Instructions to use mobilint/YOLO26s-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/YOLO26s-depth with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="YOLO26s-depth", model_type="DEFAULT", model_path="", core_mode="global8", ) try: image = model.preprocess("path/to/image.jpg") output = model(image) result = model.postprocess(output) finally: model.dispose() - Notebooks
- Google Colab
- Kaggle
YOLO26s_depth
aries-rb/ holds the MXQ reproduced from the published arxiv folder (https://github.com/mobilint/mblt-model-arxiv/tree/main/models/depth_estimation/YOLO26s_depth). It was compiled by qbcompiler 1.4.0 in the image mobilint/qbcompiler:1.4-cuda12.8.1-ubuntu22.04 (sha256:7910f1661605b82eb9616f6ac99fd6461dbb865dcadaa810cce22c638ceffd44) with --inference-scheme single, so it supports the core mode(s) single only.
It was evaluated with qbruntime 1.5.0 in async inference mode on nyu-depth (654 samples): delta1 0.75462, abs_rel 0.16996, rmse 0.62293. Only the evaluated core mode was measured.
SHA-256: ff87d4f150be6886636d82f469de16b31ddd9989e906aa6ae0059f72f86501f5 (13686291 bytes). Verify a download with sha256sum <file>.mxq and compare the digest with this value and with aries-rb/metadata.json.
aries-rb/best_result.json contains legacy build values and is retained for mblt-vision-python compile defaults. The legacy aries/ directory was removed; mblt-model-zoo <= 2.12 must pin an older revision.
{
"schema_version": 2,
"model": "YOLO26s_depth",
"variant": "c0923-nobc",
"qbcompiler": "1.4.0",
"qbruntime": "1.5.0",
"compile": {
"image_tag": "1.4-cuda12.8.1-ubuntu22.04",
"image_digest": "sha256:7910f1661605b82eb9616f6ac99fd6461dbb865dcadaa810cce22c638ceffd44",
"inference_scheme": "single",
"device": "gpu"
},
"evaluation": {
"dataset": "nyu-depth",
"sample_count": 654,
"primary": {
"delta1": 0.75462
},
"secondary": {
"abs_rel": 0.16996,
"rmse": 0.62293
},
"protocol": null,
"eval_data_identity": null,
"identity_audit": "absent",
"inference_mode": "async",
"image_tag": "1.4-cpu-ubuntu22.04",
"image_digest": "sha256:62b5bc714571dffd94fb561275767ca80d15337a433630a3ae7936305d531a7b"
},
"retention_percent": {
"delta1": 98.61606617791195
},
"content_sha256": "6e5ca98fb2d5792fd19b59cdb3e3910e2fc9b6ca599c6596efabf356788e71ca",
"attempt_id": "srcmodes10-yolo26s-depth-20261010t175053",
"superseded_attempt_ids": [],
"sha256": "ff87d4f150be6886636d82f469de16b31ddd9989e906aa6ae0059f72f86501f5",
"format": "MXQv7",
"supported_modes": [
"single"
],
"size": 13686291,
"ops_commits": {
"selection": "66d2f03be3d5af292a3bd707a630737ad5631488"
},
"arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/depth_estimation/YOLO26s_depth",
"publication": {
"status": "public",
"reason": "approved"
}
}
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Ultralytics/YOLO26