Encord Active VS cleanlab

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.

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.

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.

We have feely open-sourced our new method for improving segmentation data, published a paper on the research behind it, and released a 5-min code tutorial. You can also read more in the blog if you'd like.

We’ve open-sourced one line of code to find errors in any object detection dataset via Cleanlab Object Detection, which can utilize any existing object detection model you’ve trained.

If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data.

Sharing some context here: in grad school, I spent months writing custom data analysis code and training ML models to find errors in large-scale datasets like ImageNet, work that eventually resulted in this paper (https://arxiv.org/abs/2103.14749) and demo (https://labelerrors.com/).

Since then, I’ve been interested in building tools to automate this sort of analysis. We’ve finally gotten to the point where a web app can do automatically in a couple of hours what I spent months doing in Jupyter notebooks back in 2019—2020. It was really neat to see the software we built automatically produce the same figures and tables that are in our papers.

The blog post shared here is results-focused, talking about some of the data and dataset-level issues that a tool using data-centric AI algorithms can automatically find in ImageNet, which we used as a case study. Happy to answer any questions about the post or data-centric AI in general here!

P.S. all of our core algorithms are open-source, in case any of you are interested in checking out the code: https://github.com/cleanlab/cleanlab

Cleanlab Studio offers a user-friendly interface that allows you to visualize and review the identified issues in your dataset. You can easily explore the detected errors and make corrections with confidence. It's a hassle-free solution that can save you valuable time and improve your overall e-commerce operations. If you'd like more details you can check this article out.

I thought this community might find it very useful that Databricks has partnered with Cleanlab to bring automated data correction and ML model improvement for both structured and unstructured datasets to all Databricks users.

I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset.

I recently published a blog introducing Datalab and an open-source Python implementation that is easy-to-use for all data types (image, text, tabular, audio, etc). For data scientists, I’ve made a quick Jupyter tutorial to run Datalab on your own data.