How to Fine-Tune Foundation Models to Auto-Label Training Data

• segment-anything

  56 44,158 0.0 Jupyter Notebook

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  

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.

• Encord Active

  6 420 8.8 Python

  Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.

  

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.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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