![facebook download fbnet facebook download fbnet](https://3.bp.blogspot.com/-6dcg_j8PK1U/U2V_Sb0UohI/AAAAAAAABsM/y8XLe4CuwoQ/s1600/Facebook+v10.0.0.0.21+Beta+Apk.png)
Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:* All models have a common default configuration interface and API for * accessing/changing the classifier - get_classifier and reset_classifier * doing a forward pass on just the features - forward_features * these makes it easy to write consistent network wrappers that work with any of the models* All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired* The train script works in several process/GPU modes: * NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional) * PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled) * PyTorch w/ single GPU single process (AMP optional)* A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat() at model creation. Several (less common) features that I often utilize in my projects are included. Match the all lowercasecreation fn for the model you'd like. Use the -model arg to specify model for train, validation, inference scripts.
![facebook download fbnet facebook download fbnet](http://3.bp.blogspot.com/-ZZlwy139p-4/T7NQRymy_EI/AAAAAAAAAoM/4lYY9xCrh1A/s1600/facebook-logo12.jpg)
#FACEBOOK DOWNLOAD FBNET MODS#
#FACEBOOK DOWNLOAD FBNET CODE#
PyTorch image models, scripts, pretrained weights - (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and moreįor each competition, personal, or freelance project involving images + Convolution Neural Networks, I build on top of an evolving collection of code and models.