Instructions to use mobilint/YOLO26x-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/YOLO26x-depth with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="YOLO26x-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
YOLO26x_depth
aries-rb/ holds the MXQ reproduced from the published arxiv folder (https://github.com/mobilint/mblt-model-arxiv/tree/main/models/depth_estimation/YOLO26x_depth). It was compiled by qbcompiler 1.3.0 in the image mobilint/qbcompiler:1.3-cuda12.8.1-ubuntu22.04 (sha256:6ad69aaa9581487ac6a1864b88d4c2179c7a4a486a3ab1ac376dbdfe763415e7) 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 0.80897, abs_rel 0.14387, rmse 0.58097. Only the evaluated core mode was measured.
SHA-256: 383b31a4ae3c148bdf1092054b9b26fcb229bc476d1ca070dc6f780068d87af2 (58939453 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": "YOLO26x_depth",
"variant": "bias-corr-ls",
"qbcompiler": "1.3.0",
"qbruntime": "1.5.0",
"compile": {
"image_tag": "1.3-cuda12.8.1-ubuntu22.04",
"image_digest": "sha256:6ad69aaa9581487ac6a1864b88d4c2179c7a4a486a3ab1ac376dbdfe763415e7",
"inference_scheme": "single",
"device": "gpu"
},
"evaluation": {
"dataset": "nyu-depth",
"sample_count": 654,
"primary": {
"delta1": 0.80897
},
"secondary": {
"abs_rel": 0.14387,
"rmse": 0.58097
},
"protocol": null,
"eval_data_identity": null,
"identity_audit": "absent",
"inference_mode": "async",
"image_tag": "1.3-cpu-ubuntu22.04",
"image_digest": "sha256:7c25b894fc0807acdf27f15c61b910b96dc66d3f5d97e04b595b2d7a8008e77c"
},
"retention_percent": {
"delta1": 99.41626112176178
},
"content_sha256": "8246143cfbfee84ff6f227a9d109f08729e156bb30eb236eb5907c08dc48df2f",
"attempt_id": "srcmodes10-yolo26x-depth-20261010t175053",
"superseded_attempt_ids": [],
"sha256": "383b31a4ae3c148bdf1092054b9b26fcb229bc476d1ca070dc6f780068d87af2",
"format": "MXQv7",
"supported_modes": [
"single"
],
"size": 58939453,
"ops_commits": {
"selection": "66d2f03be3d5af292a3bd707a630737ad5631488"
},
"arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/depth_estimation/YOLO26x_depth",
"publication": {
"status": "public",
"reason": "approved"
}
}
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Ultralytics/YOLO26