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license: cc-by-4.0 |
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task_categories: |
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- image-segmentation |
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tags: |
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- open-vocabulary-segmentation |
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- zero-shot-segmentation |
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--- |
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## Dataset Card for Segmentation in the Wild |
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### Dataset Description |
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Segmentation in the Wild (SegInW) is a computer vision challenge that aims to evaluate the transferability of pre-trained vision models. It proposes a new benchmark that assesses both the segmentation accuracy and transfer efficiency of models on a diverse set of downstream segmentation tasks. The challenge consists of 25 free, public segmentation datasets, crowd-sourced on roboflow.com, providing a wide range of visual data for model training and testing. |
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### Composition |
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The SegInW challenge brings together 25 diverse segmentation datasets, offering a comprehensive evaluation of model performance across various scenarios. These datasets cover a broad range of visual content. |
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### Data Instances |
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- Images: Visual data in the form of images, depending on the dataset. |
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- Annotations: Manual annotations specifying regions of interest or providing referring phrases for language-based segmentation. |
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- Segmentation Masks: Pixel-level annotations that define the boundaries of objects or regions in the visual data. |
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- Metadata: Additional information about the data, such as collection sources, dates, and any relevant pre-processing steps. |
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**Data Splits** |
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Each folder has a train, train 10-shot and validation splits. |
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**Dataset Creation** |
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The SegInW challenge is a community effort, with the 25 datasets crowd-sourced and contributed by different researchers and organizations. The diversity of sources ensures a wide range of visual data and evaluation scenarios. The datasets were labeled on roboflow.com as part of [X-Decoder](https://x-decoder-vl.github.io/) project. |
