Comments (2)
That may be part due to historical problem. When we reproduce FPN for detection, we find BN can stablize the training process in case of NaN loss, thus we inherit from that. In fact that incurs redundant overhead of computation if now it trains well. BN is applied in original bottleneck blocks in resnet. As far as I know, it can improve the performance.
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Thanks for your quick response.
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Related Issues (20)
- Has anyone tested on the MPII dataset?
- Strange results HOT 1
- About detect single person HOT 16
- About code running environment
- How do I test with pictures?
- bbox is necessary in Test? HOT 2
- An error occurred after I executed: `python3 mptest.py -d 0-1 -m log/model_dump/snapshot_350.ckpt`
- global_loss += tf.reduce_mean(tf.square(global_out - global_label)) / len(labels); global_loss /= 2;.so damn hard to understand
- why? gk15 = (23, 23) gk11 = (17, 17) gk9 = (13, 13) gk7 = (9, 9)
- strange results
- > @dagongji10 Excuse me, can I ask you a question? Must I provide bbox of pictures when I test a picture?Could you please give me a script which can visualize test results like what you show? Thank you very much HOT 3
- coco dataset? which year?
- train train
- "read none image" during training? HOT 1
- from nets.basemodel import resnet50, resnet_arg_scope, resnet_v1
- labels = [label15, label11, label9, label7]
- How to apply inference on own dataset without GT_bbox(.json)?
- How to decrease Batch size HOT 4
- Using just GlobalNet
- is it possible to add new joint in the skel and retrain the model
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