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State of the art in Image Classification in 2022 - A Complete Guide

LearnOpenCV
59.7K subscribers
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6.5K views 2 years ago #objectdetection #AI #computervision
Learn about State-of-the-Art Deep Learning models for Image Classification in 2022. Florence is a foundation model based on Transformer architecture that can be extended to other computer vision tasks fairly easily. FixEfficientNet uses a finetuning technique called FixRes to improve the performance of an existing SotA Model - the EfficientNet. Model Soups also uses the Vision Transformer architectures along with huge amounts of data and clever weight averaging techniques to achieve SoTA performance. SoTA-models #ImageClassification #CNNs #ConvolutionalNeuralNetworks #ImageNet #ILSVRC 📑 Check out the papers on the models discussed in the video: ✅Florence: A New Foundation Model for Computer Vision - https://arxiv.org/pdf/2111.11432v1.pdf ✅FixEfficientNet: Fixing the Train-Test resolution discrepancy - https://arxiv.org/pdf/2003.08237v5.pdf ✅Model Soups: averaging weights of multiple fine-tuned models improves accuracy without increasing infere …...more
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State of the art in Image Classification in 2022 - A Complete Guide
208Likes
6,550Views
2022Apr 18
Learn about State-of-the-Art Deep Learning models for Image Classification in 2022. Florence is a foundation model based on Transformer architecture that can be extended to other computer vision tasks fairly easily. FixEfficientNet uses a finetuning technique called FixRes to improve the performance of an existing SotA Model - the EfficientNet. Model Soups also uses the Vision Transformer architectures along with huge amounts of data and clever weight averaging techniques to achieve SoTA performance. SoTA-models #ImageClassification #CNNs #ConvolutionalNeuralNetworks #ImageNet #ILSVRC 📑 Check out the papers on the models discussed in the video: ✅Florence: A New Foundation Model for Computer Vision - https://arxiv.org/pdf/2111.11432v1.pdf ✅FixEfficientNet: Fixing the Train-Test resolution discrepancy - https://arxiv.org/pdf/2003.08237v5.pdf ✅Model Soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time - https://arxiv.org/pdf/2203.05482v1.pdf ❓FAQ❓ Which method is best for image classification? Is CNN the best for image classification? Why is ANN not used for image classification? Why is image classification difficult? ⭐️Time Stamps ⭐️ 0:00-0:23: Introduction 0:23-0:58: ImageNet Dataset 0:58-1:20: Types of Accuracy 1:19-2:20: Florence Model by Microsoft 2:20-3:16: Fix EfficientNet L2 3:16-4:19: Model Soups 4:19-5:01: Summary 🖥️ On our blog - https://learnopencv.com we also share tutorials and code on topics like Image Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow. 🤖 Learn from the experts on AI: Computer Vision and AI Courses YOU have an opportunity to join the over 5300+ (and counting) researchers, engineers, and students that have benefited from these courses and take your knowledge of computer vision, AI, and deep learning to the next level. https://opencv.org/courses #️⃣ Social Media #️⃣ 📝 Linkedin:   / satyamall.  . 📱 Twitter:   / learnopencv   🔊 Facebook: https://www.facebook.com/profile.php?... 📸 Instagram:   / learnopencv   🔗 Reddit:   / spmallick   🔖Hashtags🔖 #AI #drowsydriver #drowsinessalert #driverdrowsiness #machinelearning #objectdetection #deeplearning #computervision #image #imageprocessing #imageclassification

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LearnOpenCV

59.7K subscribers