One of the key components of my 1st place solution in the Kaggle competition Recod.ai/LUC Scientific Image Forgery Detection was heavy domain-specific augmentation built with Albumentations. For microscopy images, I simulated real scientific figure artifacts and annotations: random letters, arrows, bounding boxes, zoom callouts, dashed lines, compression artifacts, blur, geometric distortions, flips/rotations, brightness & color shifts, etc. For Western blots, I used a different augmentation strategy focused on geometric transformations, resizing/cropping variations, contrast/gamma changes, inversion, compression and blur. What I especially liked about Albumentations was how easy it was to extend the library with custom transforms tailored to the domain. That flexibility made rapid experimentation extremely convenient during the competition. I've been using Albumentations for around 6 years, and it remains my go-to augmentation library for computer vision projects. Big thanks to Vladimir Iglovikov and the entire team! - Uladzislau Leketush, ML Engineer
Big thanks to Vladimir Iglovikov for such an easy to use and high-quality library as Albumentations. This library has streamlined my whole process of image augmentation, making it faster and more manageable. Really looking forward to new GUI features on their website, they have been very helpful :) - Dmitrii Sakharov, Computer Vision Engineer
Had a fantastic conversation with Vladimir Iglovikov, the founder of Albumentations. Albumentations has been a defining repository since its early days, with an immense amount of work poured into maintaining and advancing it. At Datature, we leverage Albumentations extensively, enabling our customers to add robustness to their image data for model fine-tuning. This has played a very significant role in the success of our platform. We're grateful for the dedication and innovation of the team (+contributors) behind Albumentations, which has helped shape the landscape of image augmentations in the machine learning / Kaggle community. Moving forward, we hope to explore more ways to work together and can’t wait to see how Albumentations will grow and continue to be a defining tool in the toolkit of serious hashtag#computervision engineers everywhere ⚡️ - Keechin Goh, CEO of Datature
Albumentations is absolutely essential for data augmentation in Computer Vision. I've been using it for a while, and it consistently delivers great results. It offers a powerful range of transformations, from basic flips to complex distortions, and it's incredibly fast thanks to OpenCV and NumPy optimizations. It integrates well with PyTorch and TensorFlow, making it a natural fit in any workflow. The API is intuitive, allowing for flexible and customizable pipelines with ease. Its support for bounding boxes and keypoints is ideal for advanced tasks like object detection. Albumentations is widely trusted for its reliability and flexibility, and it has become a go-to tool in my projects for improving model performance. - Alexandr Simonyan, Computer Vision Engineer
Albumentations has been instrumental in streamlining my workflow for segmentation and object detection model training. Its comprehensive set of transformations have significantly reduced the complexity of aligning image features with corresponding labels. Highly recommend it for fellow practitioners in the field - Anmol Sharan Nagi, Computer Vision Engineer
Great to see that @albumentations is becoming active on twitter. Its such a great library, and so well written. You not only can use the variety of augmentations for computervision but also easily adjust and implement rather exotic augmentations. Like we did for the ASL fingerspelling competition - Christof Henkel, Kaggle Competitions Grandmaster. Top 1 in the world.
just in general, thank you, albumentations is super useful!, Computer Vision Engineer
I rarely have something to share on LinkedIn. But today I would like to say thanks to Vladimir Iglovikov for the library he developed for this world for free, namely https://albumentations.ai/ Despite during the last couple of years I don't do any computer vision and don't use pytorch, I used to do this in the past. Albumentations has amazing variety of augmentations. I had everything I could imagine. The less code I write, the less bugs I have to fix, and I have everything already implemented in this package. Yes, it's not perfect, you should be careful with the default values. But given it is supported by a handful of enthusiastic, it's crazy good. Wish Vladimir Iglovikov will turn it into a mature product and make it an industry standard. - Natalia Pavlovskaya, Computer Vision Engineer
Vladimir I wanted to take a moment to thank you for creating the Albumentations library. I use it extensively in my projects, and it has significantly streamlined my workflow. The flexibility and efficiency it offers are unmatched. Truly grateful for your work! - Subham Agarwal, Founder and CEO at Neurabit Solution
Thank you Vladimir Iglovikov! We used Albumentations to train our digital pathology models extensively during my PhD at Oxford and it's great to see native H&E stain support! Stain-invariance is vital for training models that generalise well to new data (especially from different labs) and training with stain augmentations is a great way to do that. Highly recommend the library to those doing computer vision. - Claudia Vanea, Research Scientist at Nuance Labs
📢 Hi folks, Yesterday I had a discussion with Albumentations CEO and Founder, Vladimir Iglovikov. The main focus of our conversation was to provide feedback on the Albumentations library, and he asked if there were any areas for improvement. I shared some insights and suggestions throughout our discussion.I believe this feedback will be helpful for him in future development. I'm eagerly awaiting the updated version of the library. WHAT & WHY Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. 💡Based on my experience: 👉 Since 2020, I've been using the Albumentations library in both real-time projects and on Kaggle. Before adopting this library, I developed custom image processing and data augmentation functionality for audio, image, and text. Exploring new techniques was invaluable, but when I tried Albumentations, it consistently delivered better results, especially for data augmentation tasks. 👉 I’ve shared my experiences on Kaggle , including during BirdCLEF 2022 and 2023, where Albumentations helped improve accuracy without the need to build augmentation processes from scratch. I’ve utilized it extensively for object detection, classification, and segmentation tasks in computer vision, finding it incredibly useful across different applications. 👉 It's interesting to note that one of my favorite Kaggle Grandmasters is Vladimir. His advice has been incredibly helpful to me on my AI journey, and I truly appreciate how willing he was to share his knowledge whenever I had questions. 🥳Lastly, I would want to thank you for making this library open source and for easing some of the real-time project development process. - Venkatkumar R, Generative AI Developer
For the past six years, I've been a dedicated fan and user of Albumentations in my vision projects. I've had the pleasure of seeing it become the go-to image augmentation library in every team I've worked with. It's simply the best out there. I'm thrilled to see it integrated into Sinapsis, further solidifying its impact in the field. A huge shoutout to Vladimir Iglovikov for creating and maintaining such an incredible tool! - Ariel Ruiz-Garcia, Founder at Corteza.ai
I still remember my PhD days when I used Albumentation for many of my image augmentations works. Kudos to you! - Kamrul Hasan, Senior AI Scientist