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detectron2
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
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Thank you, I will have a look at it. I'm not that knowledgeable about existing models. Detectron2 also provides different different backbones (https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md). Is there a reason you recommend the segmentation-models library (apologies for the naive question)?
My question therefore is, how should I deal with drone imagery and small objects for instance segmentation tasks? What am I doing wrong and/or what should I be doing? Should I for example consider using an extra public dataset for bird/drone imagery segmentation first, before I fine-tune on my dataset? I am happy to provide more details if necessary. If useful, I uploaded my code here: https://github.com/augusts-bit/cv-animal-segmentation.
Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models
Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models
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