Comments (1)
lwpyr
commented on July 17, 2024
I think it is not a good idea to use this box free strategy in complex object detection. This scale regression strategy works in simple object detection tasks like pedestrians and faces.
If you insist on training this on VOC, you need to change the class prediction heat map's channel from 1 to N_class. Also you can specify the height and width regression for each category. And softmax cross entropy will replace the BCEloss in loss function.
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Related Issues (20)
- The losses value from train_Caltech are higher than authors
- I have data and need to test my data with this pre-trained model
- An exception occurs when running train_caltech
- How to generate heat map
- No module named 'nms_wrapper' HOT 1
- windows user how to test caltech? HOT 2
- Is nms postprocess necessary?
- How to run test_wider_ms.py with multiple GPUs
- Python version of dt_txt2json HOT 2
- How to generate the cache file of test subset in wider face?
- Share the code of how to execute the multi-scale training for the citypersons dataset?
- Classification loss - binary_crossentropy (possibly wrong parameters order) HOT 1
- batchsize 16 on one 2080ti cause OOM exception
- it shows me that Dimensions is not equal HOT 2
- where val dataset? i just see the train
- how to run this github
- Does anyone reproduce the caltech result using author's weight?
- how can I get the image with the bounding boxes like this HOT 2
- why the classification is a binary classification? in the cityperson dataset , it have 6 class pedestrian ?
- ImportError: Building module keras_csp.nms.gpu_nms failed: ["distutils.errors.CompileError: command 'gcc' failed with exit status 1\n"]
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