Update README.md

README.md

@@ -11,18 +11,19 @@ tags:
 - fiftyone
 - group
 - object-detection
-dataset_summary: '
+dataset_summary: >
 
 
 
 
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 111 samples.
+  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 111
+  samples.
 
 
   ## Installation
 
 
-  If you haven''t already, install FiftyOne:
+  If you haven't already, install FiftyOne:
 
 
   ```bash
@@ -44,7 +45,7 @@ dataset_summary: '
 
   # Load the dataset
 
-  # Note: other available arguments include ''max_samples'', etc
+  # Note: other available arguments include 'max_samples', etc
 
   dataset = load_from_hub("Voxel51/graspclutter6d")
 
@@ -54,8 +55,7 @@ dataset_summary: '
   session = fo.launch_app(dataset)
 
   ```
-
-  '
+license: cc-by-sa-4.0
 ---
 
 # Dataset Card for graspclutter6d
@@ -80,30 +80,45 @@ pip install -U fiftyone
 
 ```python
 import fiftyone as fo
-from fiftyone.utils.huggingface import load_from_hub
+from huggingface_hub import snapshot_download
+
+
+# Download the dataset snapshot to the current working directory
+
+snapshot_download(
+    repo_id="Voxel51/graspclutter6d", 
+    local_dir=".", 
+    repo_type="dataset"
+    )
+
+
+
+# Load dataset from current directory using FiftyOne's native format
+dataset = fo.Dataset.from_dir(
+    dataset_dir=".",  # Current directory contains the dataset files
+    dataset_type=fo.types.FiftyOneDataset,  # Specify FiftyOne dataset format
+    name="graspclutter6d"  # Assign a name to the dataset for identification
+)
 
-# Load the dataset
-# Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("Voxel51/graspclutter6d")
+# Launch the App
+session = fo.launch_app(dataset)
 
 # Launch the App
 session = fo.launch_app(dataset)
+
+
 ```
 
 
 ## Dataset Details
 
-### Dataset Description
+![image/png](graspclutter6d.gif)
 
 GraspClutter6D is a large-scale real-world dataset for robust perception and grasping in cluttered scenes. This FiftyOne dataset contains a **curated subset** of 99 scenes from the full GraspClutter6D dataset, optimized for visualization and exploration.
 
-**Full Dataset Statistics** (original):
-- 1,000 highly cluttered scenes with 14.1 objects/scene (average)
-- 62.6% occlusion rate (percentage of instances with visibility ≤ 0.95)
-- 200 unique objects captured in bins, shelves, and tables
-- 52K RGB-D images from 4 cameras (RealSense D415, D435, Azure Kinect, Zivid)
-- 736K 6D object poses with segmentation masks
-- 9.3 billion 6-DoF grasp annotations
+
+### Dataset Description
+
 
 **This Subset** (demo):
 - 99 scenes spanning the occlusion range (selected by visibility distribution)
@@ -113,8 +128,7 @@ GraspClutter6D is a large-scale real-world dataset for robust perception and gra
 
 - **Curated by:** Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee, Raeyoung Kang, Sangbeom Lee, Sangjun Noh, Youngjin Lee, Taeyeop Lee, and Kyoobin Lee
 - **Funded by :** Korea Institute of Machinery & Materials (KIMM), Gwangju Institute of Science and Technology (GIST), Korea Advanced Institute of Science and Technology (KAIST)
-- **Language(s) (NLP):** N/A (computer vision dataset)
-- **License:** 
+- **License:** Creative Commons Attribution Share Alike 4.0 
 
 ### Dataset Sources 
 
@@ -207,35 +221,6 @@ Each group has **5 slices**:
 - `rotation`: [0, 0, 0] (axis-aligned bounding boxes)
 - `obj_id`: Object type ID
 
-### Data Files
-
-**Per Scene:**
-- `rgb/NNNNNN.png`: RGB images (8-bit PNG)
-- `depth/NNNNNN.png`: Depth maps (16-bit PNG, values × depth_scale_mm = mm)
-- `mask_visib/NNNNNN_IIIIII.png`: Instance segmentation masks
-- `scene_camera.json`: Camera intrinsics and extrinsics
-- `scene_gt.json`: 6D object poses (rotation matrix + translation in mm)
-- `scene_gt_info.json`: Bounding boxes and visibility metrics
-
-**Object Models:**
-- `models_eval/obj_NNNNNN.ply`: 3D meshes for 200 objects
-
-**Grasp Labels** (hero scene only):
-- `grasp_label/obj_NNNNNN_labels.npz`: Dense grasp annotations
-  - `points`: (N, 3) contact points in object frame (meters)
-  - `offsets`: (N, 300, 12, 4, 3) approach directions
-  - `collision`: (N, 300, 12, 4) collision flags
-  - `scores`: (N, 300, 12, 4) grasp quality scores [0-1]
-
-### Subset Selection Criteria
-
-The 99 scenes were selected from 1,000 total scenes using a stratified sampling approach:
-1. Scenes ranked by mean visibility (occlusion severity)
-2. Divided into 3 buckets (high/medium/low occlusion)
-3. 33 scenes selected from each bucket, evenly spaced by scene ID
-4. Hero scene = most occluded scene with all 13 viewpoints retained
-5. Other scenes limited to viewpoint 0 (4 camera views) to reduce size
-
 ## Dataset Creation
 
 ### Curation Rationale
@@ -311,34 +296,6 @@ Annotations were produced through:
 - Simulation-based grasp generation (GraspNet framework)
 - Quality validation by research team
 
-#### Personal and Sensitive Information
-
-This dataset contains only images of inanimate objects (household items, tools, containers, etc.) arranged in controlled laboratory settings. No personal, sensitive, or private information is present.
-
-## Bias, Risks, and Limitations
-
-**Technical Limitations:**
-- **Subset size**: This demo contains only 99 of 1,000 scenes; not suitable for training large-scale models
-- **Grasp annotations**: Only available for hero scene (~14 objects); limited grasp diversity in subset
-- **Viewpoint coverage**: Most scenes have only 1 viewpoint (4 camera views); only hero scene has 13 viewpoints
-- **Object diversity**: 200 objects may not cover all real-world object categories
-- **Environment**: Captured in controlled laboratory settings; may not generalize to all real-world conditions
-- **Camera-specific**: Depth quality and characteristics vary significantly between cameras
-
-**Dataset Biases:**
-- Objects selected for robotic grasping research (household items, tools); not representative of all object types
-- Korean research lab environment and object selection
-- Scenes arranged intentionally for high occlusion; may not reflect natural object arrangements
-
-### Recommendations
-
-- **For training**: Use the full GraspClutter6D dataset from HuggingFace (1,000 scenes)
-- **For exploration**: This subset is ideal for visualization, prototyping, and understanding dataset structure
-- **Multi-camera fusion**: Leverage all 4 cameras for robust perception; single-camera methods may struggle
-- **Depth preprocessing**: Account for camera-specific depth_scale_mm factors (1.0 for RealSense, 0.1 for Kinect/Zivid)
-- **Occlusion handling**: Test algorithms on scenes across all occlusion levels (check mean_visibility field)
-- **Evaluation**: When benchmarking, report performance separately for different occlusion ranges
-
 ## Citation 
 
 **BibTeX:**
@@ -369,4 +326,4 @@ Original dataset by: Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geo
 
 ## Dataset Card Contact
 
-For questions about the full GraspClutter6D dataset: kyoobinlee@gist.ac.kr
+For questions about the full GraspClutter6D dataset: kyoobinlee@gist.ac.kr