Subject: Interest In Research And Study About Computer Vision

[NiharJani2002]

I am interested In Computer Vision,

Could You suggest a roadmap based on your experience? We are appropriate in 2023, considering the future of computer vision and recent sota.

Consider me as who understands Computer Vision from Beginner To Intermediate level.

[jbischof]

@NiharJani2002 I'd recommend taking an online course, e.g., from our friends at PyImageSearch. Reading papers directly can get quickly overwhelming for beginners.

[Mattral]

Lets see some coursera specialization courses where you can audit for free, (Based on my opinion)

Mathematics for ML(imperial college london)
https://www.coursera.org/specializations/mathematics-machine-learning?fbclid=IwAR2OheGV01ONwBV5CeqZnVHIZFGmip5iLSpR1eYFXC2AtIFRrMMYfRRvs-E

Machine Learning (stanford)
https://www.coursera.org/specializations/machine-learning-introduction?fbclid=IwAR0qBld8OEYfePiwLJGC5TX5RXZcqLdgKBz0so-2jyJNRc4MJWVEvWta7fU

Basic Level Computer Vision
https://www.coursera.org/specializations/firstprinciplesofcomputervision?fbclid=IwAR2HJ2y4xi8SuAyxKB0DNmoXwqNnC5sP6SPSf1UM4qaFg2GCCTBW0nLrmy4

Deep Learning
https://coursera.org/specializations/deep-learning?fbclid=IwAR1X08GmDvMC4K581dSXcS8hpRSNvpPOebrTrkErzBODLqYBu60zlk6OZvw

TensorFlow Professional Certificate
https://www.coursera.org/professional-certificates/tensorflow-in-practice?fbclid=IwAR0qBld8OEYfePiwLJGC5TX5RXZcqLdgKBz0so-2jyJNRc4MJWVEvWta7fU

Advanced CV
https://www.coursera.org/learn/advanced-computer-vision-with-tensorflow?fbclid=IwAR2Hwno-ITea29QvhaAvXdPolBdE5NDAm3umnIZKCj-SZhb2trk5L6rc7MY

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As for intermediate roadmaps

-Advanced Deep Learning
-Dive deeper into deep learning concepts, including loss functions and optimization algorithms.
-Learn about Regularization techniques (dropout, L1, L2).

-Object Detection
-Study object detection techniques and frameworks like YOLO, Faster R-CNN, or SSD.
-Implement object detection on real-world data.

-Image Segmentation
-Learn about semantic and instance segmentation.
-Experiment with models like U-Net and Mask R-CNN.

Transfer Learning
-Understand transfer learning and fine-tuning of pre-trained models.
-Apply transfer learning to solve real-world computer vision problems.

-Become proficient in deep learning frameworks like TensorFlow or PyTorch.
-Learn about GPU acceleration for faster training

Advanced Computer Vision Concepts
-Explore generative models like GANs for image generation.
-Study 3D vision, multi-view geometry, and depth estimation.

Practical Projects and Competitions
-Work on more complex computer vision projects.
-Participate in Kaggle competitions or other challenges to apply your skills.
-Research and Stay Updated

-Read research papers in the computer vision field, especially those related to the latest trends.
Follow leading conferences like CVPR, ICCV, and ECCV.

Online Courses and Specializations (as shown above)
-Consider advanced courses or specializations in computer vision, such as those from Coursera, edX, or universities.

-Join computer vision communities and forums.
-Collaborate on open-source projects to gain practical experience or join "Omdena Impact HUBs"