4.2 KiB
4.2 KiB
Model Comparison Dashboard
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Compliance with Requirements
Exploratory Analysis of Unstructured Data (Images)
Display of image examples by category:
- Visualization of Cityscapes dataset images organized by 8 segmentation meta-classes
- Interactive selection of training and test images with preview
- Category navigation interface with user-friendly display names
Data counting and distribution:
- Pie charts detailing the distribution of the 25,000 Cityscapes images
- Visualization of project usage: 80% train / 20% validation / 100% test
- Class distribution chart for the training dataset
- Summary tables with detailed statistics per split
Image transformations:
- Interactive demonstration of preprocessing: resizing and normalization
- Visualization of random data augmentations (rotation, flip, brightness, etc.)
- Before/after comparison of transformations with visual alignment
- Complete documentation of the 6 possible augmentation types
Input Data Selection
Multiple selection interface:
- Dropdown list for Cityscapes test images with ground truth information
- Upload of personal files (JPEG, PNG) with format validation
- Preview of selected images before processing
- Error handling and real-time user feedback
Prediction Results
Full model comparison:
- Side-by-side display of predictions from the two best models
- Visualizations with original image, predicted masks, and ground truth
- Detection and highlighting of segmentation errors
- Detailed per-class statistics with percentages and pixel counts
Performance metrics:
- Comparative inference time with speed analysis
- Performance scores (mIoU, Accuracy) per model
- Per-class IoU for detailed performance analysis
- Interactive comparative result charts
Cloud Deployment
Deployed architecture:
- FastAPI application for the model comparison API
- Streamlit interface for the user dashboard
- Deployment on cloud infrastructure with high availability
- Automatic resource management and scaling
WCAG Accessibility
Compliance with essential criteria:
Color Contrast (WCAG AA)
- Color palette with contrast ratios ≥ 4.1:1 for graphical elements
- Contrast ratios ≥ 4.5:1 for text on background
- 8 main colors validated: blue (#0066CC), red (#CC0000), green (#006600), etc.
- Replacement of gradients with solid, high-contrast colors
Text Alternatives
- Detailed alternative descriptions for all automatically generated charts
- Explanatory text for each visualization (pie charts, bar charts)
- Appropriate ARIA labels for interactive elements
- Complete textual documentation of image transformations
Non-Color-Only Indication
- Patterns and hatching complementary to colors (optional via checkbox)
- 6 different patterns: diagonal, vertical, horizontal, dots, cross
- Differentiation by shape and texture in addition to colors
- Reinforced borders and outlines for visual distinction
Keyboard Navigation
- Visible CSS focus with 2px blue outlines (#0066CC)
- Full support for tab navigation
- Clear visual indicators for focused elements
- Compliance with logical navigation order
Accessible Data Tables
- Structured tables complementary to all charts
- Appropriate headers with scope and descriptions
- Exportable data readable by screen readers
- Responsive configuration for all screens
Built-in assistive features:
- "Enable patterns (visual accessibility)" checkbox for customization
- Automatic chart descriptions with complete statistics
- Interface adaptable to the needs of users with disabilities
- Complete documentation of accessibility features via the
/accessibility-infoendpoint
Technical Architecture
Technology stack:
- Backend: FastAPI + PyTorch for model inference
- Frontend: Streamlit with accessible components
- Visualizations: Matplotlib with WCAG palettes
- REST API documented with Pydantic validation
- Optimized state management for model comparisons
Performance and reliability:
- Optimized inference times (< 1 second per image)
- Efficient memory management with automatic figure closing
- File format and size validation
- Automatic cleanup of temporary files