Encord Active
panda_patrol
| Encord Active | panda_patrol | |
|---|---|---|
| 6 | 2 | |
| 420 | 21 | |
| 0.5% | - | |
| 8.8 | 9.2 | |
| 15 days ago | 4 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
Encord Active
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Launch HN: Encord (YC W21) – Unit testing for computer vision models
We base our pricing on your user and consumption scale and would be happy to discuss this with you directly. Please feel free to explore the OS version of Active at https://github.com/encord-team/encord-active. Note that some features, such as natural language search using GPU accelerated APIs, are not included in the cloud version.
- We tried injecting hallucinogenics into vision models
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How to Fine-Tune Foundation Models to Auto-Label Training Data
Webinar from last week on how to fine-tune VFMs, specifically Meta's Segment Anything Model (SAM).
What you'll need to follow along the fine-tuning walkthrough:
Images, ground-truth masks, and optionally, prompts from the Stamp Verification (StaVer) Dataset on Kaggle (https://www.kaggle.com/datasets/rtatman/stamp-verification-s...)
Download the model weights for SAM the official GitHub repo (https://github.com/facebookresearch/segment-anything)
Good understanding of the model architecture Segment Anything paper (https://ai.meta.com/research/publications/segment-anything/)
GPU infra the NVIDIA A100 should do for this fine-tuning.
Data curation and model evaluation tool Encord Active (https://github.com/encord-team/encord-active)
Colab walkthrough for fine-tuning: https://colab.research.google.com/github/encord-team/encord-...
I'd love to get your thoughts and feedback. Thank you.
- Show HN: Open-source toolkit for ML model evaluation and active learning
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modAL VS encord-active - a user suggested alternative
2 projects | 12 Apr 2023
An active learning toolkit I use to find failure modes in my vision datasets, prioritize which data to label next using the different acquisition functions.
panda_patrol
- Show HN: Data monitoring and profiling with 1 function call
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Panda Patrol: a Python library for generating data tests and testing your data pipelines
Github: https://github.com/aivanzhang/panda_patrol
What are some alternatives?
tsuki-wscp - Web scraper for AI/ML training
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
awesome-open-data-centric-ai - Curated list of open source tooling for data-centric AI on unstructured data.
swiple - Swiple enables you to easily observe, understand, validate and improve the quality of your data
modAL - A modular active learning framework for Python
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
feast - Feature Store for Machine Learning
data-diff - Compare tables within or across databases