Comments (4)
Yeah, of course, that would work. That is domain adaptation. You need source domain labels, you need target domain data (unlabelled) and then you adapt your model from source domain to target domain.
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Hey,
How would you train object detector if you don't have labels for Source domain? I didn't get your question clearly, could please elaborate?
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Hi,
I have labels in source domain, but not in target domain i.e. pascal dataset (source domain) has labels and clipart dataset(target domain) don't have labels, then in this case "Will this implementation work and Can I train my model using SCL code"??
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hey harsh-99,
I want to do object detection on clipart(target domain) dataset and i am training my model using pascal dataset as (source domain) and clipart dataset as (target domain) both have annotation.xml, but what if clipart dataset(target domain) don't have annotation.xml, how to do object detection on clipart sample that don't have annotation.xml, i tried to remove/comment annotation.xml code but i am getting error, how to solve or approach to this issue,Thanks in advance.
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Related Issues (20)
- Experimental details HOT 1
- visualization of features from PASCAL to Clipart HOT 1
- Applying SCL on SSD HOT 1
- How to do Object detection on target domain(ex: Clipart) that don't have annotation.xml label HOT 2
- Can I run this Solution in real time or without the need to train the target data HOT 3
- About cityscape and foggy dataset HOT 3
- Can i generate or train model using this method and run it on different dataset/images that is similar to target domain data but this new dataset don't exist in target domain data when it was trained
- Run trainval_net_SCL.py with custom dataset raises RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. HOT 4
- How can I add custom new class labels, lets say-x classes to a SCL trained model( which is already trained on y classes). So I do not replace those y classes and in total I have x+y class labels. HOT 1
- About sim10k dataset HOT 2
- Your implementation of domain adaptive faster rcnn performs better than paper values, what might be the reason? HOT 1
- About the trained model. HOT 1
- after filtering, there are 0 images HOT 1
- question of t-sne
- Heat Map
- About cityscape-->foggy cityscape test set
- Using multilple GPUs to accomplish distributed training HOT 1
- ImportError: No module named cython_bbox HOT 1
- File "/home/lty/anaconda3/envs/SWDA/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 301, in forward self.padding, self.dilation, self.groups) RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/THC/THCBlas.cu:249
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