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Ray-Patch: An Efficient Querying for Light Field Transformers Official implementation of the paper "Ray-Patch: An Efficient Querying for Light Field Transformers" The implementation has been done using Pytorch 2, Pytorch Lightning 2, and cuda 11 7 To run the repository we suggest to use the conda environment: The models are evaluated on two datasets:
ICCV 2023 Open Access Repository Our Ray-Patch decoding reduces the computational footprint and increases inference speed up to one order of magnitude compared to previous models, without losing global attention, and hence maintaining specific task metrics
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Ray-Patch: An Efficient Decoder for Light Field Transformers Our Ray-Patch decoding reduces the computational footprint up to two orders of magnitude compared to previous models, without losing global attention, and hence maintaining specific task metrics
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RayPatchQuerying README. md at main - GitHub To train a Ray-Patch querying model add argument --full-scale Training the model also has a huge memory footprint We trained both models using 4 Nvidia Tesla V100 with 32 GBytes of VRAM each For ScanNet experiments, we used batch 16 and gradient accumulation of 2 to simulate batch size 32
Ray Patchak - Environmental Engineer - LinkedIn Environmental Engineer at Argonne National Laboratory · Experience: Argonne National Laboratory · Education: University of Illinois at Chicago · Location: Elgin · 259 connections on LinkedIn View