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Extend EfficientNet pretrained support to B2-B8 #4963

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Extend EfficientNet pretrained support to B2-B8 #4963

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2 changes: 1 addition & 1 deletion library/src/otx/backend/native/models/classification/backbones/efficientnet.py
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Expand Up @@ -672,6 +672,6 @@ def __new__(

if pretrained:
cache_dir = Path.home() / ".cache" / "torch" / "hub" / "checkpoints"
download_model(net=model, model_name=f"{model_name}", local_model_store_dir_path=str(cache_dir))
download_model(net=model, model_name=f"{model_name}c", local_model_store_dir_path=str(cache_dir))
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@sovrasov sovrasov Nov 25, 2025 •
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Thanks Ilya for the contribution. I think here we can not just replace this weights without a set of comprehensive experiments. 100% positive correlation with IN-top1 is not given, there could be sudden outlier which we'd like to avoid.

To tackle that, I'd propose moving this version parameter to init of EfficientNetBackbone and forwarding it from the endpoint classes (EfficientNetMulticlassCls etc). Once done, the model version is configurable from model recipe yaml file. Corner case of b0 and b1 should be handled.

print(f"Download model weight in {cache_dir!s}")
return model
5 changes: 3 additions & 2 deletions library/tests/unit/backend/native/models/classification/backbones/test_otx_efficientnet.py
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Expand Up @@ -23,8 +23,9 @@ class TestOTXEfficientNet:
"efficientnet_b8",
],
)
def test_forward(self, model_name):
model = EfficientNetBackbone(model_name, pretrained=None)
@pytest.mark.parametrize("pretrained", [True, False])
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@sovrasov sovrasov Nov 25, 2025

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Please, don't download anything here to avoid increase of UTs duration and reliability. You could mock download_model if the idea is to check that the URL is correct somehow.

def test_forward(self, model_name, pretrained):
model = EfficientNetBackbone(model_name, pretrained=pretrained)
assert model(torch.randn(1, 3, 244, 244))[0].shape[-1] == 8
assert model(torch.randn(1, 3, 244, 244))[0].shape[-2] == 8

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