Adding SAM2 to the Model Zoo!

[korbit-ai]

What changes are proposed in this pull request?

Adding Segment Anything 2 to the Fiftyone model zoo.

How is this patch tested? If it is not, please explain why.

Tested manually with different configurations -

1. Images - prompted with bounding boxes, prompted with keypoints and no prompts
2. Videos - prompted with bounding boxes and prompted with keypoints

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.

Added SAM2 into the Fiftyone model zoo with inference support for both images and videos.

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

Box prompt for Images

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "quickstart", max_samples=25, shuffle=True, seed=51
)

model = foz.load_zoo_model("segment-anything-2-hiera-tiny-image-torch")

# Prompt with boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",
)

session = fo.launch_app(dataset)

Keypoint prompt for Images

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart")
dataset = dataset.filter_labels("ground_truth", F("label") == "person")


# Generate some keypoints
model = foz.load_zoo_model("keypoint-rcnn-resnet50-fpn-coco-torch")
dataset.default_skeleton = model.skeleton
dataset.apply_model(model, label_field="gt")

model = foz.load_zoo_model("segment-anything-2-hiera-tiny-image-torch")

# Prompt with keypoints
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="gt_keypoints",
)

session = fo.launch_app(dataset)

Automatic segmentation for Images

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "quickstart", max_samples=5, shuffle=True, seed=51
)

model = foz.load_zoo_model("segment-anything-2-hiera-tiny-image-torch")

# Automatic segmentation
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

Prompting for Videos

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2)

# Only retain detections on the first frame of each video
for sample in dataset:
    for frame_idx in sample.frames:
        frame = sample.frames[frame_idx]
        if frame_idx >= 2:
            frame.detections = None
        sample.save()

model = foz.load_zoo_model("segment-anything-2-hiera-tiny-video-torch")

# Prompt with boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="frames.detections", # You can also pass in a keypoint field here
)

session = fo.launch_app(dataset)

Summary by CodeRabbit

• New Features

  • Introduced advanced image and video segmentation capabilities using the Segment Anything 2 (SAM2) model.
  • Added tools for users to apply segmentation through various prompting methods including bounding boxes and keypoints.

• Improvements

  • Enhanced the functionality and maintainability of segmentation methods with a simplified control flow.
  • Improved handling for extracting labels in keypoint processing, enhancing robustness.

• Bug Fixes

  • Refined dataset handling for video and non-video data, ensuring appropriate model application based on dataset characteristics.

• Chores

  • Updated linting configuration to accommodate OpenCV functionalities and prevent false positive warnings.

Description by Korbit AI

What change is being made?

Add Segment Anything Model 2 (SAM2) to the FiftyOne Model Zoo, including support for both image and video segmentation.

Why are these changes being made?

These changes introduce the latest SAM2 models, which provide advanced segmentation capabilities for images and videos, enhancing the FiftyOne Model Zoo's offerings. This addition allows users to leverage state-of-the-art segmentation models for various applications, supporting both CPU and GPU environments.

[korbit-ai]

Clone of the PR voxel51/fiftyone#4671

[development-korbit-ai-mentor]

👋 I'm here to help you review your pull request. When you're ready for me to perform a review, you can comment anywhere on this pull request with this command: /korbit-review.

As a reminder, here are some helpful tips on how we can collaborate together:

• To have me re-scan your pull request, simply re-invoke the /korbit-review command in a new comment.
• You can interact with me by tagging @development-korbit-ai-mentor in any conversation in your pull requests.
• On any comment I make on your code, please leave a 👍 if it is helpful and a 👎 if it is unhelpful. This will help me learn and improve as we work together
• Lastly, to learn more, check out our Docs.

[korbit-ai]

My review is in progress 📖 - I will have feedback for you in a few minutes!

[coderabbitai]

Warning

Rate limit exceeded

@korbit-ai[bot] has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 18 minutes and 14 seconds before requesting another review.

How to resolve this issue?

After the wait time has elapsed, a review can be triggered using the @coderabbitai review command as a PR comment. Alternatively, push new commits to this PR.

We recommend that you space out your commits to avoid hitting the rate limit.

How do rate limits work?

CodeRabbit enforces hourly rate limits for each developer per organization.

Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout.

Please see our FAQ for further information.

Commits

Files that changed from the base of the PR and between 278d934 and 7d9de22.

---

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

Share

• X
• Mastodon
• Reddit
• LinkedIn

Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>.
  • Generate unit testing code for this file.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai generate unit testing code for this file.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai generate interesting stats about this repository and render them as a table.
  • @coderabbitai show all the console.log statements in this repository.
  • @coderabbitai read src/utils.ts and generate unit testing code.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Other keywords and placeholders

• Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
• Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
• Add @coderabbitai anywhere in the PR title to generate the title automatically.

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.

[korbit-ai]

I have reviewed your code and did not find any issues!

---

Please note that I can make mistakes, and you should still encourage your team to review your code as well.