Recipes for computer vision - educational and research use

Filenaming: Task-Stage-Model-Dataset-Framework

For single-label classification, mirco F1 and accruacy are equal. For multi-label, they are not.
For imbalance dataset, use weighted F1 instead of macro F1.
Training hacks/Fastai: progressive resizing, gradual unfreezing
Baseline - Object Classification [single/multi class, single/multi label]: ResNet, ViT, EfficientNet
Baseline - Object Detection [single/multi class, single/multi label]: YoloV3-5, RetinaNet, FasterRCNN
Baseline - Object Segementation [semantic / instance]: MaskRCNN, U-NET, FCN, DeepLabV3
MultilabelStratifiedKFold: https://github.com/trent-b/iterative-stratification

Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
xai-cv		xai-cv
.gitignore		.gitignore
LICENSE		LICENSE
ObjectClassificationBinary_FineTune_ResNet_CatsVsDogs_Pytorch.ipynb		ObjectClassificationBinary_FineTune_ResNet_CatsVsDogs_Pytorch.ipynb
ObjectClassificationBinary_Infer_ResNet_CatsVsDogs_Pytorch.ipynb		ObjectClassificationBinary_Infer_ResNet_CatsVsDogs_Pytorch.ipynb
ObjectClassificationMultiClass_FineTune_ResNet_MNIST_Pytorch.ipynb		ObjectClassificationMultiClass_FineTune_ResNet_MNIST_Pytorch.ipynb
ObjectDetection_Infer_YoloV3_COCO_OpenCV.ipynb		ObjectDetection_Infer_YoloV3_COCO_OpenCV.ipynb
ObjectSegmentationInstance_Infer_MaskRCNN_COCO_PyTorch.ipynb		ObjectSegmentationInstance_Infer_MaskRCNN_COCO_PyTorch.ipynb
ObjectSegmentationPanopticAndSemantic_Infer_Mask2Former_Cityscapes_PyTorch.ipynb		ObjectSegmentationPanopticAndSemantic_Infer_Mask2Former_Cityscapes_PyTorch.ipynb
ObjectSegmentationPanoptic_Infer_DETR_COCO_PyTorch.ipynb		ObjectSegmentationPanoptic_Infer_DETR_COCO_PyTorch.ipynb
README.md		README.md