[ECCV 2022] This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation. - BEVFormer/projects/configs/bevformer/bevformer_tiny.py at master · Simon-
bevformer模型bevformer-tiny-epoch-2 难免**任性上传382.22MB文件格式pth github上面下载特别慢,费了好大劲下载下来,在这里共享给大家 (0)踩踩(0) 所需:1积分电信网络下载
This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation. - BEVFormer/projects/configs/bevformer/bevformer_tiny.py at master · mfkiwl/BEVFormer
BEVFormer / projects / configs / bevformer / bevformer_tiny.py bevformer_tiny.py9.16 KB 一键复制编辑原始数据按行查看历史 lizhiqi提交于3年前.init # BEvFormer-tiny consumes at lease 6700M GPU memory # compared to bevformer_base, bevformer_tiny has ...
BEVFormer / projects / configs / bevformer / bevformer_tiny.py bevformer_tiny.py9.16 KB 一键复制编辑原始数据按行查看历史 lizhiqi提交于3年前.init # BEvFormer-tiny consumes at lease 6700M GPU memory # compared to bevformer_base, bevformer_tiny has ...
bevformer模型bevformer-tiny-epoch-24 难免**任性上传382.22MB文件格式pth github上面下载特别慢,费了好大劲下载下来,在这里共享给大家 (0)踩踩(0) 所需:1积分
BEVFormer / projects / configs / bevformer / bevformer_tiny.py bevformer_tiny.py9.16 KB 一键复制编辑原始数据按行查看历史 lizhiqi提交于3年前.init # BEvFormer-tiny consumes at lease 6700M GPU memory # compared to bevformer_base, bevformer_tiny has ...
# BEvFormer-tiny consumes at lease 6700M GPU memory # compared to bevformer_base, bevformer_tiny has # smaller backbone: R101-DCN -> R50 # smaller BEV: 200*200 -> 50*50 # less encoder layers: 6 -> 3 # smaller input size: 1600*900 -> 800*450 # multi-scale feautres ...
# BEvFormer-tiny consumes at lease 6700M GPU memory # compared to bevformer_base, bevformer_tiny has # smaller backbone: R101-DCN -> R50 # smaller BEV: 200*200 -> 50*50 # less encoder layers: 6 -> 3 # smaller input size: 1600*900 -> 800*450 # multi-scale feautres ...
# BEvFormer-tiny consumes at lease 6700M GPU memory # compared to bevformer_base, bevformer_tiny has # smaller backbone: R101-DCN -> R50 # smaller BEV: 200*200 -> 50*50 # less encoder layers: 6 -> 3 # smaller input size: 1600*900 -> 800*450 # multi-scale feautres ...