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tomas-gajarsky/
facetorch-deepfake-efficientnet-b7

Name: facetorch-deepfake-efficientnet-b7
Availability: InStock

deepfake-detection

face-forgery-detection

mit

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EfficientNet-B7 deepfake detector

The EfficientNet-B7 checkpoint from Selim Seferbekov's DFDC solution, exported for Facetorch.

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	`deepfake-efficientnet-b7`
Architecture	TF EfficientNet-B7 Noisy Student
Input	380 by 380 RGB face crop
Output	One binary logit per face; Facetorch applies sigmoid and a configurable decision threshold.
Dynamic shapes	Batch dimension 1 through 64.
Weights license	MIT

Preprocessing: Resize to 380 by 380 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	`4520da731410d4a4a7917d4d1dc91e367e82a890a213e0f6318296b0e16974d1`
`model-torch2.11.pt2`	pt2	>=2.11, <2.12	cpu, cuda	`97b49a70174c0d4f72d9d510d817bdc49a907af9af0242a6a1ba934a7cc9e4b7`
`model.pt`	torchscript	>=2.6, <2.12	cpu	`e36197ae83fa7c050e8ceba267a1f028f2d74d8ad805bf5d5037a26e8ca96c13`

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 = "deepfake-efficientnet-b7"
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, 380, 380, 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/selimsef/dfdc_deepfake_challenge	`89c6290490bac96b29193a4061b3db9dd3933e36`	checkpoint publisher and architecture source	MIT

Upstream checkpoint	SHA-256	Source
`final_777_DeepFakeClassifier_tf_efficientnet_b7_ns_0_31`	`00dd6cd9466cddfd7da7c333d8fecae81593307827093deaf4d7cdf704bc8bfa`	publisher location

Mapping method: exact_tensor_equality_against_all_seven_author_release_checkpoints.

Result: The named checkpoint uniquely matched all 1,202 model tensors 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.

Intended use

Research and human-assisted screening for manipulated imagery.

Limitations and responsible use

The score is not proof that media is authentic or manipulated and is vulnerable to domain shift and new generation methods.
Use corroborating forensic evidence and human review.
The checkpoint license does not grant rights to the DFDC 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-deepfake-efficientnet-b7