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@@ -8,30 +8,36 @@ dataset_info:
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  splits:
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  - name: train
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  num_bytes: 14474596.43478261
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- num_examples: 18
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- - name: test
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- num_bytes: 3800851.565217391
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- num_examples: 5
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  download_size: 18278418
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  dataset_size: 18275448
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  configs:
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  - config_name: default
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  data_files:
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- - split: train
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- path: data/train-*
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- - split: test
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- path: data/test-*
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  license: apache-2.0
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  language:
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  - en
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  ---
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  # DATASET SAMPLE
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- These 20 images and labels are carefully crafted data from FalconEditor, Duality.ai’s simulation platform.
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- We will be releasing a sample of our datasets here on HuggingFace, and making the rest of the 1000+ image datasets available to anyone who signs up for a free account -
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- ## Dataset Structure
 
 
 
 
 
 
 
 
 
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  The dataset has the following structure:
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  ```plaintext
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  Multiclass Object Detection Dataset/
 
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  splits:
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  - name: train
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  num_bytes: 14474596.43478261
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+ num_examples: 20
 
 
 
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  download_size: 18278418
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  dataset_size: 18275448
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  configs:
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  - config_name: default
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  data_files:
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+ - split: images
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+ path: data/images-*
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+ - split: labels
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+ path: data/labels-*
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  license: apache-2.0
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  language:
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  - en
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  ---
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  # DATASET SAMPLE
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+ [Duality.ai ](https://www.duality.ai/edu) just released a 1000 image dataset used to train a YOLOv8 model in object detection -- and it's 100% free!
 
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+ Just [create an EDU account here](https://falcon.duality.ai/auth/sign-up).
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+ This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by [creating a FalconCloud account](https://falcon.duality.ai/auth/sign-up). Once you verify your email, the link will redirect you to the dataset page.
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+
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+ What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?
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+ - The digital twins are not generated by AI, but instead crafted by 3D artists to be INDISTINGUISABLE to the model from the physical-world objects. This allows the training from this data to transfer into real-world applicability
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+ - 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.
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+ - 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.
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+
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c8dc99951843ca6762fe02/08ehub3yPYozSzxNFtVIx.png)
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+ # Dataset Structure
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  The dataset has the following structure:
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  ```plaintext
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  Multiclass Object Detection Dataset/