Скачать с ютуб Master ImageNet Models for Classification in TensorFlow & Keras в хорошем качестве

Master ImageNet Models for Classification in TensorFlow & Keras

📚 Blog post Link: https://learnopencv.com/image-classif... 📚 Check out our FREE Courses at OpenCV University : https://opencv.org/university/free-co... In this computer vision tutorial video, we will teach you how to perform image classification using pre-trained ImageNet models. While we previously learned how to train a basic neural network to classify images from the CIFAR-10 dataset, that was a relatively easy task as there were only ten classes. On the contrary, classifying a larger number of object types will necessitate much larger networks with millions of parameters. Fortunately, pre-trained models are accessible in Keras via the ImageNet project, which have been trained to recognize objects from 1,000 different classes. With just a few lines of code, we can employ these pre-trained models for image classification without any training. Topics Covered ✅ImageNet and ILSVRC ✅Pre-Trained Models in Keras ✅Read and Display Sample Images ✅Pre-Trained Model Setup ✅Make Predictions using the Pre-Trained Models ❓FAQ on Keras/TensorFlow What are pre-trained ImageNet models in TensorFlow and Keras? How do I install TensorFlow and Keras for image classification tasks? What are the popular pre-trained models available for image classification? How can I load a pre-trained ImageNet model using TensorFlow and Keras? How do I preprocess input images for the pre-trained ImageNet models? Can I fine-tune a pre-trained ImageNet model for my specific classification task? How do I replace the top layers of a pre-trained model to adapt it to my dataset? What is transfer learning and how does it relate to pre-trained ImageNet models? How do I train a fine-tuned model with my custom dataset? How can I evaluate the performance of my image classifier using pre-trained ImageNet models? What are some common issues and solutions when working with pre-trained ImageNet models? How can I optimize my image classifier for better performance and efficiency? Are there any limitations or drawbacks of using pre-trained ImageNet models for image classification tasks? How do I save and load my fine-tuned model for future use? Can I use pre-trained ImageNet models for tasks other than image classification, like object detection or segmentation? ⭐️ Time Stamps:⭐️ 00:00-00:48: Introduction 00:48-01:36: ImageNet & ILSVRC 01:36-02:11: Keras 2.11 02:11-03:25: API in Keras 03:25-04:10: Preprocess 04:10-05:15: Batch Dimension 05:15-06:00: Predict Method 06:00-07:02: Reading & Displaying Images 07:02-07:20: Loading Models 07:20-10:01: Convenience Functions 10:01-11:23: Making Predictions 11:23-11:50: Resnet-50 11:50-13:57: Inception-V3 13:57-14:18: Conclusion Resources: 🖥️ 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/university/courses/ #️⃣ Connect with Us #️⃣ 📝 Linkedin:   / satyamallick   📱 Twitter:   / learnopencv   🔊 Facebook: https://www.facebook.com/profile.php?... 📸 Instagram:   / learnopencv   🔗 Reddit:   / spmallick   🔖Hashtags🔖 #keras #tensorflow #machinelearning #neuralnetwork #objectdetection #deeplearning #computervision #learnopencv #opencv #tutorial #kerastutorial #tensorflowtutorial #ImageClassification #PretrainedModels #ImageNet #TensorFlow #Keras #DeepLearning #TransferLearning #FineTuning #NeuralNetworks #CNNs #ObjectRecognition #ImageProcessing #ComputerVision #MachineLearning #AITutorial #PythonProgramming #ModelOptimization #CustomDataset #ModelEvaluation #ModelPerformance