README.md

---
pretty_name: "CraftSight: A Multi-label Perception Dataset for Minecraft Agents"
license: other
license_name: "cdla-sharing-1.0-mixed"
license_link: https://cdla.dev/sharing-1-0/
task_categories:
  - image-classification
  - image-feature-extraction
annotations_creators:
  - expert-generated
  - machine-generated
size_categories:
  - 10K<n<100K
tags:
  - minecraft
  - computer-vision
  - multi-label-classification
  - image-feature-extraction
  - gameplay
  - representation-learning
  - agent-perception
  - reinforcement-learning
  - embodied-ai
configs:
  - config_name: core
    default: true

    metadata_filenames:
      - core_train.csv
      - core_val.csv
      - core_test.csv

    data_files:
      - split: train
        path:
          - images/**/*.webp
          - core_train.csv

      - split: validation
        path:
          - images/**/*.webp
          - core_val.csv

      - split: test
        path:
          - images/**/*.webp
          - core_test.csv

  - config_name: full

    metadata_filenames:
      - full_train.csv
      - full_val.csv
      - full_test.csv

    data_files:
      - split: train
        path:
          - images/**/*.webp
          - full_train.csv

      - split: validation
        path:
          - images/**/*.webp
          - full_val.csv

      - split: test
        path:
          - images/**/*.webp
          - full_test.csv

  - config_name: unlabeled

    metadata_filenames:
      - unlabeled.csv

    data_files:
      - split: train
        path:
          - images/**/*.webp
          - unlabeled.csv
---

# CraftSight

<p align="center">
  <img src="https://img.shields.io/badge/version-1.0.1-blue" alt="Version">
</p>

**Frame-level multi-label visual annotations for Minecraft agents.**

> **Dataset creation toolkit:** CraftSight is built and maintained with [CraftSight Labeler](https://github.com/Krows7/CraftSight-Labeler), an open-source, browser-based annotation tool and reproducible release pipeline for multi-label Minecraft vision datasets. It supports manual and model-assisted labeling, structured game-state annotations, and trajectory-safe train/validation/test splits.

CraftSight provides Minecraft gameplay frames annotated with terrain, hazards, blocks, interfaces, structures, mobs, and scene-level context. It is intended for multi-label perception, representation learning, active learning, and embodied-agent research.

> **Not an official Minecraft product. Not approved by or associated with Mojang or Microsoft.**

> Minecraft is a trademark of the Microsoft group of companies. Minecraft-related rights remain with Mojang, Microsoft, and their licensors.

## Dataset overview

| Item | Count |
|---|---:|
| Indexed images | 64,279 |
| Annotated images | 3,058 |
| Active annotated images | 2,823 |
| Ignored annotated images | 235 |
| Unlabeled images | 61,221 |
| Benchmark trajectories | 571 |
| Core labels | 19 |
| Full labels | 48 |
| Rows with optional game state | 50 |

CraftSight exposes three configurations:

| Configuration | Rows | Targets | Recommended use |
|---|---:|---:|---|
| **`core`** | 2,823 | 19 labels | Standard training and comparable evaluation |
| **`full`** | 2,823 | 48 labels | Long-tail, few-shot, active-learning, and exploratory work |
| **`unlabeled`** | 61,221 | none | Self-supervised learning, embeddings, clustering, retrieval, and annotation expansion |

`core` and `full` contain exactly the same active frames and use the same trajectory-level train/validation/test assignment. They differ only in their `label_*` columns.

## Quick start
```python
from datasets import load_dataset

dataset = load_dataset("Krows7/CraftSight-Minecraft", "core")

print(dataset)

example = dataset["train"][0]
image = example["image"]

print(image)
print(image.size)
print(example["label_water"])
```

The `image` column is created automatically by Hugging Face `ImageFolder`. Accessing `example["image"]` returns a decoded Pillow image; no manual path joining, download call, or `Image.open()` step is required.

`core` is the default configuration:

```python
dataset = load_dataset("Krows7/CraftSight-Minecraft")
```

Load all 48 labels:

```python
full = load_dataset("Krows7/CraftSight-Minecraft", "full")
```

Load the unlabeled image index:

```python
unlabeled = load_dataset(
    "Krows7/CraftSight-Minecraft",
    "unlabeled",
    split="train",
)

example = unlabeled[0]
image = example["image"]
```

## Working with labels

Every target is stored in a separate `label_<name>` column.

| Value | Meaning |
|---:|---|
| `1` | The label is present |
| `0` | The label was reviewed and is absent |
| `-1` | The label is unknown or was not reviewed |

**Never treat `-1` as a negative label.**

```python
label_columns = [
    column
    for column in dataset["train"].column_names
    if column.startswith("label_")
]
```

Example masked binary cross-entropy:

```python
import torch
import torch.nn.functional as F

# logits and targets have shape [batch_size, number_of_labels]
known = targets != -1
safe_targets = targets.clamp_min(0).float()

loss_per_target = F.binary_cross_entropy_with_logits(
    logits,
    safe_targets,
    reduction="none",
)

loss = loss_per_target[known].mean()
```

The canonical label order and numeric semantics are defined in [`label_schema.json`](label_schema.json). Class boundaries and edge cases are documented in [`label_definitions.md`](label_definitions.md).

## Images

The Viewer-facing CSV files store image references in a `file_name` column:

```text
images/<namespace>/<relative-path>.webp
```

### Access decoded images

```python
from datasets import load_dataset

dataset = load_dataset(
    "Krows7/CraftSight-Minecraft",
    "core",
    split="train",
)

example = dataset[0]
image = example["image"]

print(type(image))
print(image.mode)
print(image.size)
```

### Access the underlying path without decoding

```python
from datasets import Image, load_dataset

dataset = load_dataset(
    "Krows7/CraftSight-Minecraft",
    "core",
    split="train",
)

paths = dataset.cast_column(
    "image",
    Image(decode=False),
)

print(paths[0]["image"])
```

Depending on the loading mode, this returns a local cached path, a remote path, or embedded bytes.

### Stream examples without materializing the full dataset

```python
from datasets import load_dataset

stream = load_dataset(
    "Krows7/CraftSight-Minecraft",
    "core",
    split="train",
    streaming=True,
)

example = next(iter(stream))
image = example["image"]
```

Image provenance and permitted uses differ by `data_source`; review [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md) before downloading, redistributing, or using images commercially.

## Choosing a configuration

### `core`

Use `core` for:

- headline benchmark results;
- model comparison;
- standard multi-label training;
- threshold selection on validation data;
- experiments requiring positive support for every target in every split.

Core labels:

```text
water
drop_off
dark_cave
sand_or_redsand
tree_log
leaves
stone
inventory_open
hostile_near
wood_planks
cobblestone
torch
water_bucket_in_hand
building
hostile_present
sky_visible
open_surface
mountains
glass_pane_or_window
```

### `full`

Use `full` for:

- rare-label and long-tail studies;
- few-shot experiments;
- active learning;
- label completion;
- schema extension;
- partially labeled learning.

The full configuration preserves all 48 schema labels. Some classes have limited support, and `furnace` and `ladder` currently have no positive active examples.

<details>
<summary><strong>Show all 48 labels</strong></summary>

```text
water
lava
fire
drop_off
dark_cave
web_cobweb
ice
sand_or_redsand
gravel
tree_log
leaves
stone
crafting_table
furnace
chest
bed
inventory_open
underwater
hostile_near
wood_planks
coal_ore
iron_ore
cobblestone
door
ladder
torch
water_bucket_in_hand
building
player_damage
hostile_present
creeper
zombie
skeleton
spider
enderman
witch
cow
sheep
pig
chicken
villager
sky_visible
open_surface
farmland
crop
ocean
mountains
glass_pane_or_window
```

</details>

### `unlabeled`

Use `unlabeled` for:

- self-supervised pretraining;
- image feature extraction;
- embedding generation;
- clustering and similarity search;
- trajectory representation learning;
- active-learning candidate selection.

The loaded configuration contains an image feature and trajectory metadata:

```text
image
data_source
agent
task
seed
episode
frame_index
trajectory_id
```

## Splits

The supervised configurations use a leakage-resistant group split:

| Split | Images | Trajectories |
|---|---:|---:|
| Train | 1,961 | 499 |
| Validation | 426 | 38 |
| Test | 436 | 34 |

All frames from one `trajectory_id` remain in one split. No trajectory appears in more than one split.

The canonical assignment is stored in:

```text
splits/core/splits_by_trajectory.csv
```

The `full` configuration reuses the same manifest. Do not create a new random frame-level split, because adjacent frames may be nearly identical.

## Dataset structure

### Repository layout

```text
.
├── README.md
├── LICENSE
├── LICENSE_SCOPE.md
├── THIRD_PARTY_NOTICES.md
├── label_schema.json
├── state_schema.json
├── label_definitions.md
├── classes_core.txt
├── classes_full.txt
├── metadata.csv
├── all_images.txt
├── core_train.csv
├── core_val.csv
├── core_test.csv
├── full_train.csv
├── full_val.csv
├── full_test.csv
├── unlabeled.csv
└── images/
    ├── basalt/
    ├── mobs/
    └── custom/
```

`metadata.csv` is the canonical annotation table and includes both active and ignored rows.

The root `core_*.csv`, `full_*.csv`, and `unlabeled.csv` files are Viewer-ready `ImageFolder` metadata tables. Their `file_name` values point into the shared `images/` directory and are exposed by `datasets` as the `image` feature.

`all_images.txt` is the canonical list of all 64,279 image paths. `unlabeled.csv` contains exactly the 61,221 paths absent from `metadata.csv`.

### Annotated row fields

#### Identity and trajectory metadata

| Column | Type | Description |
|---|---|---|
| `image` | `datasets.Image` | Image linked from the shared `images/` store and decoded lazily |
| `data_source` | string | `basalt`, `mobs`, or `custom` |
| `agent` | nullable string | Agent or participant identifier |
| `task` | nullable string | Task name |
| `seed` | nullable integer | Trajectory seed |
| `episode` | nullable string | Episode identifier |
| `frame_index` | nullable integer | Frame index parsed from the source filename when available |
| `trajectory_id` | string | Group used for leakage-safe splitting |

#### Annotation metadata

| Column | Type | Description |
|---|---|---|
| `annotation_method` | string | `manual`, `model_assisted`, `model_accept`, or `model_ignore` |
| `ignore` | integer | `1` excludes the row from standard supervised splits |
| `thr_mode` | nullable string | Model-assist threshold mode |
| `thr_global` | nullable float | Global model-assist threshold |

#### Optional game state

| Column | Type | Description |
|---|---|---|
| `has_state` | integer | Whether state metadata is available |
| `state_hp` | nullable integer | Player health |
| `state_armor` | nullable integer | Armor points |
| `state_hunger` | nullable integer | Hunger level |
| `state_biome` | nullable string | Recorded biome |
| `state_selected_slot` | nullable integer | Selected hotbar slot |
| `state_held_item` | nullable string | Recorded held item |
| `state_time_of_day` | nullable string | Coarse time-of-day category |

The state schema is defined in [`state_schema.json`](state_schema.json). State is a separate modality and must not silently replace visual evidence.

## Supported tasks

CraftSight supports:

- multi-label image classification;
- image feature extraction;
- visual representation learning;
- scene and hazard recognition;
- perception modules for embodied and reinforcement-learning agents;
- active-learning and partially labeled learning research.

CraftSight is **not an object-detection dataset**. It does not provide bounding boxes, instance masks, or object coordinates.

## Evaluation

Use `core` for comparable benchmark results.

Recommended metrics:

- macro F1;
- micro F1;
- mean average precision;
- per-class average precision;
- per-class precision and recall;
- known-target and positive-example counts for each class.

Select thresholds only on validation data. Mask all `-1` targets during training and evaluation.

Do not report a class metric for a split with no known positive examples for that class.

## Dataset creation

### Image sources

| Namespace | Source | Image terms |
|---|---|---|
| `mobs` | Minecraft Screenshots Dataset with Features by `sqdartemy` | CC BY-NC 4.0 |
| `basalt` | BASALT Benchmark Evaluation Dataset, MineRL BASALT team, Zenodo record `8021960` | MIT according to the upstream record |
| `custom` | Gameplay recordings captured by the dataset maintainer | Minecraft and other applicable terms |

Image counts by namespace:

| Namespace | Indexed | Annotated | Unlabeled |
|---|---:|---:|---:|
| `basalt` | 60,677 | 2,725 | 57,952 |
| `mobs` | 3,545 | 276 | 3,269 |
| `custom` | 57 | 57 | 0 |

See [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md) for source links, attribution requirements, and source-specific conditions.

### Annotation process

Active annotations were produced through:

| Method | Active rows |
|---|---:|
| Manual | 2,742 |
| Model-assisted | 54 |
| Accepted model suggestion | 27 |

Annotations are image-level and multi-label. Multiple labels may be positive in one frame. Annotation rules prioritize visible evidence from the current frame rather than inference from adjacent frames, task names, or hidden game state.

### Quality control

The tabular release was checked for:

- schema and column-order consistency;
- valid target values;
- duplicate image paths;
- path-derived metadata consistency;
- state-field consistency;
- split coverage;
- trajectory leakage;
- alignment between `core` and `full`;
- positive-label support across core splits;
- exact partitioning of annotated and unlabeled image paths.

The current release has:

- no duplicate annotated paths;
- no overlap between `metadata.csv` and `unlabeled.csv`;
- complete coverage of `all_images.txt`;
- no trajectory leakage;
- exact agreement between core and full split assignments.

## Limitations

- Labels are highly imbalanced.
- `furnace` and `ladder` have no positive active examples.
- Several full labels are rare or absent from individual validation/test splits.
- Only 50 annotated rows contain game-state metadata.
- Frames within a trajectory remain temporally correlated.
- Sources, tasks, and agents are not uniformly represented.
- Some semantic label relationships are not mechanically enforced in every row.
- `frame_index` is unavailable for some source filenames.
- Performance may change with game version, edition, texture pack, shaders, field of view, resolution, and UI scale.

Users should report per-class support and inspect failure cases before drawing broad conclusions.

## Licensing

CraftSight uses a layered licensing model.

### Annotation and metadata layer

Maintainer-created annotations, normalized metadata, schemas, class lists, definitions, split manifests, and covered data organization are provided under **CDLA-Sharing-1.0**.

- The unmodified agreement is in [`LICENSE`](LICENSE).
- The covered repository layer is defined in [`LICENSE_SCOPE.md`](LICENSE_SCOPE.md).
- Published modified or extended covered data must remain under the unmodified CDLA-Sharing-1.0.
- Changed data files must be identified.
- Existing attribution and practical source links must be preserved.

The CDLA does not impose restrictions on qualifying computational Results, subject to the agreement's definitions and terms.

### Image layer

Images are not relicensed under CraftSight's CDLA grant:

- `mobs` images are subject to CC BY-NC 4.0;
- `basalt` images follow the terms stated by the upstream Zenodo record;
- `custom` images remain subject to Minecraft and other applicable rights.

See [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md) before redistribution or commercial use.

## Citation

```bibtex
@dataset{craftsight_2026,
  author    = {Shaga, Konstantin},
  title     = {CraftSight: A Multi-label Perception Dataset for Minecraft Agents},
  year      = {2026},
  publisher = {Hugging Face},
  version   = {1.0.1},
  url       = {https://huggingface.co/datasets/Krows7/CraftSight-Minecraft}
}
```

## Maintenance

Use the repository Discussions tab for annotation corrections, provenance updates, licensing questions, and schema proposals.