Autoencoder-Driven Anomaly Detection.
Visualize anomaly detection results across different datasets
Detecting visual anomalies for novel categories!
Analyze NFL injuries from 2012-2015
Classify images as normal or anomaly
Identify image anomalies by generating heatmaps and scores
Detect anomalies in credit card transaction data
Detect adversarial examples using neighborhood relations
Detect financial transaction anomalies and get expert insights
Monitor and analyze ADAS sensor data
Detect fraudulent Ethereum transactions
Detect anomalies in images
ManuEncode is a powerful tool designed for Anomaly Detection in images. It leverages an autoencoder model to identify unusual patterns or outliers in image data. By uploading examples of normal images, users can train the model to recognize and detect anomalies in new, unseen images. ManuEncode is user-friendly and accessible, making it suitable for both experts and non-experts.
• Autoencoder Model: Utilizes advanced neural network architecture to learn normal image patterns and detect deviations. • Image Upload: Easily upload datasets of normal images for training. • Real-time Detection: Quickly analyze new images for anomalies. • Customizable Thresholds: Adjust sensitivity levels to fine-tune detection accuracy. • Visual Feedback: Provides clear visual results to highlight anomalies. • Fast Processing: Optimized for efficient training and inference.
What types of images can ManuEncode process?
ManuEncode supports standard image formats like JPG, PNG, and BMP. It is optimized for grayscale and RGB images.
Can I customize the anomaly detection threshold?
Yes, users can adjust sensitivity levels to balance between false positives and missed detections based on their needs.
How long does the training process take?
Training time depends on the size of the dataset and complexity. Small datasets typically take a few minutes, while larger datasets may require more time.