| --- |
| pretty_name: Label Factory Multi-Task RGB-D Collection |
| license: mit |
| tags: |
| - image |
| - video |
| - 3d |
| - rgb-d |
| configs: |
| - config_name: depth_estimation |
| data_files: |
| - split: train |
| path: "*/data/depth_estimation/train-*.parquet" |
| - split: validation |
| path: "*/data/depth_estimation/validation-*.parquet" |
| - split: test |
| path: "*/data/depth_estimation/test-*.parquet" |
| - config_name: instance_segmentation |
| data_files: |
| - split: train |
| path: "*/data/instance_segmentation/train-*.parquet" |
| - split: validation |
| path: "*/data/instance_segmentation/validation-*.parquet" |
| - split: test |
| path: "*/data/instance_segmentation/test-*.parquet" |
| - config_name: object_pose_estimation |
| data_files: |
| - split: train |
| path: "*/data/object_pose_estimation/train-*.parquet" |
| - split: validation |
| path: "*/data/object_pose_estimation/validation-*.parquet" |
| - split: test |
| path: "*/data/object_pose_estimation/test-*.parquet" |
| - config_name: object_pose_models |
| data_files: |
| - split: train |
| path: "*/data/object_pose_estimation/models.parquet" |
| --- |
| |
| # KarolyArmin/Hand_tools |
| |
| Growing multi-task RGB-D collection generated with the Label Factory workflow. |
| Every capture is stored in its own zero-padded directory (`0000`, `0001`, |
| `0002`, ...). Uploading a new capture appends it to the collection without |
| replacing earlier directories. Task configurations aggregate compatible |
| Parquet shards from every numbered directory. |
| |
| ## Training configurations |
| |
| - `depth_estimation`: RGB input, metric depth target, intrinsics and depth units |
| - `instance_segmentation`: RGB input, instance-mask target, boxes and annotations |
| - `object_pose_estimation`: RGB-D input, masks, camera transforms and object poses |
| - `object_pose_models`: one STL payload per object type and numbered dataset |
|
|
| Every configuration provides deterministic `train`, `validation`, and `test` |
| splits. Adding another numbered capture appends rows through the wildcard paths; |
| it does not replace earlier datasets. |
|
|
| ## Loading all or several numbered datasets |
|
|
| The configuration automatically combines every uploaded directory (`0000`, |
| `0001`, `0002`, ...). Filter `dataset_id` to use selected captures: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| poses = load_dataset("KarolyArmin/Hand_tools", "object_pose_estimation") |
| selected_ids = set(["0000", "0001", "0004"]) |
| for split_name in poses: |
| poses[split_name] = poses[split_name].filter( |
| lambda row: row["dataset_id"] in selected_ids |
| ) |
| ``` |
|
|
| ## Depth-estimation example |
|
|
| ```python |
| from datasets import load_dataset |
| import numpy as np |
| |
| depth_dataset = load_dataset("KarolyArmin/Hand_tools", "depth_estimation") |
| sample = depth_dataset["train"][0] |
| rgb = sample["image"].convert("RGB") |
| depth_native = np.asarray(sample["depth"]) |
| depth_mm = depth_native.astype(np.float32) * sample["depth_scale"] |
| depth_m = depth_mm / 1000.0 |
| intrinsics_3x3 = np.asarray(sample["camera_intrinsics"]).reshape(3, 3) |
| ``` |
|
|
| ## Instance-segmentation example |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| import numpy as np |
| |
| instances = load_dataset("KarolyArmin/Hand_tools", "instance_segmentation") |
| sample = instances["validation"][0] |
| rgb = sample["image"].convert("RGB") |
| instance_mask = np.asarray(sample["instance_mask"]) |
| annotations = json.loads(sample["annotations_json"]) |
| boxes_xywh = np.asarray(sample["boxes_xywh"]) |
| instance_names = sample["instance_names"] |
| ``` |
|
|
| ## Object-pose-estimation example |
|
|
| ```python |
| from datasets import load_dataset |
| import numpy as np |
| |
| poses = load_dataset("KarolyArmin/Hand_tools", "object_pose_estimation") |
| sample = poses["test"][0] |
| rgb = sample["image"].convert("RGB") |
| depth = np.asarray(sample["depth"]) |
| instance_mask = np.asarray(sample["instance_mask"]) |
| world_from_camera = np.asarray(sample["world_from_camera"]).reshape(4, 4) |
| camera_from_world = np.asarray(sample["camera_from_world"]).reshape(4, 4) |
| world_from_objects = np.asarray(sample["world_from_objects"]).reshape(-1, 4, 4) |
| camera_from_objects = np.asarray(sample["camera_from_objects"]).reshape(-1, 4, 4) |
| object_names = sample["object_names"] |
| |
| # Resolve the STL models referenced by this pose sample. |
| models = load_dataset("KarolyArmin/Hand_tools", "object_pose_models")["train"] |
| required_keys = set(sample["object_model_keys"]) |
| models = models.filter(lambda row: row["object_model_key"] in required_keys) |
| first_model = models[0] |
| with open(first_model["stl_file_name"], "wb") as file: |
| file.write(first_model["stl"]) |
| ``` |
|
|
| ## Collection contents |
|
|
| Each numbered directory contributes: |
|
|
| - RGB images and aligned 16-bit metric depth maps |
| - camera intrinsics and metric world-coordinate camera poses |
| - instance masks, boxes, RLE annotations and visualization overlays |
| - per-frame and consolidated object poses |
| - metric scene reconstruction and object STL models |
| - task-specific train, validation and test Parquet shards |
|
|
| ## Latest generated contribution |
|
|
| - Directory: `0002/` |
| - RGB-D frames: 147 |
| - Annotated object instances: 4 |
| - Selected upload files: 922 |
|
|
| Depth images are stored in millimetres. Pose conventions and coordinate-system |
| metadata are recorded in `0002/sam6d_scene/annotations.json` and |
| `0002/reconstruction/metric_alignment/camera_poses_metric.json`. |
|
|
| ## Limitations |
|
|
| Annotations are automatically generated and must be independently reviewed for |
| the intended downstream use. Reconstruction, depth-alignment, segmentation and |
| pose-estimation errors may remain. |
|
|
