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tomas-gajarsky/
facetorch-fer-efficientnet-b2

Name: facetorch-fer-efficientnet-b2
Availability: InStock

facial-expression-recognition

emotion-recognition

apache-2.0

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EfficientNet-B2 facial-expression classifier

EmotiEffLib EfficientNet-B2 AffectNet checkpoint with eight visible-expression categories.

This repository contains immutable model artifacts used by Facetorch. Use the packaged Facetorch manifest to select a revision and artifact; do not treat mutable main or older unlisted files as a release contract.

Contract

Field	Value
Model ID	`fer-efficientnet-b2`
Architecture	TF EfficientNet-B2 with max pooling
Input	260 by 260 RGB face crop
Output	Eight logits ordered as Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, Surprise.
Dynamic shapes	Batch dimension 1 through 64.
Weights license	Apache-2.0

Preprocessing: Resize to 260 by 260 and apply ImageNet mean [0.485, 0.456, 0.406] and standard deviation [0.229, 0.224, 0.225].

Release artifacts

File	Format	Runtime	Devices	SHA-256
`model-torch2.6.pt2`	pt2	>=2.6, <2.7	cpu, cuda	`faf0adb86eaf6f45e0511f41a4f0f41df2069da0216371132101b28e017a8320`
`model-torch2.11.pt2`	pt2	>=2.11, <2.12	cpu, cuda	`5247f93acebf092290ec56e48bebf4220b58f07d3bd4d9907f07b532c42d09bc`
`model.pt`	torchscript	>=2.6, <2.12	cpu	`91672ff45602901b1631db910ec2e65db21def35bda550c5f4e30a75361c9bdd`

Facetorch v1 supports the Torch 2.6 and 2.11 cohort files listed in its manifest. The legacy TorchScript object is CPU-only and requires the explicit legacy opt-in. Files from unsupported cohorts are not part of the v1 release contract.

Loading the manifest-selected artifact

python

import torch
from huggingface_hub import hf_hub_download
from facetorch.artifacts import get_model_manifest

MODEL_ID = "fer-efficientnet-b2"
device = "cuda" if torch.cuda.is_available() else "cpu"
artifact = get_model_manifest().candidates(
    MODEL_ID,
    torch_version=torch.__version__,
    device=device,
    allow_legacy_models=False,
)[0]
path = hf_hub_download(
    repo_id=artifact.repo_id,
    revision=artifact.revision,
    filename=artifact.filename,
)
model = torch.export.load(path).module().to(device).eval()
example = torch.randn(1, 3, 260, 260, device=device)
with torch.inference_mode():
    output = model(example)

The random tensor above is only a loading smoke test. Use Facetorch's documented preprocessing for meaningful inference.

Provenance

Upstream	Immutable revision	Role	License
https://github.com/sb-ai-lab/EmotiEffLib	`520a051c64cd191521e5934655314e769a319684`	checkpoint publisher and architecture source	Apache-2.0

Upstream checkpoint	SHA-256	Source
`models/affectnet_emotions/enet_b2_8.pt`	`20c8bb3ae6dace81b2ac51c8418bee508d86f14006566ecdfafdfd6d6acba14d`	publisher location

Mapping method: exact_tensor_equality.

Result: 508 of 508 tensors matched exactly.

The repository owner approved the mapping and redistribution record on 2026-08-23. Under the recorded policy, an author-published checkpoint in a permissively licensed repository with no separate checkpoint terms uses that repository license. MIT and Apache-2.0 have not been converted or treated as interchangeable. See LICENSE, THIRD_PARTY_NOTICES.md, and Facetorch's facetorch/models/governance.json.

Papers

Classifying Emotions and Engagement in Online Learning Based on a Single Facial Expression Recognition Neural Network

Intended use

Research classification of visible facial-expression categories.

Limitations and responsible use

Expression labels do not establish emotion, intent, mental state, or truthfulness.
Performance varies with culture, context, demographic representation, pose, and image quality.
The checkpoint license does not grant rights to AffectNet or other training datasets.
The artifact license does not itself license training datasets, input data, or a deployment's processing of personal data.
Do not use model output as the sole basis for consequential decisions about a person.

tomas-gajarsky/facetorch-fer-efficientnet-b2