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