Transformers-Tutorials
anomalib
Transformers-Tutorials | anomalib | |
---|---|---|
7 | 17 | |
8,172 | 3,291 | |
- | 4.2% | |
8.6 | 9.3 | |
9 days ago | 1 day ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Transformers-Tutorials
-
AI enthusiasm #6 - Finetune any LLM you wantđź’ˇ
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
- FLaNK Stack Weekly for 07August2023
- How to annotate compound words to build NER models?
-
[discussion] Anybody Working with VITMAE?
I'm pretraining on 850K grayscale spectrograms of birdsongs. I'm on epoch 400 out of 800 and the loss has declined from about 1.2 to 0.7. I don't really have a sense of what is "good enough" and I guess the only way I can judge is by looking at the reconstruction. I'm doing that using this notebook as a guide and right now it's doing quite badly.
-
[D] NLP has HuggingFace, what does Computer Vision have?
More tutorials can be found at https://github.com/NielsRogge/Transformers-Tutorials.
-
[Discussion] Information Extraction with LayoutLMv2
Ive been looking for an off the shelf encoder-decoder document understanding model for key information extraction. I found a great Huggingface implementation with concise notebook examples. However, the token classification model outputs a list of token labels corresponding bounding boxes for the token, but, not the text contained within the labeled bounding boxes themselves. Am I missing something? LayoutLMv2 describes itself as being capable of information extraction but without extracting the text I feel like it's fallen short of that ambition.
-
[Project] Deepmind's Perceiver IO available through Hugging Face
Example Notebooks
anomalib
-
Anomaly Detection with FiftyOne and Anomalib
In this post, you'll learn how to perform anomaly detection on visual data using FiftyOne and Anomalib from the OpenVINO toolkit. For demonstration, we'll use the MVTec AD dataset, which contains images of various objects with anomalies like scratches, dents, and holes.
-
May 8, 2024 AI, Machine Learning and Computer Vision Meetup
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.
- Anomalib: Anomaly detection library comprising cutting-edge algorithms
-
Exploring Open-Source Alternatives to Landing AI for Robust MLOps
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.
- Defect Detection using Computer Vision
-
From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
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.
- FLaNK Stack Weekly for 07August2023
-
Powering Anomaly Detection for Industry 4.0
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.
-
Early anomaly detection / Failure prediction on time series
try https://github.com/openvinotoolkit/anomalib it's primarily aimed at vision applications but might provide some inspiration
-
Anomaly detection in images using PatchCore
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.
What are some alternatives?
nn - 🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes đź“ť; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... đź§
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
gorilla-cli - LLMs for your CLI
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
pytorch-image-models - The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
ncappzoo - Contains examples for the Movidius Neural Compute Stick.
notebooks - Notebooks using the Hugging Face libraries 🤗
pycaret - An open-source, low-code machine learning library in Python
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
fiftyone - The open-source tool for building high-quality datasets and computer vision models
OpenBuddy - Open Multilingual Chatbot for Everyone