Comments (3)
huanghoujing
commented on June 26, 2024
1
Hi, (Shared 1x1 Conv) means there is only one 1x1 Conv layer after pooling, shared between all parts/stripes (There are 6 parts, as an example). Independent 1x1 Conv means each part is processed by its 1x1 Conv layer, meaning different parts are processed by different model weights. The (Shared 1x1 Conv) implementation can only be found in early versions of code, not in current commit.
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abhishekaich27
commented on June 26, 2024
Hi @huanghoujing ! So now the implementation is for each stripe having it's own classifier as described in Figure 2 of the base paper?
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huanghoujing
commented on June 26, 2024
@abhishekaich27 Yes, you are right.
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Related Issues (20)
- Testing result on Market1501 HOT 5
- 有关dropout的问题 HOT 2
- 你好 我想问一下，我怎样才能用你的模型测试我的数据集呢？ HOT 1
- Error on inference HOT 4
- About the issue on training model by combined dataset HOT 2
- RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated HOT 3
- the performance of model trained on combined dataset HOT 1
- About open source license
- Missing Refined Part Pooling HOT 2
- about finetune and scratch HOT 1
- about the im_mean and im_std HOT 1
- Different CMC for model_weight_file HOT 1
- About the code execution process HOT 2
- About the input image size
- About the input image size HOT 4
- About loss function
- 验证集
- About the performance of model trained on market1501 HOT 2
- Preprocessing data with torchvision.transforms is much slower HOT 1
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