segmentation
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Similar to #723 , add tests for all possible combinations so they don't break in the future.
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这个issue主要讲一下,如何把你自己的模型添加到lite.ai.toolkit。lite.ai.toolkit集成了一些比较新的基础模型,比如人脸检测、人脸识别、抠图、人脸属性分析、图像分类、人脸关键点识别、图像着色、目标检测等等,可以直接用到具体的场景中。但是,毕竟lite.ai.toolkit的模型还是有限的,具体的场景下,可能有你经过优化的模型,比如你自己训了一个目标检测器,可能效果更好。那么,如何把你的模型加入到lite.ai.toolkit中呢?这样既能用到lite.ai.toolkit一些已有的算法能力,也能兼容您的具体场景。这个issue主要是讲这个问题。大家有疑惑的可以提在这个issue,我会尽可能回答~
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Enhancement
A discussion in #614 revealed a good place for improvement - we should ensure that input image is continuous upon start of the augmentation pipeline. This could be implemented by adding
image = np.ascontiguousarray(image)
to image and mask targets.A proposed place to add this call - somewhere at the beginning of
A.Compose.__call__
.