Albumentations

Albumentations is absolutely essential for data augmentation in #ComputerVision. 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. Was very delighted to speak with Vladimir Iglovikov about the future of Albumentations, which I believe is incredibly bright.

Monthly update. As of 2024-11-01 (Changes in brackets for the past 30 days) Money: - $62 (+$37) in donations - 3 (+2) sponsors --- Monthly active Users in the UI tool - 26 (+13) ---- Stats: - Contributors: 3 (-1) - 4.9 million downloads (+6.5%) https://lnkd.in/g7sWxZhW - Used by 27,927 (+1.03%) https://lnkd.in/gVvcNxgP - Moved up 23 places at PyPI Download Leaderboard https://pypilb.vercel.app/ -------- Marketing: - 14196 (+0.07%) stars on GitHub - 2258 (+1.03%) citations for a scientific paper https://lnkd.in/g3hFuHwC - 137 (+0) followers on Twitter https://lnkd.in/gT6CZE2a - 322 (+54) followers on LinkedIn https://lnkd.in/gtPQVWQ6 - 13k (+8.3%) unique visitors on the website ---- #albumentations #monthlyupdate #deeplearning #python #computervision #imageaugmentation #dataaugmentation #opensource #machinelearning #ai #datascience #projectstats

🚀 Albumentations 1.4.21 Released 🚀 🔗 https://lnkd.in/gdjQnGwN ---- - Decoupled random generator in Compose from the global random state. You can fix it as transform.set_random_seed(). Should help with reproducability. - Added option to save parameters of the transforms that were used on every application. Should help with debugging and finding optimal augmentation parameters per class / per sample. - Moved benchmark to a separate repo. - Speedup in PlankianJitter on uint8 images - Can automatically Random / Center Crop. Helps if you have images that could be smaller than crop size. - Replaced on uint8 images addWeighted by OpenCV with a faster version from SimSimd by Ash Vardanian #Albumentations #ComputerVision #DataAugmentation #MachineLearning #AI

I recently came across an incredible tool, Albumentations. If you’re working with image data, this library is a game-changer. It’s packed with a ton of image augmentation options and integrates seamlessly with Python, making it super easy to use. What really impressed me was their demo website, it’s like having a playground to test out all their augmentation functions. For anyone building machine learning models, this is huge. With a wider variety of augmented images, models become way more robust and adaptable to real-world scenarios. Definitely worth checking out if you want to level up your data pipeline and model performance!

Big thanks to Savelii (Sava) K. for the support So far, he is the largest sponsor of Albumentations in October 2024!

🚀 Albumentations 1.4.19 Released 🚀 🔗 https://lnkd.in/gRgKD97s ---- - Now you can use any interpolation for masks. Could be useful in [1] semantic segmentation, as INTER_NEAREST_EXACT is more accurate than INTER_NEAREST [2] In heatmap, depth estimation, probability map where INTER_LINEAR, INTER_CUBIC, INTER_AREA would work better than INTER_NEAREST - Replaces cv2.LUT by OpenCV with stringzilla LUT by Ash Vardanian, as latter is materially faster 🧹 Removed heavy scikit-image dependency 🐛 Various bugfixes. 🚨 Python 3.8 is not supported anymore #Albumentations #ComputerVision #DataAugmentation #MachineLearning #AI

Exponents are weird beasts. Very unintuitive. Maybe that's why it is tempting to look for them everywhere. Last month - more than 10% of total downloads accumulated over past 6 years. If growth stops - in 12 months will pass 100M of total downloads. If it will continue with the same pace - much shorter. Thinking about making a poster for a wall in a living room as a reminder that sometimes things take time.

It's the final countdown until RetrieveX on Oct 17... And I am super excited to announce two more speakers. Vladimir Iglovikov, the creator of Albumentations (and the CEO of the company), a Kaggle Grandmaster, and former ML Engineer at Lyft's self-driving division. Vladimir holds a PhD in Physics and has published over 20 papers in Physics and Machine Learning. Kelly Peng is cofounder of First Intelligence, multimodal AI company, and Kura, a company that focuses on immersive AR glasses. She also did nano-fabrication, AI and optical research at Stanford, as well as AI brain-machine interface research at UC Berkeley. Both will lead two workshops at RetrieveX. Want to learn the state of the art in AI? Get your ticket today - we're almost at capacity: https://lnkd.in/gr5gPeBa (apply DAVIT35 FOR 35% OFF)

🎶 Enhancing Music Genre Classification with Data Augmentation 🎶 We’re thrilled to share an exciting update on our music genre classification project! After achieving an impressive 91% accuracy with the VGG16 model, we pushed the boundaries further by incorporating data augmentation techniques. 🔄 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐮𝐝𝐢𝐨 𝐒𝐩𝐞𝐜𝐭𝐫𝐨𝐠𝐫𝐚𝐦𝐬: Using the Albumentations library, we applied various transformations to the mel spectrograms, such as shifts, rotations, and color jitters. These augmentations helped the model generalize better by simulating variations in the data, which improved robustness. 🎯 𝐑𝐞𝐬𝐮𝐥𝐭𝐬: These enhancements boosted the model’s accuracy by an additional 1-2%, reaching even higher precision in classifying music genres. The power of augmenting data, even in non-traditional domains like audio, is truly remarkable😎! ✨ 𝐅𝐢𝐧𝐚𝐥 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐬: Working on this project has been an exciting and enriching learning experience. Not only did we achieve great results, but it also helped me grow my skills in deep learning, transfer learning, and creative problem-solving in the domain of music classification. 👉 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐨𝐧 𝐆𝐢𝐭𝐇𝐮𝐛: https://lnkd.in/gBB6Dwmj #DeepLearning #MusicClassification #VGG16 #MachineLearning #AI #TechInnovation #PassionProject #OpenToWork #JobSearch #HireMe #RemoteWork #JobSeeker