OpenOOD - ImageNet

Reproduces the OpenOOD benchmark for OOD detection, using a pre-trained ResNet 50.

Warning

This is currently incomplete, see ImageNet-OpenOOD.

import pandas as pd  # additional dependency, used here for convenience
import torch
from torchvision.models import resnet50
from torchvision.models.resnet import ResNet50_Weights

from pytorch_ood.benchmark import ImageNet_OpenOOD
from pytorch_ood.detector import MaxSoftmax
from pytorch_ood.utils import fix_random_seed

fix_random_seed(123)

device = "cuda:0"
loader_kwargs = {"batch_size": 16, "num_workers": 12}

model = resnet50(ResNet50_Weights.IMAGENET1K_V1).eval().to(device)
trans = ResNet50_Weights.IMAGENET1K_V1.transforms()

print(trans)

If you want to test more detectors, you can just add them here

detectors = {
    "MSP": MaxSoftmax(model),
}

The ImageNet root should contain at least the validation tar, the dev kit tar, and the meta.bin that is generated by the torchvision ImageNet implementation.

results = []
benchmark = ImageNet_OpenOOD(root="data", image_net_root="data/imagenet-2012/", transform=trans)


with torch.no_grad():
    for detector_name, detector in detectors.items():
        print(f"> Evaluating {detector_name}")
        res = benchmark.evaluate(detector, loader_kwargs=loader_kwargs, device=device)
        for r in res:
            r.update({"Detector": detector_name})
        results += res

df = pd.DataFrame(results)
print((df.set_index(["Dataset", "Detector"]) * 100).to_csv(float_format="%.2f"))

This should produce a table with the following output:

Dataset	Detector	AUROC	AUPR-IN	AUPR-OUT	FPR95TPR
ImageNetO	MSP	28.64	2.52	94.85	91.20
OpenImagesO	MSP	84.98	62.61	94.67	49.95
Textures	MSP	80.46	37.50	96.80	67.75
SVHN	MSP	97.62	95.56	98.77	11.58
MNIST	MSP	90.04	90.45	89.88	39.03