Instructions to use mobilint/YOLO26m-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/YOLO26m-depth with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="YOLO26m-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
YOLO26m_depth
aries-rb/ holds the MXQ reproduced from the published arxiv folder (https://github.com/mobilint/mblt-model-arxiv/tree/main/models/depth_estimation/YOLO26m_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 all, so it supports the core mode(s) single, multi, global4, global8 only.
It was evaluated with qbruntime 1.5.0 in async inference mode on nyu-depth (654 samples): delta1 0.79982, abs_rel 0.14832, rmse 0.59253. Only the evaluated core mode was measured.
SHA-256: 31a77e10baffc70b0bd653aec3e4b6f21b88be41bd7665cf9a0f045b63720583 (26931698 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": "YOLO26m_depth",
"variant": "c0923-asym-qronos",
"qbcompiler": "1.4.0",
"qbruntime": "1.5.0",
"compile": {
"image_tag": "1.4-cuda12.8.1-ubuntu22.04",
"image_digest": "sha256:7910f1661605b82eb9616f6ac99fd6461dbb865dcadaa810cce22c638ceffd44",
"inference_scheme": "all",
"device": "gpu"
},
"evaluation": {
"dataset": "nyu-depth",
"sample_count": 654,
"primary": {
"delta1": 0.79982
},
"secondary": {
"abs_rel": 0.14832,
"rmse": 0.59253
},
"protocol": null,
"eval_data_identity": {
"annotation_sha256": "cf5550fd19e9b819877df26b761d4922903c4086b485614560bf5dc0def2122a",
"sample_list_sha256": "7681a8f408fa8c24520f4f61f995ab25037869803543df605d0a0831a47f6f04"
},
"identity_audit": "verified",
"inference_mode": "async",
"image_tag": "1.4-cpu-ubuntu22.04",
"image_digest": "sha256:62b5bc714571dffd94fb561275767ca80d15337a433630a3ae7936305d531a7b"
},
"retention_percent": {
"delta1": 99.24556396575258
},
"content_sha256": "368750dbd104b4641111287a0ee4d319dcff9f10ea066ec66b6ee50c52988b93",
"attempt_id": "YOLO26m_depth-jinman-coreall-c8d548201702",
"superseded_attempt_ids": [],
"sha256": "31a77e10baffc70b0bd653aec3e4b6f21b88be41bd7665cf9a0f045b63720583",
"format": "MXQv7",
"supported_modes": [
"single",
"multi",
"global4",
"global8"
],
"size": 26931698,
"ops_commits": {
"selection": "fd3ef917cdf31869fa65fdc69e3c727f7e48f50c"
},
"arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/depth_estimation/YOLO26m_depth",
"publication": {
"status": "public",
"reason": "approved"
}
}
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Model tree for mobilint/YOLO26m-depth
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Ultralytics/YOLO26