Comments (4)
Hey dude, thanks for your interest in FeatherCNN. The two frameworks take different technical paths under the hood, so it's hard to explain in a nut shell. From a user's perspective, both frameworks are aiming at delivering high performance for CNN inference computation. FeatherCNN is very recently released so it may have some problems. It also lacks several layer support for some specific neural networks. Currently ncnn is the more stable project after over a years' development. Performance benchmarks will be provided in the wiki later on, but only for FeatherCNN. As the usage of the two frameworks are very similar, I suggest you to try out both frameworks for your own neural network models, and then pick the one you like.
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Hi, wonderful work! Thanks. running on NVIDIA TX1( Coretex A57 CPU,) with 10threads. speed : Avg speed, ~85 ms.
./feather_benchmark ./data/mobilenet.feathermodel ./data/input_3x224x224.txt 20 10 (No question but ijust want to make a small contribution).
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Thank you, but why do you want to launch 10 threads on a TX1? I think there are only 4 CPUs.
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horse racing mechanism in Tencent?
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Related Issues (20)
- Do you plan to support convert pytorch model to feathercnn model? HOT 2
- InitFromPath never returns HOT 1
- 请问该框架与ncnn的区别与联系? HOT 12
- 请问是否支持ssd? HOT 1
- Running benchmark with SqueezeNet model, segmentation fault occured HOT 2
- Does this also support RPi3B with Raspbian OS (armv7l) HOT 3
- 请问转换caffe模型的文件是feather_convert_caffe还是caffe_model_convert HOT 5
- typo and why so large input buffer HOT 1
- supported layer mismatch between layer_factory.cpp and feather_convert_caffe.cc
- Model convert error - libprotobuf HOT 1
- Performance testing using experimental branch on Andriod
- build_linux.sh
- 未使用的局部变量
- Add support for tf or split+transpose in caffe.
- loadparam
- 这个项目还在维护吗?
- Layer type Deconvolution not registered
- Documentation or examples for ARM usage. HOT 3
- OpenCL or Vulkan port?
- Evaluation
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