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keras implementation of custom backbones

Python 100.00%

resnet resnext densenet googlenet efficientnet mobilenet darknet cspnet swintransformer

backbones's Introduction

VGG vgg16, vgg19, vgg for SSD
ResNet resnet18, resnet34, resnet50, resnet101, resnext, resnest, stochastic depth, CSP-resnet, 2.5D, SENet, SKNet
MobileNet mobileNetV1, mobileNetV2, mobileNetV3
GoogleNet InceptionV1-4, Xception, EfficientNet, Xception for deeplab, EfficientNetV2
darknet darknet53,
hourglass hourglass, hourglass for corner/centerNet
GCN GCN, GAT, DAG
HRNet HRNetv1, HRNetv2
Swin Transformer Swin-T/S/B/L
EfficientNet EfficientNet, EfficientNet-Lite, EfficientNetV2
utils BN: syncBN, softBN SGD: SoftSGD AdamW: Adam with weight decay

backbones's People

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huangongshu whyuek

backbones's Issues

ResNet_RS pre-trained

Hello, and thank you for this excellent work,
But I was wondering about the possibility of obtaining weights
resnet50_weights_tf_dim_ordering_tf_kernels.h5

If there is no way to get it, can you give some advice about training it on data ImageNet,
For your information, I have downloaded resnet50 standard weights from TF-V1 and TF-v2 and the number of layers mismatch appears,
Or this error<..
ValueError: You are trying to load a weight file containing 107 layers into a model with 139 layers.

and,
Can I take advantage of the following checkpoints?? ,

ImageNet Checkpoints from TF.tpu.official

We release configs and checkpoints of the ResNet-RS model family trained on ImageNet in Tensorflow 1.

Model	Input Size	V100 Lat (s)	TPU Lat (ms)	Top-1 Accuracy	Download
ResNet-RS-50	160x160	0.31	70	78.8	config \| ckpt
ResNet-RS-101	160x160	0.48	120	80.3	config \| ckpt
ResNet-RS-101	192x192	0.70	170	81.2	config \| ckpt
ResNet-RS-152	192x192	0.99	240	82.0	config \| ckpt
ResNet-RS-152	224x224	1.48	320	82.2	config \| ckpt
ResNet-RS-152	256x256	1.76	410	83.0	config \| ckpt
ResNet-RS-200	256x256	2.86	570	83.4	config \| ckpt
ResNet-RS-270	256x256	3.76	780	83.8	config \| ckpt
ResNet-RS-350	256x256	4.72	1100	84.0	config \| ckpt
ResNet-RS-350	320x320	8.48	1630	84.2	config \| ckpt
ResNet-RS-420	320x320	10.16	2090	84.4	config \| ckpt

Thank you and good luck to you anyway

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