mmselfsup VS anomalib

MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.

For some reason, the loss goes up shaply right at the start and slowly goes back down. I am self-supervised pretraining an image modeling with DenseCL using mmselfsup (https://github.com/open-mmlab/mmselfsup). This shape happened on the Coco-2017 dataset and my custom dataset. As you can see, it happens consistently for different runs. How could the loss increase so sharply and is it indicative of an issue with the training? The loss peaks before the first epoch is finished. Unfortunately, the library does not support validation.

This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.

Then, when it comes to semi-supervised learning for anomaly detection, I had positive experiences with Anomalib which offers a robust library dedicated to deep learning anomaly detection algorithms. They implemented the latest models with PyTorch and offer tools to benchmark their performance.

Anomalib is an open-source library for unsupervised anomaly detection in images. It offers a collection of state-of-the-art models that can be trained on your specific images.

Anomalib is an open-source deep learning library developed by Intel that makes it easy to benchmark different anomaly detection algorithms on both public and custom datasets, all by simply modifying a config file. As the largest public collection of anomaly detection algorithms and datasets, it has a strong focus on image-based anomaly detection. It’s a comprehensive, end-to-end solution that includes cutting-edge algorithms, relevant evaluation methods, prediction visualizations, hyperparameter optimization, and inference deployment code with Intel’s OpenVINO Toolkit.

try https://github.com/openvinotoolkit/anomalib it's primarily aimed at vision applications but might provide some inspiration

Anomaly detection typically refers to the task of finding unusual or rare items that deviate significantly from what is considered to be the "normal" majority. In this blogpost, we look at image anomalies using PatchCore. Next to indicating which images are anomalous, PatchCore also identifies the most anomalous pixel regions within each image. One big advantage of PatchCore is that it only requires normal images for training, making it attractive for many use cases where abnormal images are rare or expensive to acquire. In some cases, we don't even know all the unusual patterns that we might encounter and training a supervised model is not an option. One example use case is the detection of defects in industrial manufacturing, where most defects are rare by definition as production lines are optimised to produce as few of them as possible. Recent approaches have made significant progress on anomaly detection in images, as demonstrated on the MVTec industrial benchmark dataset. PatchCore, presented at CVPR 2022, is one of the frontrunners in this field. In this blog post we first dive into the inner workings of PatchCore. Next, we apply it to an example in medical imaging to gauge its applicability outside of industrial examples. We use the anomalib library, which was developed by Intel and offers ready-to-use implementations of many recent image anomaly detection methods.