…"Segmentation in the Wild (SegInW)" Challenge is a part of X-Decoder, that proposed a new benchmark to evaluate the transfer ability of pre-trained vision models. This benchmark presents a diverse set of downstream segmentation datasets, measuring the ability of pre-training models on both the segmentation accuracy and their transfer efficiency in a new task, in This SegInW Challenge consists of 25 free public Segmentation datasets, crowd-sourced on roboflow.com. For more details about the challenge submission format, please refer to X-Decoder for SGinW.
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MatSeg Dataset for Zero-Shot Material States Segmentation: The dataset contains large-scale synthetic images for training data and highly diverse real-world image benchmarks for testing. Focusing on zero-shot class-agnostic segmentation of materials and their states. This means finding the region of materials states without pre-training on the specific material classes or states. It contains both hard segmentation maps and soft and partial similarity annotations for similar but not identical materials.
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