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How to create a confusion matrix with the test result in your training model & How to visualize the history of network learning: accuracy, loss in graphs.How_to_create-image-classification-for-recognizing-persons-animals-others
Create image classification for recognizing persons, animals, others.flower_classification
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Image classification (persons, animals, other) on raspberry pi from pi-camera (in live time) used custom model tflite (output to terminal)Quantization
Quantization (post-training quantization) your (custom mobilenet_v2) models .h5 or .pb models using TensorFlow Lite 2.4classify_picamera_in_live_time_cusom_model
Image classification (persons, animals, other) on raspberry pi from pi-camera (in live time) used custom model .h5 (output to terminal)simple_classification_pi_vgg16_pretrain_model
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Image classification (persons, animals, other) on raspberry pi from pi-camera (with cycle while) used custom model .h5 (output to terminal)simple_classification_pi_vgg16_pretrain_model_from_pi_camera_while
Simple image classification on raspberry pi from pi-camera (with cycle while) used the pre-trained model VGG16simple_object_detection
Create simple recognition object detection in the live time on the Raspberry Pi - version 2oleksandr-g-rock
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Simple image classification (persons, animals, other) on raspberry pi used custom model tflite (output to terminal)create_animation_graphs_in_python
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Simple image classification on raspberry pi from pi-camera (in live time) used the pre-trained model mobilenet_v1 and TensorFlow Lite (output to terminal)custom_object_detection_person_notperson
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Image classification (persons, animals, other) on raspberry pi from pi-camera (with cycle while) used custom tflite model (output to terminal)simple-image-classification-4-divide-image-tflite
Simple image classification (persons, animals, other) on raspberry pi used custom model tflite (output to terminal) dividing image into 4 parts using OpenCV and TensorFlow Litedisease-recognition-from-xray
Recognition (3 diseases) from X-ray (Machine Learning tutorial) with accuracy: 96%Love Open Source and this site? Check out how you can help us