Instructions to use mobilint/EfficientNet_B4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/EfficientNet_B4 with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="EfficientNet_B4", 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
File size: 3,008 Bytes
5fff1d1 1ca08ea 5fff1d1 cea90d1 5fff1d1 1c43d1a 5fff1d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 1c43d1a cea90d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
library_name: mobilint
license: bsd-3-clause
pipeline_tag: image-classification
tags:
- mobilint
base_model_relation: quantized
---
# EfficientNet_B4_IMAGENET1K_V1
`aries-rb/` holds the MXQ reproduced from the published arxiv folder (https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/EfficientNet_B4_IMAGENET1K_V1). 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 imagenet (50000 samples): top1_accuracy 0.82386, top5_accuracy 0.95972. Only the evaluated core mode was measured.
SHA-256: `c54435e3c975041aa69c17ace9a224cfe553b9114b29ecaf9b24a06451621a30` (28819691 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.
```json
{
"schema_version": 2,
"model": "EfficientNet_B4_IMAGENET1K_V1",
"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": "all",
"device": "gpu"
},
"evaluation": {
"dataset": "imagenet",
"sample_count": 50000,
"primary": {
"top1_accuracy": 0.82386
},
"secondary": {
"top5_accuracy": 0.95972
},
"protocol": null,
"eval_data_identity": {
"annotation_sha256": "f00550c10ff1d4110abc683786b6cc09d5082dadd3cee1568f5cfa1dad962960",
"sample_list_sha256": "54446819dced83110352e5a0361ff05b19c331c5a9802dcb0222e6d1e44a0547"
},
"identity_audit": "verified",
"inference_mode": "async",
"image_tag": "1.4-cpu-ubuntu22.04",
"image_digest": "sha256:62b5bc714571dffd94fb561275767ca80d15337a433630a3ae7936305d531a7b"
},
"retention_percent": {
"top1_accuracy": 98.79127994819771
},
"content_sha256": "494121102737bb1ab832fa6bb5d75a95bbc89ee99f5f157d5c7b66ea793940c3",
"attempt_id": "EfficientNet_B4_IMAGENET1K_V1-jinman-coreall-d74f677c1a03",
"superseded_attempt_ids": [],
"sha256": "c54435e3c975041aa69c17ace9a224cfe553b9114b29ecaf9b24a06451621a30",
"format": "MXQv7",
"supported_modes": [
"single",
"multi",
"global4",
"global8"
],
"size": 28819691,
"ops_commits": {
"selection": "6cc61b6c4600f8afef654f68ee851d2b65ebac1e"
},
"arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/EfficientNet_B4_IMAGENET1K_V1",
"publication": {
"status": "public",
"reason": "approved"
}
}
```
|