花卉识别-花卉数据集-花卉识别训练源码-花卉图片集（02）

共110个文件

txt：46个

py：33个

pyc：27个

版权申诉

花卉数据集

花朵分类

5星 · 超过95%的资源 69 浏览量 2023-04-15 19:46:37 上传评论 5 收藏 684.45MB ZIP 举报

资源推荐

资源详情

资源评论

收起资源包目录

花卉识别-花卉数据集-花卉识别训练源码-花卉图片集（02）（110个子文件）

run.bat 255B

dataset-002.dat 700.25MB

release.exe 10.44MB

17-32.jpg 388KB

reshape_layers.py 16KB

inception.py 16KB

resnet.py 14KB

operation_layers.py 14KB

convolution_layers.py 12KB

converter.py 11KB

elementwise_layers.py 9KB

activation_layers.py 6KB

vgg.py 6KB

squeezenet.py 6KB

pooling_layers.py 6KB

1_train.py 6KB

normalization_layers.py 5KB

mobilenet.py 5KB

utils.py 4KB

build_model.py 3KB

layers.py 3KB

padding_layers.py 3KB

2_test.py 3KB

release.py 2KB

linear_layers.py 2KB

4_export.py 2KB

tsl16.py 2KB

mnist.py 1KB

upsampling_layers.py 1KB

dataloader.py 1KB

3_eval.py 1KB

zfnet.py 1KB

alexnet.py 1KB

lenet.py 820B

main.py 761B

constant_layers.py 449B

__init__.py 136B

reshape_layers.cpython-38.pyc 13KB

inception.cpython-38.pyc 13KB

operation_layers.cpython-38.pyc 13KB

resnet.cpython-38.pyc 9KB

elementwise_layers.cpython-38.pyc 7KB

convolution_layers.cpython-38.pyc 7KB

converter.cpython-38.pyc 7KB

activation_layers.cpython-38.pyc 6KB

squeezenet.cpython-38.pyc 5KB

pooling_layers.cpython-38.pyc 5KB

mobilenet.cpython-38.pyc 5KB

normalization_layers.cpython-38.pyc 4KB

vgg.cpython-38.pyc 4KB

utils.cpython-38.pyc 3KB

layers.cpython-38.pyc 2KB

padding_layers.cpython-38.pyc 2KB

build_model.cpython-38.pyc 2KB

linear_layers.cpython-38.pyc 2KB

dataloader.cpython-38.pyc 1KB

upsampling_layers.cpython-38.pyc 1KB

tsl16.cpython-38.pyc 1KB

mnist.cpython-38.pyc 1KB

alexnet.cpython-38.pyc 1KB

zfnet.cpython-38.pyc 1KB

lenet.cpython-38.pyc 903B

constant_layers.cpython-38.pyc 664B

__init__.cpython-38.pyc 283B

resnet50_pred.txt 911KB

mobilenet_v2_pred.txt 910KB

inception_v3_pred.txt 910KB

resnet101_pred.txt 909KB

inception_v1_pred.txt 909KB

squeezenet_v2_pred.txt 907KB

mobilenet_v1_pred.txt 907KB

resnet18_pred.txt 906KB

inception_v2_pred.txt 904KB

resnet152_pred.txt 904KB

squeezenet_v3_pred.txt 904KB

LeNet_pred.txt 904KB

squeezenet_v1_pred.txt 903KB

vgg16_pred.txt 903KB

mnist_pred.txt 903KB

vgg11_pred.txt 903KB

alexnet_pred.txt 903KB

resnet34_pred.txt 902KB

zfnet_pred.txt 902KB

tsl16_pred.txt 902KB

vgg13_pred.txt 901KB

vgg19_pred.txt 898KB

readme.txt 381B

info-002.txt 94B

inception_v1_eval.txt 78B

mnist_eval.txt 78B

LeNet_eval.txt 78B

mobilenet_v1_eval.txt 78B

squeezenet_v3_eval.txt 78B

inception_v2_eval.txt 78B

zfnet_eval.txt 78B

squeezenet_v2_eval.txt 78B

resnet152_eval.txt 78B

vgg16_eval.txt 78B

tsl16_eval.txt 78B

resnet101_eval.txt 78B

共 110 条

1、将原始数据集文件（如dataset-001.dat、dataset-002.dat等）放在src_data文件夹中； 2、运行release.py脚本或者release.exe可执行文件，对src_data目录中的数据集进行释放。如果释放成功，则会在当前目录下产生test/train文件夹，分别存放测试集和训练集，并生成一个classes.txt文件来保存类别名称。

评论收藏

内容反馈

版权申诉