Comments (5)
- We use the cropped and resized (256x256) pedestrian images as inputs. related code
- It is 5533
- Yes, but the ID pretraining itself is much easier than the joint training process. As you can have ~256 different class samples in each minibatch, while in the joint training process, this number is typically less than 10.
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@Cysu Thanks a lot for your prompt reply!
I'll try it out in this way - please correct me if something wrong:
step1. pretrain: finetune IDNet (with plain softmax layer) from ImageNet pretrained VGG16 model, batchsize ~256;
step2. train: finetune IDNet (with RSS layer) from pretrained model in step1, batchsize ~256;
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Yes. You're right. Maybe the batch size could be tweaked. We used 128 in the original experiments.
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Thank you for sharing. I have some questions about person_search. Does the class of background is a must? Can this adding the background class improve the re-id accuracy?
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We hypothesize that having the background class can improve the performance, as the proposals will inevitably contain false positives. Will conduct some experiments to verify this. Thank you very much for the question.
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Related Issues (20)
- target_blobs.size() == source_layer.blobs_size() (1 vs. 0) Incompatible number of blobs for layer feat
- Rewrite batch question
- demo.py error HOT 2
- Error in demo --gpu 0
- about train model download
- About the log file of oim loss
- about dataset
- Caffe Installation Issue on GPU GTX 1050 Ubuntu 18.04 HOT 2
- CUHK-SYSU Person Search Dataset HOT 1
- Can I use standard caffe for inference only? HOT 1
- If you have problems when compiling, please see here
- About the Datase
- About the Dataset HOT 2
- Please help !!! problems running the demo HOT 1
- Implementation bug about unlabeled_matching_layer?
- A good pytorch implementation is available now.
- cuda 8.0 and cudnn v5.1
- 折线图 HOT 1
- how to get cuhk02 03 and sysu
- CMake error
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