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Tutorial Refreshes #4207

Merged
merged 9 commits into from Mar 28, 2024
Merged

Tutorial Refreshes #4207

merged 9 commits into from Mar 28, 2024

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jacobmarks
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@jacobmarks jacobmarks commented Mar 27, 2024

What changes are proposed in this pull request?

FiftyOne has evolved a ton over the past few years — in particular, the UI in the app, the visualization of embeddings within a panel in the app, the ultralytics and HF integrations, and the expansion of the model zoo. All of these developments and changes have made many workflows easier in FiftyOne, and have changed the way things appear when following tutorials line by line.

This PR updates and enhances the following tutorials:

  • Evaluating Detections
  • Finding Detection Mistakes
  • Fine-tuning YOLOv8 Models
  • Monocular Depth Estimation
  • Using Image Embeddings

(Details)

Release Notes

Is this a user-facing change that should be mentioned in the release notes?

  • No. You can skip the rest of this section.
  • Yes. Give a description of this change to be included in the release
    notes for FiftyOne users.

(Details in 1-2 sentences. You can just refer to another PR with a description
if this PR is part of a larger change.)

What areas of FiftyOne does this PR affect?

  • App: FiftyOne application changes
  • Build: Build and test infrastructure changes
  • Core: Core fiftyone Python library changes
  • Documentation: FiftyOne documentation changes
  • Other

Summary by CodeRabbit

  • Documentation
    • Added a new section on Hugging Face Transformers Integration for monocular depth estimation in tutorials.
    • Updated the YOLOv8 tutorial with a new link presentation and added a section on FiftyOne's Ultralytics Integration.

@jacobmarks jacobmarks requested a review from brimoor March 27, 2024 23:08
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coderabbitai bot commented Mar 27, 2024

Walkthrough

The recent updates introduce integrations with Hugging Face Transformers for monocular depth estimation and enhance the YOLOv8 tutorial with a focus on FiftyOne's Ultralytics Integration. These changes enrich the tutorials by demonstrating the application of transformer-based models and providing practical guidance on using YOLOv8 with FiftyOne datasets, highlighting the evolving synergy between advanced model architectures and dataset management tools.

Changes

File Path Change Summary
.../monocular_depth_estimation.ipynb Introduced Hugging Face Transformers Integration for monocular depth estimation with FiftyOne's capabilities.
.../yolov8.ipynb Updated YOLOv8 announcement link and added FiftyOne's Ultralytics Integration section with inference code snippet.

🐇✨
To the depth unseen, to the corners unlit,
We bring transformers, and models well-fit.
With YOLOv8's might, and FiftyOne's grace,
We chart unseen paths, in data's vast space.
🌟📚 Let our code merge, let our datasets unite,
In tutorials woven, by the moon's gentle light.
🥕🎉

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Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits Files that changed from the base of the PR and between 7bea0c2 and e11fe9e.
Files ignored due to path filters (38)
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Files selected for processing (2)
  • docs/source/tutorials/monocular_depth_estimation.ipynb (2 hunks)
  • docs/source/tutorials/yolov8.ipynb (2 hunks)
Files skipped from review due to trivial changes (1)
  • docs/source/tutorials/monocular_depth_estimation.ipynb

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codecov bot commented Mar 27, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 28.86%. Comparing base (6f28ab0) to head (e11fe9e).
Report is 7 commits behind head on develop.

Additional details and impacted files
@@           Coverage Diff            @@
##           develop    #4207   +/-   ##
========================================
  Coverage    28.85%   28.86%           
========================================
  Files          768      769    +1     
  Lines        97048    97140   +92     
  Branches      1120     1128    +8     
========================================
+ Hits         28004    28039   +35     
- Misses       69044    69101   +57     
Flag Coverage Δ
app 16.03% <ø> (+0.02%) ⬆️
python 99.23% <ø> (ø)

Flags with carried forward coverage won't be shown. Click here to find out more.

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LGTM 🚀 FYI just did a static review: trusting that you built the docs locally and verified that the HTML-rendered tutorials look good to you

@jacobmarks
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Yep! I checked the html looks good for all tutorials :)

@jacobmarks jacobmarks merged commit 33e1d9e into develop Mar 28, 2024
10 checks passed
@jacobmarks jacobmarks deleted the docs/tutorial-updates branch March 28, 2024 16:37
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