Common Challenges
Data Availability
Attaining a large volume of high-quality and correctly labeled images, which are often labor-intensive and limited by data privacy
Missing or Biased Data
Underrepresented classes and scenarios in training that cause poor performance in production and lead to unfair outcomes
Model Generalization
Models that are unable to generalize adequately and cause irrelevant predictions when applied across various datasets
Purpose-Built Support
Use Arize to address common (and uncommon) challenges associated with your computer vision model.
Visualize Unstructured Data
Exploratory Data Analysis With 3D Visualizations
- Automatically generate embedding vectors for visual analysis and in-depth troubleshooting
- Color model dimensions such as features, tags, predictions, etc to reduce fatigue when analyzing a large amount of data
- Analyze clusters to find patterns in a data set such as low-resolution images, new data not found in training, or if your model is confusing two classes
Built-in Support
Native Object Detection Support
- Comprehensive support to upload object detection data such as bounding box coordinates, image links, and data labels for detailed analytics
- Zoom in on areas of high drift to identify prediction errors and better understand evolving patterns, new inputs, or labeling errors
- Surface underrepresented predicted classes to mitigate systemic bias and improve prediction performance
Optimize and Improve Model Performance
Diagnose and Correct Computer Vision Errors
- Drill down to the root cause of performance degradation with our embeddings analyzer
- Reduce costs by exporting problematic clusters for relabeling and fine-tuning
- Share production data in a dedicated notebook across teams and functions for further analysis
- Validate and compare model versions in a notebook before deploying into production
Industry Examples
