Automatically find and fix errors in any ML datasets with cleanlab
This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
The dataset consists of unlabeled patch triplets from 251079 locations across the globe, each patch covering 2640mx2640m and including 4 seasonal time stamps.
You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌
Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥
Here's 8 free Courses that'll teach you better than the paid ones:
1. CS50’s Introduction to Artificial Intelligence with Python (Harvard)
2. Data Science: Machine Learning (Harvard)
3. Artificial Intelligence (MIT)
4. Introduction to Computational Thinking and Data Science (MIT)
5. Machine Learning (MIT)
6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)
7. Statistical Learning (Stanford)
8. Mining Massive Data Sets (Stanford)
VToonify: Controllable High-Resolution Portrait Video Style Transfer
Resources for performing deep learning on satellite imagery:
- ML best Practice
Harvard CS109A #DataScience course materials — huge collection free & open!
1. Lecture notes
2. R code, #Python notebooks
3. Lab material
4. Advanced sections
and more ...
UFO: segmentation 140+ FPS
👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.