Datasets:
text stringlengths 37 667 |
|---|
0 0.559570 0.462402 0.304688 0.381836 |
0 0.515625 0.404785 0.218750 0.469727 |
0 0.468262 0.413086 0.280273 0.390625 |
0 0.541504 0.377930 0.286133 0.484375 |
0 0.515137 0.444824 0.461914 0.489258 |
0 0.518555 0.271484 0.476562 0.290039 0.454102 0.307617 0.431641 0.334961 0.410156 0.388672 0.408203 0.438477 0.428711 0.530273 0.431641 0.564453 0.450195 0.606445 0.474609 0.628906 0.500977 0.643555 0.533203 0.651367 0.566406 0.652344 0.606445 0.633789 0.640625 0.595703 0.701172 0.473633 0.709961 0.437500 0.709961 0.3... |
1 0.566406 0.258789 0.539062 0.263672 0.526367 0.270508 0.525391 0.269531 0.530273 0.274414 0.508789 0.290039 0.493164 0.311523 0.487305 0.327148 0.484375 0.347656 0.487305 0.388672 0.490234 0.398438 0.502930 0.420898 0.526367 0.439453 0.546875 0.447266 0.559570 0.449219 0.572266 0.449219 0.591797 0.445312 0.611328 0.4... |
0 0.575195 0.170898 0.567383 0.187500 0.554688 0.199219 0.532227 0.208008 0.506836 0.208984 0.476562 0.198242 0.464844 0.187500 0.456055 0.169922 0.425781 0.197266 0.408203 0.238281 0.407227 0.292969 0.422852 0.496094 0.421875 0.550781 0.433594 0.579102 0.463867 0.613281 0.471680 0.628906 0.495117 0.638672 0.514648 0.6... |
1 0.497070 0.083008 0.477539 0.090820 0.470703 0.095703 0.459961 0.108398 0.457031 0.117188 0.458008 0.118164 0.457031 0.120117 0.458008 0.167969 0.460938 0.170898 0.462891 0.178711 0.467773 0.187500 0.473633 0.193359 0.484375 0.200195 0.498047 0.205078 0.510742 0.207031 0.531250 0.206055 0.543945 0.202148 0.560547 0.1... |
0 0.374023 0.234375 0.328125 0.291992 0.352539 0.541016 0.361328 0.549805 0.484375 0.600586 0.512695 0.607422 0.533203 0.584961 0.592773 0.489258 0.606445 0.238281 0.598633 0.229492 0.565430 0.217773 0.566406 0.256836 0.551758 0.284180 0.528320 0.300781 0.507812 0.308594 0.456055 0.313477 0.420898 0.305664 0.393555 0.2... |
1 0.391602 0.173828 0.381836 0.184570 0.374023 0.202148 0.374023 0.227539 0.375977 0.234375 0.374023 0.239258 0.374023 0.253906 0.375977 0.263672 0.383789 0.278320 0.402344 0.294922 0.419922 0.303711 0.439453 0.309570 0.463867 0.312500 0.485352 0.311523 0.505859 0.307617 0.527344 0.299805 0.541992 0.291016 0.556641 0.2... |
0 0.406250 0.140625 0.398438 0.153320 0.408203 0.477539 0.417969 0.544922 0.433594 0.609375 0.444336 0.619141 0.467773 0.619141 0.651367 0.598633 0.661133 0.581055 0.681641 0.292969 0.683594 0.224609 0.649414 0.159180 0.647461 0.173828 0.634766 0.189453 0.595703 0.207031 0.539062 0.213867 0.477539 0.207031 0.443359 0.1... |
1 0.424805 0.100586 0.423828 0.132812 0.425781 0.136719 0.425781 0.146484 0.422852 0.152344 0.424805 0.173828 0.439453 0.189453 0.472656 0.204102 0.495117 0.208984 0.523438 0.211914 0.553711 0.211914 0.584961 0.208008 0.623047 0.195312 0.633789 0.188477 0.642578 0.179688 0.646484 0.171875 0.648438 0.157227 0.645508 0.1... |
0 0.313477 0.237305 0.291016 0.255859 0.284180 0.278320 0.337891 0.450195 0.366211 0.608398 0.382812 0.645508 0.425781 0.673828 0.491211 0.688477 0.560547 0.685547 0.616211 0.666992 0.653320 0.630859 0.680664 0.480469 0.745117 0.278320 0.738281 0.256836 0.713867 0.237305 0.670898 0.219727 0.620117 0.208008 0.543945 0.2... |
Synthetic Transparent Glass & Packaging Dataset
A photorealistic synthetic computer vision dataset for transparent glass and packaging object detection and instance segmentation. The dataset is designed for models dealing with challenging transparent and reflective materials, including transparency, reflections, refractions, specular highlights, and harsh lighting conditions.
Dataset at a Glance
| Property | Details |
|---|---|
| Dataset type | Synthetic computer vision dataset |
| Objects | Transparent glass and packaging |
| Images | 240 |
| Resolution | 1024 × 1024 |
| Training split | 200 images |
| Validation split | 40 images |
| Detection annotations | YOLO bounding boxes |
| Segmentation annotations | YOLO instance segmentation polygons |
| Image format | JPEG |
| Annotation format | YOLO .txt |
| Ground truth | Automatically generated from 3D scene data |
| Primary tasks | Object detection and instance segmentation |
| Material challenges | Transparency, reflections, refractions, specular highlights |
This repository contains a 5-image sample dataset with dual ground-truth annotations so you can test pipeline compatibility instantly.
🛒 Download the Full Production Dataset (240 Images)
To fine-tune or evaluate your models on the complete dataset, access the full pack on Gumroad:
👉 Download Full Dataset on Gumroad ($49)
Full Dataset Specifications
240 Renders @ 1024x1024: 200 Train / 40 Val native resolution split.
Dual Ground-Truth Annotations: Includes both 2D Bounding Boxes (
labels_box/) and Multi-Point Instance Polygons (labels_segment/).Automatically Generated Ground Truth: Bounding boxes and segmentation polygons are generated directly from the underlying 3D scene, eliminating manual annotation work.
Commercial Single-Team License: Royalty-free commercial usage rights to train, evaluate, and deploy models.
indoor-pack : https://huggingface.co/datasets/Ji0134ch/synthetic-transparent-objects-indoor
Repository Structure
.
├── data/
│ ├── images/ # Sample 1024x1024 JPEG renders
│ ├── labels_box/ # YOLO format bounding box annotations (.txt)
│ └── labels_segment/ # YOLO format instance segmentation polygons (.txt)
├── src/
│ └──visualize_sample.py # Script to draw bounding box and polygon overlays
└── README.md
Applications
This dataset is designed for computer vision tasks involving transparent and reflective objects, including:
- Transparent glass object detection
- Transparent packaging detection
- YOLO object detection
- Instance segmentation
- Synthetic-to-real computer vision experiments
- Robotics and 3D vision research
- Downloads last month
- 84