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
|
Download README.md from mobilint/EfficientNet_B4: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/mobilint/EfficientNet_B4/resolve/main/README.md
- Command line
-
hf download hf://mobilint/EfficientNet_B4/README.md
-
curl -L -o README.md https://huggingface.co/mobilint/EfficientNet_B4/resolve/main/README.md
2.74 kB
| 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 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 0.82396, top5_accuracy 0.95996. Only the evaluated core mode was measured. | |
| SHA-256: `82fbca1946a3094089ebbe5df58bb515a43a52a1e83a4dae988ae698df127c45` (25645905 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": "single", | |
| "device": "gpu" | |
| }, | |
| "evaluation": { | |
| "dataset": "imagenet", | |
| "sample_count": 50000, | |
| "primary": { | |
| "top1_accuracy": 0.82396 | |
| }, | |
| "secondary": { | |
| "top5_accuracy": 0.95996 | |
| }, | |
| "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": 98.8032712185529 | |
| }, | |
| "content_sha256": "494121102737bb1ab832fa6bb5d75a95bbc89ee99f5f157d5c7b66ea793940c3", | |
| "attempt_id": "srcmodes8-efficientnet-b4-imagenet1k-v1-20261009t164855", | |
| "superseded_attempt_ids": [], | |
| "sha256": "82fbca1946a3094089ebbe5df58bb515a43a52a1e83a4dae988ae698df127c45", | |
| "format": "MXQv7", | |
| "supported_modes": [ | |
| "single" | |
| ], | |
| "size": 25645905, | |
| "ops_commits": { | |
| "selection": "66d2f03be3d5af292a3bd707a630737ad5631488" | |
| }, | |
| "arxiv_folder": "https://github.com/mobilint/mblt-model-arxiv/tree/main/models/image_classification/EfficientNet_B4_IMAGENET1K_V1", | |
| "publication": { | |
| "status": "public", | |
| "reason": "approved" | |
| } | |
| } | |
| ``` | |