bearid
Hypraptive BearID project. FaceNet for bears. (by hypraptive)
Face Recognition
The world's simplest facial recognition api for Python and the command line (by ageitgey)
bearid | Face Recognition | |
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3 | 34 | |
46 | 51,816 | |
- | - | |
3.3 | 0.0 | |
4 months ago | 2 months ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
bearid
Posts with mentions or reviews of bearid.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-10.
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Bearcam Companion: GitHub, User Groups and Rekognition
By the end of my previous post, I had reached a good baseline for the Bearcam Companion app. It was past time to start tracking the code in a version control system. Since we already have the BearID Project on GitHub, I decided to use the same for this project.
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BearCam Companion
We initially focused on building a photo dataset of the bears at Brooks Falls. We collected photos from individuals and a large set from the National Parks Service's bear monitoring program at Katmai. From there we got involved with Dr. Melanie Clapham, a conservation scientist in British Columbia, studying the bears of Glendale Cove. We built a computer to run our ML training and developed an open source application, bearid, which we provided to Melanie as a Docker container she could run in the field on a laptop.
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AWS Community Builders: My First Step
The BearID Project application bearid runs on a server. It takes photos or videos as inputs and outputs a sequence of boxes labeled with the bear's name. This can only happen after someone goes into the field (or, forest), collects a bunch of SD cards from trail cameras, and uploads them to the server. This happens a few times a year. If the trail camera was connected, it could send data to the cloud, but the connection would probably be expensive. If the camera could detect and identify the bear and send only the metadata to a server, we could update the researcher in real time. I have less experience in this area, so connecting it all together with AWS services will be a real learning experience for me.
Face Recognition
Posts with mentions or reviews of Face Recognition.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-28.
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Security Image Recognition
Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition
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Facial recognition software/API for face-blind teacher?
Have you tried this repo: github
- GitHub - ageitgey/face_recognition: The world's simplest facial recognition api for Python and the command line
- The simplest facial recognition API for Python
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Every thing you need to know about Machine Learning Pipeline
One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.
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Reverse image search / facial recognition
Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition
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Made a easy to use face recognition library
It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.
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Salisbury council meeting minutes addressing conspiracy theorist councillors
You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition
- Face comparison in Stable Diffusion
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Understanding different Algorithms for Facial Recognition
To know more about face_recognition module https://github.com/ageitgey/face_recognition