estimate Depth from a Single Image
We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.
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How to Cluster Images
With all that background out of the way, let’s turn theory into practice and learn how to use clustering to structure our unstructured data. We’ll be leveraging two open-source machine learning libraries: scikit-learn, which comes pre-packaged with implementations of most common clustering algorithms, and fiftyone, which streamlines the management and visualization of unstructured data:
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Efficiently Managing and Querying Visual Data With MongoDB Atlas Vector Search and FiftyOne
FiftyOne is the leading open-source toolkit for the curation and visualization of unstructured data, built on top of MongoDB. It leverages the non-relational nature of MongoDB to provide an intuitive interface for working with datasets consisting of images, videos, point clouds, PDFs, and more.
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FiftyOne Computer Vision Tips and Tricks - March 15, 2024
Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit.
- FLaNK AI for 11 March 2024
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How to Build a Semantic Search Engine for Emojis
If you want to perform emoji searches locally with the same visual interface, you can do so with the Emoji Search plugin for FiftyOne.
- FLaNK Stack Weekly for 07August2023
What are some alternatives?
MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
monodepth2 - [ICCV 2019] Monocular depth estimation from a single image
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
ZoeDepth - Metric depth estimation from a single image
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
Deep-Learning-Push-Up-Counter - Deep Learning approach to count the number of repetitions in a video of push ups or pull ups.
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
LFattNet - Attention-based View Selection Networks for Light-field Disparity Estimation
streamlit - Streamlit — A faster way to build and share data apps.
analytics-zoo - Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.