| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - ru |
| tags: |
| - minecraft |
| - biome |
| - classification |
| - game |
| - computer-vision |
| - time-of-day |
| pretty_name: Minecraft Biome, Dimension & Time Dataset |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # 🌍 Minecraft Vision Dataset |
|
|
| Manually collected Minecraft screenshots for classification tasks. |
| Useful for training AI agents, bots, and computer vision models. |
|
|
| ## 📦 What's inside |
| - Biomes, dimensions, time of day, and more. |
| - See the repository file browser for the full list of classes and current image counts. |
|
|
| ## 📂 Dataset structure |
| The root contains one folder per class. New classes are added over time. |
|
|
| ## 🔍 Current statistics |
| Exact numbers of classes and images are always visible on the [dataset page](https://huggingface.co/datasets/Nininkkka/Minecraft-vision-dataset). |
| Browse the "Files and versions" tab – everything is there. |
|
|
| ## 🔄 Continuous updates |
| This dataset is regularly expanded. No manual changelog is kept – check the commit history for updates. |
|
|
| ----------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| # 🎮 Minecraft Vision Dataset - Recommended Groups |
|
|
| ⚠️ **Important:** Don't mix groups in one training run. Pick **one** below. |
|
|
| --- |
|
|
| ## 🧩 Recommended Groups |
|
|
| 1. **🌍 Core biomes (128x128, 7 classes):** |
| `caves, end, forests, nether, oceans, plains, sky` |
| *(Most popular & balanced)* |
|
|
| 2. **🌞 Time of day (128x128, 4 classes):** |
| `time-day, time-noon, time-night, time-midnight` |
|
|
| 3. **📸 High resolution (384x384, 5 classes):** |
| `caves_384x384, desert_384x384, nether_384x384, end_384x384, forests_384x384` |
|
|
| --- |
|
|
| ## 🚀 Quick filter example (PyTorch) |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("Nininkkka/Minecraft-vision-dataset", split="train", streaming=True) |
| |
| # Filter Group 1 |
| def filter_group1(x): |
| path = x["image"]["path"] |
| return "/v1/" in path and any(b in path for b in ["caves/","end/","forests/","nether/","oceans/","plains/","sky/"]) |
| |
| group1 = ds.filter(filter_group1) |
