The classification models mainly based on CBAM-keras, the others are models that i explore and try:

• Frame include tensorflow and pytorch
• Specific details you can view README.md or TRAINING.md under each floder
• the structure of CBAM : Convolutional Block Attention Module"
• The main project file I use = ./ImageClassClassification/CBAM-tensorflow-slim

• Inception V4 + CBAM / + SE
• Inception-ResNet-v2 + CBAM / + SE
• ResNet V1 50 + CBAM / + SE
• ResNet V1 101 + CBAM / + SE
• ResNet V1 152 + CBAM / + SE
• ResNet V1 200 + CBAM / + SE
• ResNet V2 50 + CBAM / + SE
• ResNet V2 101 + CBAM / + SE
• ResNet V2 152 + CBAM / + SE
• ResNet V2 200 + CBAM / + SE

• Python 3.x
• TensorFlow 1.x
• TF-slim
• torch 1.x
• Keras (IMDB dataset)
• tqdm
• scikit-image
• numpy
• torch>=0.4.0
• torchvision
• pillow
• matplotlib
• wing

The application scenario of the model is vehicle detection.There are SSD and yolov3 In the project, the model i used is MyYOLO. Other floders are versions on keras and pytorch.

• Result: When the confidence is 0.8, the accuracy rate is above 0.95.
• SSD is an unified framework for object detection with a single network. It has been originally introduced in this research article.
• YOLOv3 [Original Implementation]
• The main project file I use = ./TargetDetection/MyYOLO

Semantic segmentation in cable quality inspection and autonomous driving applications Irregular boundary lines by semantic segmentation to achieve quality inspection the main network in the project is Bisenet+resnet50

• network structure, aritcle of Bisenet
• The main project file I use = ./SemantemeDivision/Segmentation
• Supported models:
  • Fontends
    • Inceptions_v4
    • Mobilenet_v2
    • Resnet_v1
    • Resnet_v2
    • Se_resnext
  • Builders:
    • Bisenet
    • Deeplab_v3
    • Refinenet