Comments (3)
To further fine-tune a COCO model on another dataset (e.g., Cityscapes as we did), you need to remove the layers that are class-dependent (in Mask R-CNN, in bbox cls, bbox reg, and mask pred layers), and reinitialize them.
In Detectron, the weights are loaded by matching names, so a simple way to do this is either to change the name of the class-dependent layers when you do further fine-tuning, or add a flag in the weight loading to not load these weights.
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Proposal, Box, and Mask Detection Baselines
These models are all trained on coco.
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yes, but my dataset has class number different from coco dataset so your mentioned model cannot be directly loaded into the training scripts. I don't know how caffe2 handles model importing with different layer sizes. I know however in caffe I can just modify the layer name.
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Related Issues (20)
- the speed is slow
- 关于bbox损失函数部分,其中mask rcnn的λ权重平衡参数在哪啊 配置和代码里没看见啊。。。 HOT 1
- Detectron2 In-Place One-to-Many Augmentations HOT 2
- KeyError: u'Key TEST.SCALES was renamed to TEST.SCALE; please update your config. Note: Also convert from a tuple, e.g. (600, ), to a integer, e.g. 600.'
- libcaffe2_detectron_ops_gpu.so运行慢
- n/a
- How can I disable the logging system? HOT 1
- How to train Faster R-CNN on my own custom dataset? HOT 4
- Is there any script for batch inference?
- Detectron or Detection? HOT 1
- Project dependencies may have API risk issues
- Caffe 2 merged with pytorch new installation instruction?
- Convert Cityscapes to COCO format: How to convert to other classes (ex: traffic light) HOT 1
- detectron implantación inparcial,solicitud de inplantacion HOT 2
- Mask-RCNN model not properly generating segmentation masks for a specific class- custom dataset
- App etiquette HOT 2
- problem with adaptive streaming HOT 3
- why is threshold of detector confidence is 0.05, not 0.5? Helps, bros
- Really?
- Precision and recall (not AP not AR) values per class
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