Roboflow Model Leaderboard
https://github.com/roboflow/model-leaderboard- Category
- AI Tools
- Rank
- No. 1526Tools index
Previous survey · No. 1534 ·
- Listed in
- #33 Find AI benchmarks
- Pricing
- Open Source
- Type
- TOOL
- Builder
- roboflow
- GitHub
- 109 stars
- Date
About
Comparison leaderboard for object detection models — which is best for small or large objects, with handy benchmarks.
What it does
Roboflow Model Leaderboard runs object detection models against COCO validation data and presents comparable evaluation results. It records mean average precision and F1 values at multiple thresholds, breaks both measures down by object size, and includes parameter counts, licenses, source links, papers, and run settings when available.
Why it's ranked here
This is a useful starting point for narrowing an object detection shortlist. Its shared dataset and metric schema make model families easier to compare, while size-specific results expose differences hidden by aggregate accuracy. Treat the output as screening evidence, not a production verdict, because the benchmark covers one configured dataset and does not report deployment speed or hardware costs.
What's good
The result format captures more than one headline metric. It includes mean average precision at several overlap thresholds, F1 at two thresholds, and separate small, medium, and large object results. Per-model environments reduce dependency conflicts. Stored run settings, parameter counts, model licenses, source links, and paper links add useful context for interpreting comparisons.
Tradeoffs
The workflow is still partly manual. Each model owns its runner and dependencies, naming standards remain relaxed, and the all-model shell script is described as an early version. The current data downloader targets COCO validation data. Several models are explicitly excluded during aggregation, and the displayed metrics omit latency, memory use, throughput, and hardware details.
How to use it well
Use it when choosing which object detectors deserve deeper testing, especially when target size affects your application. Compare accuracy, F1, parameter count, license, and recorded run settings, then reproduce promising candidates in isolated environments. Follow with tests on your own data and deployment hardware. It does not replace application-specific validation, speed profiling, memory measurement, or production monitoring.
Technical notes+
models/object_detection/utils.py loads COCO annotations through supervision, counts PyTorch parameters, and serializes mAP and F1 results with size-specific fields and metadata. build_static_site.py scans models/object_detection for matching result JSON files, skips a hard-coded blacklist, and emits browser data through static/aggregate_results.js. static/script.js builds the sortable table, badges, and run-parameter tooltips. gradio_app.py provides a separate Gradio table interface. download_data.py fetches and extracts COCO validation images and annotations. frontend/next.config.js configures a static Next.js export, while requirements.txt pins Gradio and Roboflow.
Observed
- Primary languages
- Python and JavaScript
- Install surface
- Python dependencies are pinned in requirements.txt; each model is expected to maintain a separate requirements file and virtual environment.
- Interfaces
- Static browser interface, local Gradio application, and runnable Python and shell scripts
- Benchmark dataset
- Configured for COCO validation data
- Metric structure
- Stores mAP and F1 results, including small, medium, and large object breakdowns.
- Frontend packaging
- Next.js is configured for static export with unoptimized images.
Read from README.md, pyproject.toml, requirements.txt, gradio_app.py, download_data.py, build_static_site.py, static/script.js, static/theme-toggle.js, frontend/next.config.js, frontend/postcss.config.js, frontend/tailwind.config.js, static/aggregate_results.js, models/object_detection/utils.py, models/object_detection/configs.py.
What it can do
Compare object detection model performance
Multiple object detection models → Performance comparison results
Benchmark models on small object detection
Object detection models and small object datasets → Small object detection accuracy metrics
Benchmark models on large object detection
Object detection models and large object datasets → Large object detection accuracy metrics
Rank models by detection performance
Model performance metrics → Leaderboard rankings
Generate model performance benchmarks
Object detection models and test datasets → Standardized benchmark scores
Identify best model for specific object sizes
Object size requirements and model comparisons → Recommended optimal model
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