The 6 Phases CPMAI is a Best-practice methodology for AI Projects proposed by Cognilytica. This methodology consists of 6 phases. Phase 1: Business Understanding Assess the business situation Should you even use an AI in the first place? Is it…
4 Categories There are mainly 4 Image generation methods. 1. GAN Train a GENERATOR that generates an image from “z”, and have a DISCRIMINATOR discriminate whether the generated image is real or not. The GENERATOR tries to learn to be…
CVAT Here is one tool you can use to create annotation data for free. CVAT: – You can annotate data for many computer-vision-related tasks(semantic segmentation, 3d object detection, etc.) – You can export with multiple format options Link: CVAT
Stable Diffusion I’ve found a site where you can generate an image from a text using “Stable-Diffusion”. Stable Diffusion is a new image-generation method where you gradually add gaussian noise and then reverse. Reference – Site – Paper
Image Segmentation Here is one way to implement Image segmentation using contour detection. The precision is not quite there, but I was amazed that you can do segmentation just with contour detection. I think it is still usable if.. You…
PatchCore PatchCore offers competitive inference time while achieving state-of-the-art performance for both anomaly detection and localization. PatchCore is presented as an effective method for.. Maximizing nominal information available at test time Reducing biased towards ImageNet classes by using mid-level network…
PaDiM Several methods have been proposed to combine anomaly detection(Give anomaly score to images) and localization(Assign each pixel an anomaly score to output anomaly map) in a one-class learning setting(Whether an image is normal or not). However, either they require…
Anomaly Detection Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image.…
Simple SGD vs Cyclic Learning Rate I compared the training speed between two optimizers by training a UNet Model. Simple SGD optimizer = torch.optim.SGD(model.parameters(), lr=0.01) Cyclic Learning Rate optimizer = torch.optim.SGD(model.parameters(), lr=0.01) scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.1, max_lr=1e-4) Cyclic Learning Rate…
Learning Rate Range Test Learning rate may be the most important hyper-parameter in deep learning and you can use this test to find the right learning rate; Run your model and record accuracy/loss for several epochs while letting the learning…