Instructions to use litert-community/MiDaS-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/MiDaS-small 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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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.60029dd
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Add measured Galaxy S26 NPU/GPU section
Verified signatureNumbers from the S26 classic sweep. Primary logs: litertlm-convert/community_accel_work/s2_npu_sweep/results.jsonl (on-device JIT) and portal_work/bench_all.txt + bench_redo.txt (the 2026-08-23 AOT sweep); every row states which of the two produced it. Rows carry device, SoC, runtime, N and thermal state, and join to this repo by exact .tflite file name. Rows taken outside the sweep's thermal window are not reported.55956ac
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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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Add minimal usage snippets and hero image
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Upload sample-depth.png with huggingface_hub
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Upload sample-depth.png with huggingface_hub
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docs: add Training data & PII section (submission-guideline compliance)
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Upload folder using huggingface_hub
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initial commit
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