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项目作者： tyui592

项目描述 ：
PyTorch implementation of "Learning Deep Features for Discriminative Localization"
高级语言： Python
项目地址: git://github.com/tyui592/class_activation_map.git
创建时间： 2019-11-10T14:36:31Z
项目社区：https://github.com/tyui592/class_activation_map
开源协议：
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Class Activation Map

Unofficial Pytorch Implementation of ‘Learning Deep Features for Discriminative Localization’

Reference: Learning Deep Features for Discriminative Localization, CVPR2016

I used the Networks that trained ImageNet data from torchvision.models.

Requirements

  • torch (version: 1.2.0)
  • torchvision (version: 0.4.0)
  • Pillow (version: 6.1.0)
  • matplotlib (version: 3.1.1)
  • numpy (version: 1.16.5)

Usage

Arguments

  • --gpu-no: Number of gpu device (-1: cpu, 0~n: gpu)
  • --network: Network for backbone (Possible networks: resnet50, resnext50_32x4d, wide_resnet50_2, googlenet, densenet161, inception_v3, shufflenet_v2_x1_0, mobilenet_v2, mnasnet1_0)
  • --image: Input image path
  • --topk: Create k Class Activation Maps (CAMs) with the highest probability
  • --imsize: Size to resize image (maintaining aspect ratio)
  • --cropsize: Size to crop cetenr region
  • --blend-alpha: Interpolation factor to overlay the input with CAM
  • --save-path: Path to save outputs

Example Script

python cam.py --image imgs/input/img1.jpg --topk 3 --imsize 256 --network resnet50

Results

reulsts