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cy0307/c-sam2-video-segmentation

sourceHugging Facemitupdated 3mo agoView on Hugging Face

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Model Card

SAM 2 — video segmentation 🚧 not trained yet

Click an object on frame 0; SAM 2 tracks its mask through the whole clip.

Status — documented recipe (placeholder). A production-grade pipeline from [Ropedia Academy](https://chaoyue0307.github.io/ropedia-academy/) for an advanced, GPU-heavy task. Everything below — base model, objective, dataset, config, the exact evaluation — is specified; the weights / metrics / figures land here automatically when you run the notebook on a GPU (one click below). Try the trained models live in the [Ropedia demos Space](https://huggingface.co/spaces/cy0307/ropedia-demos).

At a glance


Base model	facebook/sam2-hiera-large (pretrained)
Task	promptable video object segmentation
Training objective	Promptable mask prediction + memory propagation across frames (inference).
Track	C · Egocentric vision
Built on	facebookresearch/sam2
Notebook	![Open In Colab](https://colab.research.google.com/github/ChaoYue0307/ropedia-academy/blob/main/notebooks/advanced/Csam2video_segmentation.ipynb)
Compute / storage / time	GPU required — see the Compute · storage · time table in the notebook

Dataset

—Source: Your video + click/box prompts. Benchmark: DAVIS 2017 / SA-V.

Training config

GPU-scale — the notebook ships a demo profile (free Colab T4) and a full profile, with an exact Compute · storage · time table. Hyperparameters (optimizer, steps, batch, LoRA rank, …) are in the training cell.

Evaluation results

⏳ Pending — run the notebook on a GPU to fill this in. This lab reports J&F mean (region IoU + boundary F) on a held-out split (see its Evaluate cell).

Inference example

No weights are published yet. After a GPU run, load the checkpoint/adapter the notebook saves (it also has a ready inference cell). Base model: facebook/sam2-hiera-large (pretrained).

How to fill this repo

1.Open the notebook in Colab → Runtime → GPU → Run all (runs the real pipeline).
2.Run its Publish to the Hugging Face Hub step (or HfApi().upload_folder(...)) — the checkpoint + metrics.json + figures replace this placeholder.

—[ ] Train / run on a GPU · [ ] upload weights · [ ] add metrics.json · [ ] add figures · [ ] swap in the real results card

Limitations

Not yet trained — no numbers to report. The pipeline is GPU-heavy (see the compute table); on free Colab use the demo-scale settings. This is an educational, reproducible recipe, not a tuned production release.

License

Code: MIT (this repository). The base model (facebookresearch/sam2) and dataset are each under their own licenses — check the upstream source before redistribution.

Citation

bibtex

@misc{ropedia_academy,
  title  = {Ropedia Academy: an interactive course on embodied & spatial AI},
  author = {Ropedia Academy},
  year   = {2026},
  howpublished = {\url{https://chaoyue0307.github.io/ropedia-academy/}}
}

Method / original work: Ravi et al., SAM 2, 2024.

Related assets

—🚀 Live demos: https://huggingface.co/spaces/cy0307/ropedia-demos
—🤗 All models + collection: https://huggingface.co/cy0307
—📚 Course & all labs: https://chaoyue0307.github.io/ropedia-academy/ · Labs tab
—💻 Source / notebooks: github.com/ChaoYue0307/ropedia-academy
—🔗 Relates to tracks: A · D

Documented placeholder in the [Ropedia Academy](https://chaoyue0307.github.io/ropedia-academy/) collection — train it on a GPU to publish the real model. Contributions welcome on [GitHub](https://github.com/ChaoYue0307/ropedia-academy).