Upload from Ropedia Academy

README.md

@@ -3,40 +3,72 @@ license: mit
 tags:
 - ropedia-academy
 - advanced
+- gpu
 - todo
+- embodied-ai
 - track-d
 ---
 
-# NeRF from scratch (tiny_nerf)  🚧 placeholder
+# NeRF from scratch (tiny_nerf)  🚧 not trained yet
 
-> **Status — not trained yet.** This is a *documented placeholder* for an advanced, GPU-heavy pipeline from **[Ropedia Academy](https://chaoyue0307.github.io/ropedia-academy/)**. The checkpoint, metrics and plots are **TODO** — train it on a GPU (link below) and the weights + a full results card land here.
+> Train a NeRF from scratch — runs on the GPU in minutes (too slow to CPU-train here).
 
-Train a NeRF from scratch — runs on the GPU in minutes (too slow to CPU-train here).
+**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** | From scratch |
 | **Task** | neural radiance field |
-| **Built on** | [self-contained PyTorch (bmild tiny_nerf data)](https://github.com/bmild/nerf) |
+| **Training objective** | Volume-rendering photometric loss through a positional-encoded MLP. |
 | **Track** | B · 3D & rendering |
+| **Built on** | [self-contained PyTorch (bmild tiny_nerf data)](https://github.com/bmild/nerf) |
 | **Notebook** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ChaoYue0307/ropedia-academy/blob/main/notebooks/training/B_nerf_from_scratch.ipynb) |
-| **Compute / storage / time** | see the *Compute · storage · time* table inside the notebook (GPU required) |
+| **Compute / storage / time** | GPU required — see the *Compute · storage · time* table in the notebook |
+
+## Dataset
+- **Source:** tiny_nerf (bmild) — a single Lego scene (106 views).
+
+## 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 **PSNR (held-out views)** 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: **From scratch**.
 
 ## How to fill this repo
-1. Open the [notebook in Colab](https://colab.research.google.com/github/ChaoYue0307/ropedia-academy/blob/main/notebooks/training/B_nerf_from_scratch.ipynb) → **Runtime → GPU → Run all** (trains/runs the real pipeline).
-2. Run its **Publish to the Hugging Face Hub** step (or `HfApi().upload_folder(...)`) to push the checkpoint + metrics + plots here, replacing this placeholder.
+1. Open the [notebook in Colab](https://colab.research.google.com/github/ChaoYue0307/ropedia-academy/blob/main/notebooks/training/B_nerf_from_scratch.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** ([self-contained PyTorch (bmild tiny_nerf data)](https://github.com/bmild/nerf)) and **dataset** are each under their own licenses — check the upstream source before redistribution.
 
-## TODO
-- [ ] Train / run on a GPU (see the notebook)
-- [ ] Upload the checkpoint / adapter weights
-- [ ] Add `metrics.json` (loss / eval history)
-- [ ] Add result figures & sample outputs
-- [ ] Replace this placeholder card with the real results
+## 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/}}
+}
+```
 
-## Results
-_TODO — add final metrics and plots after training. (Placeholder.)_
+**Method / original work:** Mildenhall et al., *NeRF*, ECCV 2020.
 
-## Links to tracks A–D
-Relates to: **D** — see the *How this links to tracks A–D* note in the notebook.
+## 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:** D
 
 ---
-*Placeholder in the [Ropedia Academy](https://chaoyue0307.github.io/ropedia-academy/) trained-model collection. Browse all labs in the [Labs tab](https://chaoyue0307.github.io/ropedia-academy/labs).*
+*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).*