Image Segmentation
LiteRT
LiteRT
android
on-device
gpu
lane-detection
adas
autonomous-driving
real-time
Instructions to use litert-community/Ultra-Fast-Lane-Detection-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/Ultra-Fast-Lane-Detection-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Commit History
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Card: measured device / browser results (edge-compat, 2026-09)
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Card: definition line (LiteRT, 2026-09)
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Add measured Raspberry Pi 5 (CPU) section
Verified signatureNumbers from the 2026-08-31 Raspberry Pi 5 sweep: LiteRT benchmark_model (litert-cli-nightly 0.2.0.dev20260805), CPU/XNNPACK, 4 threads, 10 warm-up + 50 timed runs x 3 invocations per file, cooldown between invocations, thermal throttle state verified 0x0 on every run. Median + spread, never single-shot. Measurements are joined to this repo by exact .tflite file name.2f5bde1
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Add measured Snapdragon NPU / GPU section (Galaxy S26)
Verified signatureNumbers from the 2026-08-23 Galaxy S26 sweep. Primary log: litertlm-convert/portal_work/bench_all.txt + bench_redo.txt, summarised in portal_work/NOTES.md (2026-08-23, the 50-model NPU/GPU comparison). Every row carries device, SoC, runtime, N and thermal state. Measurements are joined to this repo by exact .tflite file name.1a14551
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Card: add a measured Performance table (Pixel 8a, benchmark_model GPU+CPU), naming the runtime for each row
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Upload ufld.tflite with huggingface_hub
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Upload hero.png with huggingface_hub
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Upload README.md with huggingface_hub
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initial commit
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