Instructions to use mobilint/DeiT_Small_Patch16_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/DeiT_Small_Patch16_224 with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="DeiT_Small_Patch16_224", 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
DeiT_Small_Patch16_224
aries-rb/ holds the MXQ reproduced from the published arxiv folder (https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/DeiT_Small_Patch16_224). 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 imagenet (50000 samples): top1_accuracy 0.7984, top5_accuracy 0.9505. Only the evaluated core mode was measured.
SHA-256: 0533ff026e7498c2311269f881609e01c4fe3a15beca942dc8399f3e61c007c8 (30306320 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": "DeiT_Small_Patch16_224",
"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": "single",
"device": "gpu"
},
"evaluation": {
"dataset": "imagenet",
"sample_count": 50000,
"primary": {
"top1_accuracy": 0.7984
},
"secondary": {
"top5_accuracy": 0.9505
},
"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": {
"top1_accuracy": 99.98246800410749
},
"content_sha256": "80ac140ee3062cefedb6d33da868d8c7ec27b6a0cb735f6631d067716de8eaee",
"attempt_id": "DeiT_Small_Patch16_224-jinman-20261017t110702-6d28f",
"superseded_attempt_ids": [],
"sha256": "0533ff026e7498c2311269f881609e01c4fe3a15beca942dc8399f3e61c007c8",
"format": "MXQv7",
"supported_modes": [
"single"
],
"size": 30306320,
"ops_commits": {
"selection": "9c9a469c2a5f34dd9650f972b289e1bfdf438c27"
},
"arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/DeiT_Small_Patch16_224",
"publication": {
"status": "public",
"reason": "approved"
}
}
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Model tree for mobilint/DeiT_Small_Patch16_224
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timm/deit_small_patch16_224.fb_in1k