--- 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 Version

**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_` 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//.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.
Show all 48 labels ```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 ```
### `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.