docs/report.md

Project Report

Model Selection

• Model: timm/mobilenetv4_conv_medium.e500_r224_in1k
• Task: image classification on ImageNet-1k
• Input size: 224 x 224
• Reason for selection: compact backbone, low CPU cost, and straightforward export to ONNX

Optimization Phase

Run python scripts/export_models.py and python scripts/benchmark_models.py --image path/to/sample.jpg to produce the measured values below.

Model	Size	Latency
Original	TBD MB	TBD ms
ONNX	TBD MB	TBD ms
Quantized	TBD MB	TBD ms

Error Handling Strategy

• Missing file: 422
• Invalid file type: 415
• Too large file: 413
• Corrupted image: 422
• Unexpected server crash: 500

Validation checks cover file extension, MIME type, upload size, and image decodability before inference starts.

System Architecture

flowchart TD
    Client --> FastAPI
    FastAPI --> ProcessPool
    ProcessPool --> ONNXModel
    ONNXModel --> Response

CI/CD Pipeline

flowchart TD
    Push --> GitHubActions
    GitHubActions --> Pytest
    Pytest --> DockerBuild
    DockerBuild --> HuggingFaceSpaces

Performance Testing

Load testing is intended for both local Docker and Hugging Face Spaces with JMeter against POST /predict. Collect throughput, request latency, and P95 latency, then identify the CPU saturation point where response time rises sharply.

Deliverables Checklist

• FastAPI application
• Model export scripts
• Benchmarking script
• Quantized ONNX model path
• Pytest suite
• Docker packaging
• GitHub Actions workflow
• Deployment script for Hugging Face Spaces