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diff --git a/0009/sam6d_sequences/Screwdriver/Screwdriver_world_pose.json b/0009/sam6d_sequences/Screwdriver/Screwdriver_world_pose.json
new file mode 100644
index 0000000000000000000000000000000000000000..362d6192ff3a5626e34c2ae4ed81487faf0e54e2
--- /dev/null
+++ b/0009/sam6d_sequences/Screwdriver/Screwdriver_world_pose.json
@@ -0,0 +1,890 @@
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+      "silhouette_iou_median": 0.8615865762651251,
+      "silhouette_iou_frames": 40
+    }
+  ],
+  "R": [
+    [
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+      0.22574262819990037,
+      0.5734004411066437
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+      0.9608279820350378,
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+      0.16077890020732893,
+      0.775414094789952
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+  ],
+  "quaternion_xyzw": [
+    0.11326831134888453,
+    0.3153779355776115,
+    -0.08219670690881212,
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+    0.11787405636985672,
+    -0.11662338966588306,
+    0.27930669726248436
+  ],
+  "world_from_object": [
+    [
+      0.7875609182483014,
+      0.22574262819990037,
+      0.5734004411066437,
+      0.11787405636985672
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+      0.27930669726248436
+    ],
+    [
+      0.0,
+      0.0,
+      0.0,
+      1.0
+    ]
+  ],
+  "ransac": {
+    "estimation_mode": "position_then_orientation",
+    "translation_threshold_m": 0.01,
+    "rotation_threshold_deg": 5.0,
+    "maximum_rotation_threshold_deg": 30.0,
+    "minimum_score": 0.0,
+    "symmetry_mode": "auto",
+    "symmetry_tolerance_fraction": 0.01,
+    "number_of_rotational_symmetries": 4,
+    "maximum_instances": 0,
+    "minimum_instance_frame_fraction": 0.05,
+    "low_support_fallback_used": false,
+    "min_silhouette_iou": 0.0,
+    "automatic_support_threshold_frames": 5,
+    "position_suppression_threshold_m": 0.02,
+    "number_of_candidate_frames": 45,
+    "number_of_candidate_observations": 47,
+    "number_of_raw_pose_clusters": 1,
+    "number_of_instances": 1,
+    "remaining_unclustered_observations": 3,
+    "rejected_pose_clusters": []
+  },
+  "skipped_frames": [],
+  "sources": {
+    "config_path": "/workspace/scripts/configs/default.yaml",
+    "object_poses": "/workspace/data/0009/experimental_multi_instance/pem/Screwdriver/Screwdriver_poses_per_frame.json",
+    "colmap_reconstruction": "/workspace/data/0009/reconstruction/sfm_filtered",
+    "sim3_alignment": "/workspace/data/0009/reconstruction/metric_alignment"
+  }
+}
diff --git a/0009/sam6d_sequences/ransac_run_summary.json b/0009/sam6d_sequences/ransac_run_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..31ff9d8aa2aedabf120793c6f86faa3e68a5d72b
--- /dev/null
+++ b/0009/sam6d_sequences/ransac_run_summary.json
@@ -0,0 +1,21 @@
+{
+  "format": "label_factory_multi_instance_pose_fitting_summary_v1",
+  "objects_requested": [
+    "Pliers",
+    "Rachet",
+    "Screwdriver"
+  ],
+  "found_objects": {
+    "Pliers": 1,
+    "Rachet": 1,
+    "Screwdriver": 1
+  },
+  "non_found_objects": [],
+  "objects_succeeded": [
+    "Pliers",
+    "Rachet",
+    "Screwdriver"
+  ],
+  "low_support_objects": [],
+  "objects_excluded": []
+}
diff --git a/README.md b/README.md
index 32897cd3e640101ba184f8c4ccd896981de3804a..63fc3eb4b70c753394cfb3f887295b780e07cd23 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,158 @@
 ---
+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"
 ---
+
+# ABC-iRobotics/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("ABC-iRobotics/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("ABC-iRobotics/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("ABC-iRobotics/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("ABC-iRobotics/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("ABC-iRobotics/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: `0009/`
+- RGB-D frames: 250
+- Annotated object instances: 3
+- Selected upload files: 1535
+
+Depth images are stored in millimetres. Pose conventions and coordinate-system
+metadata are recorded in `0009/sam6d_scene/annotations.json` and
+`0009/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.