Instructions to use mobilint/YOLO26s-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/YOLO26s-cls with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="YOLO26s-cls", 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_cls
Source: https://github.com/ultralytics/ultralytics
aries-rb/ holds the MXQ reproduced from the published arxiv folder (https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/YOLO26s_cls). 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 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 imagenet (50000 samples): top1_accuracy 0.7549, top5_accuracy 0.92672. Only the evaluated core mode was measured.
SHA-256: 355956e1ee649bd51da4ffbe45fb6208e5642e2c8ee3d366345471864cff0a7a (7758568 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_cls",
"variant": "c0923-asym",
"qbcompiler": "1.3.0",
"qbruntime": "1.5.0",
"compile": {
"image_tag": "1.3-cuda12.8.1-ubuntu22.04",
"image_digest": "sha256:6ad69aaa9581487ac6a1864b88d4c2179c7a4a486a3ab1ac376dbdfe763415e7",
"inference_scheme": "all",
"device": "gpu"
},
"evaluation": {
"dataset": "imagenet",
"sample_count": 50000,
"primary": {
"top1_accuracy": 0.7549
},
"secondary": {
"top5_accuracy": 0.92672
},
"protocol": null,
"eval_data_identity": {
"annotation_sha256": "f00550c10ff1d4110abc683786b6cc09d5082dadd3cee1568f5cfa1dad962960",
"sample_list_sha256": "54446819dced83110352e5a0361ff05b19c331c5a9802dcb0222e6d1e44a0547"
},
"identity_audit": "verified",
"inference_mode": "async",
"image_tag": "1.3-cpu-ubuntu22.04",
"image_digest": "sha256:7c25b894fc0807acdf27f15c61b910b96dc66d3f5d97e04b595b2d7a8008e77c"
},
"retention_percent": {
"top1_accuracy": 99.33940414780503
},
"content_sha256": "69a2c6d927e9abe16de2b25e7b39599d9d6fc910190ede9f378da5af1945beb1",
"attempt_id": "YOLO26s_cls-jinman-coreall-35794085c092",
"superseded_attempt_ids": [],
"sha256": "355956e1ee649bd51da4ffbe45fb6208e5642e2c8ee3d366345471864cff0a7a",
"format": "MXQv7",
"supported_modes": [
"single",
"multi",
"global4",
"global8"
],
"size": 7758568,
"ops_commits": {
"selection": "03fae69e43242602d43194b909b2ba769887fe15"
},
"arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/YOLO26s_cls",
"publication": {
"status": "public",
"reason": "approved"
}
}
- Downloads last month
- 75
Model tree for mobilint/YOLO26s-cls
Base model
Ultralytics/YOLO26