Datasets:
image
imagewidth (px) 1.43k
1.43k
| label
stringlengths 0
162
|
---|---|
0 0.3711484593837535 0.28308823529411764 0.14285714285714285 0.08508403361344538
1 0.47549019607843135 0.5540966386554622 0.05042016806722689 0.05042016806722689 |
|
1 0.7314425770308123 0.7531512605042017 0.27521008403361347 0.22373949579831934 |
|
0 0.3802521008403361 0.3316701680672269 0.2689075630252101 0.266281512605042 |
|
0 0.6355042016806722 0.5236344537815126 0.09873949579831932 0.07878151260504201 |
|
1 0.5997899159663865 0.41544117647058826 0.08893557422969188 0.04936974789915966 |
|
0 0.37289915966386555 0.6509978991596639 0.5707282913165266 0.47741596638655465 |
|
1 0.4061624649859944 0.8815651260504201 0.32072829131652664 0.23686974789915966 |
|
0 0.4362745098039216 0.028361344537815126 0.03081232492997199 0.05672268907563025
1 0.47128851540616246 0.44432773109243695 0.12044817927170869 0.0861344537815126 |
|
0 0.6011904761904762 0.6488970588235294 0.36764705882352944 0.20850840336134455 |
|
0 0.594187675070028 0.6202731092436975 0.16876750700280113 0.11449579831932773
1 0.021358543417366947 0.5777310924369747 0.042717086834733894 0.04936974789915966 |
|
1 0.6186974789915967 0.7725840336134454 0.41386554621848737 0.29936974789915966 |
|
1 0.5021008403361344 0.6636029411764706 0.08823529411764706 0.09296218487394958 |
|
1 0.6992296918767507 0.7045693277310925 0.31442577030812324 0.2421218487394958 |
|
1 0.5507703081232493 0.46559873949579833 0.04831932773109244 0.04569327731092437 |
|
1 0.4642857142857143 0.6158088235294118 0.07983193277310924 0.08140756302521009 |
|
0 0.757703081232493 0.11528361344537816 0.0350140056022409 0.013130252100840336
1 0.4215686274509804 0.5091911764705882 0.0742296918767507 0.07615546218487394 |
|
0 0.553921568627451 0.6058298319327731 0.32212885154061627 0.25157563025210083 |
DATASET SAMPLE
Duality.ai just released a 1000 image dataset used to train a YOLOv8 model in multiclass object detection -- and it's 100% free!
Just create an EDU account here.
This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by creating a FalconCloud account. Once you verify your email, the link will redirect you to the dataset page.
What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?
- The digital twins are not generated by AI, but instead crafted by 3D artists to be INDISTINGUISHABLE to the model from the physical-world objects. This allows the training from this data to transfer into real-world applicability
- The simulation software, called FalconEditor, can easily create thousands of images with varying lighting, posing, occlusions, backgrounds, camera positions, and more. This enables robust model training.
- The labels are created along with the data. This not only saves large amounts of time, but also ensures the labels are incredibly accurate and reliable.
Dataset Structure
The dataset has the following structure:
Multiclass Object Detection Dataset/
|-- images/
| |-- 000000000.png
| |-- 000000001.png
| |-- ...
|-- labels/
| |-- 000000000.txt
| |-- 000000001.txt
| |-- ...
Components
- Images: RGB images of the object in
.png
format. - Labels: Text files (
.txt
) containing bounding box annotations for each class- 0 = cheerios
- 1 = soup
Licensing
license: apache-2.0
- Downloads last month
- 52