AISOMA AG
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Artificial Intelligence in Healthcare - Promising Progress (Best Use Cases)
https://www.linkedin.com/pulse/artificial-intelligence-healthcare-promising-progress-murat-durmus/
https://www.linkedin.com/pulse/artificial-intelligence-healthcare-promising-progress-murat-durmus/
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Case Study:
Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+
without connecting to any external web/cloud services
Our approach:
1. Object detection
HoughCircle Detection (OpenCV)
2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).
Achieved validation accuracy: 93.3%*
Test accuracy achieved: 93.4%*
Runtime for classification: 50ms
*strongly dependent on lighting conditions and camera settings
https://youtu.be/XVvfcj_F_uc
Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+
without connecting to any external web/cloud services
Our approach:
1. Object detection
HoughCircle Detection (OpenCV)
2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).
Achieved validation accuracy: 93.3%*
Test accuracy achieved: 93.4%*
Runtime for classification: 50ms
*strongly dependent on lighting conditions and camera settings
https://youtu.be/XVvfcj_F_uc
Post has attachment
Case Study:
Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+
without connecting to any external web/cloud services
Our approach:
1. Object detection
HoughCircle Detection (OpenCV)
2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).
Achieved validation accuracy: 93.3%*
Test accuracy achieved: 93.4%*
Runtime for classification: 50ms
*strongly dependent on lighting conditions and camera settings
https://youtu.be/XVvfcj_F_uc
Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+
without connecting to any external web/cloud services
Our approach:
1. Object detection
HoughCircle Detection (OpenCV)
2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).
Achieved validation accuracy: 93.3%*
Test accuracy achieved: 93.4%*
Runtime for classification: 50ms
*strongly dependent on lighting conditions and camera settings
https://youtu.be/XVvfcj_F_uc
Post has attachment
Case Study:
Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+
without connecting to any external web/cloud services
Our approach:
1. Object detection
HoughCircle Detection (OpenCV)
2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).
Achieved validation accuracy: 93.3%*
Test accuracy achieved: 93.4%*
Runtime for classification: 50ms
*strongly dependent on lighting conditions and camera settings
https://youtu.be/XVvfcj_F_uc
Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+
without connecting to any external web/cloud services
Our approach:
1. Object detection
HoughCircle Detection (OpenCV)
2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).
Achieved validation accuracy: 93.3%*
Test accuracy achieved: 93.4%*
Runtime for classification: 50ms
*strongly dependent on lighting conditions and camera settings
https://youtu.be/XVvfcj_F_uc
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There's a bit of truth in it. Just a bit... ;-)
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A “new” kind of Information Processing in the Brain.
https://medium.com/@webadmin_46735/a-new-kind-of-information-processing-in-the-brain-ff8c733e32f3
https://medium.com/@webadmin_46735/a-new-kind-of-information-processing-in-the-brain-ff8c733e32f3
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