Autoencoder-Driven Anomaly Detection.
Classify images as normal or anomaly
Detect fraudulent Ethereum transactions
Detect anomalies in images
Detecting visual anomalies for novel categories!
Detect anomalies in Excel data
A sample fraud detection using unsupervised learning models
Detect anomalies in credit card transaction data
Identify and visualize anomalies in Excel data
Implement using models like Isolation Forest/Local Outlier.
MVTec website
Detect network anomalies in real-time data
ManuEncode is an AI-powered tool designed for anomaly detection in images. It leverages advanced autoencoder technology to identify unusual patterns or abnormalities by learning from uploaded examples of normal images. This makes it particularly useful for applications where detecting deviations from the norm is critical.
• Autoencoder-Driven Detection: Utilizes deep learning to automatically identify anomalies in images.
• Image-Specific Anomaly Detection: Works with user-uploaded normal examples to learn and detect abnormalities.
• Real-Time Processing: Offers quick analysis and detection of anomalies in uploaded images.
• Customizable Thresholds: Allows users to adjust sensitivity for varying detection accuracy needs.
• Multiple Image Formats Supported: Accepts common image formats such as JPG, PNG, and BMP.
• User-Friendly Interface: Intuitive design for easy upload, processing, and result visualization.
What file formats does ManuEncode support?
ManuEncode supports standard image formats like JPG, PNG, and BMP for both training and detection.
Can I customize the anomaly detection sensitivity?
Yes, users can adjust the detection threshold to fine-tune the sensitivity of anomaly detection.
How long does the training process take?
The training time depends on the size and number of uploaded images. Larger datasets may require more time for processing.