| --- |
| license: mit |
| base_model: facebook/sam2-hiera-large |
| tags: |
| - ropedia-academy |
| - advanced |
| - gpu |
| - todo |
| - embodied-ai |
| - track-a |
| - track-d |
| --- |
| |
| # 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](https://github.com/facebookresearch/sam2) | |
| | **Notebook** | [](https://colab.research.google.com/github/ChaoYue0307/ropedia-academy/blob/main/notebooks/advanced/C_sam2_video_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](https://colab.research.google.com/github/ChaoYue0307/ropedia-academy/blob/main/notebooks/advanced/C_sam2_video_segmentation.ipynb) → **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](https://github.com/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](https://huggingface.co/spaces/cy0307/ropedia-demos) |
| - 🤗 **All models + collection:** [https://huggingface.co/cy0307](https://huggingface.co/cy0307) |
| - 📚 **Course & all labs:** [https://chaoyue0307.github.io/ropedia-academy/](https://chaoyue0307.github.io/ropedia-academy/) · [Labs tab](https://chaoyue0307.github.io/ropedia-academy/labs) |
| - 💻 **Source / notebooks:** [github.com/ChaoYue0307/ropedia-academy](https://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).* |
|
|
