![]() How to use / evaluate OFA Specialized Networks Use Python eval_ofa_net.py -path 'Your path to imagenet' -net ofa_mbv3_d234_e346_k357_w1.0 OFA Network If the above scripts failed to download, you download it manually from Google Drive and put them under $HOME/.torch/ofa_nets/. get_active_subnet( preserve_weight = True) # Manually set the sub-network ofa_network. # Randomly sample sub-networks from OFA network ofa_network. model_zoo import ofa_net ofa_network = ofa_net( net_id, pretrained = True) Train once, specialize for many deployment scenariosĨ0% top1 ImageNet accuracy under mobile settingĬonsistently outperforms MobileNetV3 on Diverse hardware platforms First place in the 3rd Low-Power Computer Vision Challenge, DSP track at ICCV’19 using the Once-for-all Network. First place in the 4th Low-Power Computer Vision Challenge, both classification and detection track. OFA is available via pip! Run pip install ofa to install the whole OFA codebase. The hands-on tutorial of OFA is released! First place in the CVPR 2020 Low-Power Computer Vision Challenge, CPU detection and FPGA track. Once-for-All (OFA) Network is adopted by Alibaba and ranked 1st in the open division of the MLPerf Inference Benchmark ( Datacenter and Edge). Once-for-All (OFA) Network is adopted by ADI MAX78000/MAX78002 Model Training and Synthesis Tool. Once-for-All (OFA) Network is adopted by SONY Neural Architecture Search Library. Once-for-All is available at PyTorch Hub now!
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |