80+
Classes
COCO Dataset categories
SSD MobileNet
Architecture
Efficient lightweight model
Real-time
Inference
Server-side processing
Multi-format
Input
JPG, PNG, WEBP support
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System Architecture
Built on the Single Shot MultiBox Detector (SSD) architecture
Image Preprocessing
Component 1
Client-side optimization and normalization
- Automatic image resizing
- Format validation & conversion
- Tensor normalization (0-1 range)
- Batch dimension expansion
MobileNet Backbone
Component 2
Efficient feature extraction network
- Depthwise separable convolutions
- Inverted residual blocks
- Linear bottlenecks
- Low-latency execution
SSD Detection Head
Component 3
Single Shot MultiBox Detector
- Multi-scale feature maps
- Anchor box generation
- Class probability prediction
- Bounding box regression
Post-Processing
Component 4
Result filtering and formatting
- Non-Maximum Suppression (NMS)
- Confidence threshold filtering
- Coordinate rescaling
- JSON result serialization
EngineeringChallenges
Optimizing computer vision for web deployment
Inference Latency
Server-side TensorFlow execution
Fast, consistent response times
Model Size vs Accuracy
MobileNet V2 architecture
Good balance of speed and precision
Input Variation
Robust image preprocessing pipeline
Handles diverse resolutions and formats
Result Visualization
Responsive bounding box overlay system
Accurate mapping across device sizes
Processing PipelineImplementation Details
User Upload
Secure file handling
Technologies:
React Dropzone
Client-side Preview
File Validation
Server Action
Request processing
Technologies:
Next.js Server Actions
FormData Handling
Error Management
Model Inference
Object detection
Technologies:
TensorFlow.js Node
COCO-SSD Model
Tensor Operations
Response Rendering
Visual feedback
Technologies:
Framer Motion
Canvas/CSS Overlay
Statistical Summary
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