nx上编译成功的ncnn,可以适配在虚拟机和ARM核心板上

上传者: YOULANSHENGMENG | 上传时间: 2023-02-20 23:35:56 | 文件大小: 13.93MB | 文件类型: RAR
nx上编译成功的ncnn,可以适配在虚拟机和ARM核心板上,进行基于ncnn的推理,build中包含了动态库,可以进行移植使用

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( 2936 个子文件 13.93MB ) nx上编译成功的ncnn,可以适配在虚拟机和ARM核心板上
.gitmodules 185B
squeezenet_int8.param 7.10KB
efficientnetv2_b0.param 27.75KB
alexnet.param 1.64KB
efficientnet_b0.param 19.74KB
vgg16.param 2.75KB
resnet50_int8.param 13.39KB
mobilenet_v2.param 12.35KB
proxylessnasnet.param 11.72KB
mobilenet.param 4.36KB
resnet18.param 6.99KB
regnety_400m.param 19.24KB
resnet50.param 15.48KB
googlenet_int8.param 14.92KB
blazeface.param 9.11KB
mobilenet_ssd.param 14.06KB
yolo-fastestv2.param 14.78KB
resnet18_int8.param 6.01KB
yolov4-tiny.param 6.39KB
nanodet_m.param 20.43KB
shufflenet_v2.param 15.44KB
mobilenetv2_yolov3.param 11.15KB
squeezenet_ssd_int8.param 16.87KB
shufflenet.param 18.04KB
mobilenet_yolo.param 5.69KB
mobilenet_int8.param 3.89KB
yolo-fastest-1.1.param 17.49KB
CMakeLists.txt 530B
squeezenet_ssd.param 19.44KB
README.md 185.31KB
squeezenet.param 8.03KB
mobilenet_v3.param 14.45KB
googlenet.param 16.85KB
mobilenet_ssd_int8.param 12.17KB
mnasnet.param 9.73KB
benchncnn.cpp 8.44KB
vgg16_int8.param 2.35KB
ncnn_generate_rvv_source.cmake 520B
ncnn_add_layer.cmake 13.95KB
ncnn_generate_avx_source.cmake 508B
ncnn_generate_arm82_source.cmake 514B
ncnn_generate_fma_source.cmake 508B
ncnn_generate_shader_comp_header.cmake 928B
ncnn_add_shader.cmake 1.39KB
run_test.cmake 275B
ncnn_generate_msa_source.cmake 514B
ncnn_generate_avx512_source.cmake 517B
ncnnConfig.cmake.in 1.18KB
ncnn_generate_shader_spv_header.cmake 41.60KB
pyproject.toml 162B
32-ncnn.png 483B
128-ncnn.png 1.16KB
256-ncnn.png 2.48KB
64-ncnn.png 636B
16-ncnn.png 233B
the-benchmark-of-caffe-android-lib,-mini-caffe,-and-ncnn.md 3.24KB
vulkan-conformance-test.md 2.34KB
build-for-VisualStudio.zh.md 3.77KB
how-to-build.md 25.71KB
build-mlir2ncnn.md 1.17KB
Home.md 3.34KB
faq.md 19.30KB
operation-param-weight-table.md 6.30KB
tensorflow-op-combination.md 2.06KB
how-to-implement-custom-layer-step-by-step.md 8.09KB
binaryop-broadcasting.md 1.22KB
param-and-model-file-structure.md 2.52KB
new-param-load-api.md 1.51KB
custom-allocator.md 2.08KB
low-level-operation-api.md 7.18KB
armv7-mix-assembly-and-intrinsic.md 2.66KB
add-custom-layer.zh.md 2.83KB
arm-a53-a55-dual-issue.md 2.68KB
preload-practice.zh.md 1.05KB
new-model-load-api.md 5.13KB
ncnn-tips-and-tricks.zh.md 3.17KB
aarch64-mix-assembly-and-intrinsic.md 1.09KB
how-to-write-a-neon-optimized-op-kernel.md 445B
how-to-be-a-contributor.zh.md 2.88KB
element-packing.md 2.93KB
operators.md 59.33KB
application-with-ncnn-inside.md 3.04KB
FAQ-ncnn-protobuf-problem.zh.md 2.27KB
use-ncnnoptimize-to-optimize-model.md 599B
use-ncnn-with-alexnet.zh.md 4.87KB
use-ncnn-with-alexnet.md 4.65KB
use-ncnn-with-opencv.md 3.56KB
FAQ-ncnn-throw-error.md 3.83KB
vulkan-notes.md 3.34KB
ncnn-load-model.md 1.38KB
quantized-int8-inference.md 2.46KB
use-ncnn-with-pytorch-or-onnx.md 1.20KB
FAQ-ncnn-vulkan.md 6.49KB
efficient-roi-resize-rotate.md 4.57KB
build-minimal-library.md 4.44KB
openmp-best-practice.md 4.06KB
openmp-best-practice.zh.md 3.23KB
FAQ-ncnn-produce-wrong-result.md 5.47KB
use-ncnn-with-own-project.md 2.36KB
build-android.cmd 2.93KB
......
文件过多,未全部展示
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