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README.md

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----
-license: mit
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+---
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+tags:
+- open-vocabulary-segmentation
+- zero-shot-segmentation
+---
+
+## Dataset Card for Segmentation in the Wild
+### Dataset Description
+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.
+
+### Composition
+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.
+
+### Data Instances
+
+- Images: Visual data in the form of images, depending on the dataset.
+- Annotations: Manual annotations specifying regions of interest or providing referring phrases for language-based segmentation.
+- Segmentation Masks: Pixel-level annotations that define the boundaries of objects or regions in the visual data.
+- Metadata: Additional information about the data, such as collection sources, dates, and any relevant pre-processing steps.
+
+**Data Splits** 
+Each folder has a train, train 10-shot and validation splits.
+
+**Dataset Creation**
+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.