tomascanivari/mask2former-swin-large-coco-instance-finetuned-buildings
Image Segmentation • 0.2B • Updated • 286
Datasets:
image_info dict | annotations listlengths 1 202 | PIL_image imagewidth (px) 512 512 | PIL_annotation imagewidth (px) 512 512 |
|---|---|---|---|
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... |
End of preview. Expand in Data Studio
Building Extraction Dataset
This dataset is a processed verison of the dataset of the Kaggle competition: https://www.kaggle.com/competitions/building-extraction-generalization-2024/. The original train and validation images and COCO annotations were resized to (512, 512). Then from the segmentations on the COCO annotations, a PIL_annotation file was created for each sample in which the Red channel corresponds to the semantic segmentation mask and the Green channel to the instance segmentation mask. A PIL_image file is also delivered in the dataset for each sample, so it can be downloaded directly without the need of local files.
Note: in the test split, the 'annotations' and 'PIL_annotations' columns are placeholders.
Dataset Structure
- Train: 3,784 images
- Validation: 933 images
- Test: 1000 images (no annotations)
Features
Each split includes:
image_info: original coco metadata with filename, width, and heightannotations: original coco annotations formatPIL_image: imagePIL_annotation: masks
- Downloads last month
- 11
Size of downloaded dataset files:
1.39 GB
Size of the auto-converted Parquet files:
1.39 GB
Number of rows:
5,717
Models trained or fine-tuned on tomascanivari/buildings-extraction-coco-hf