update app

.gitignore

@@ -138,4 +138,5 @@ dmypy.json
 cython_debug/
 
 .DS_Store
-.vscode
+.vscode
+data/

README.md

@@ -16,7 +16,7 @@ Out-of-distribution (OOD) detection is an essential safety measure for machine l
 
 This demo is [online](https://huggingface.co/spaces/edadaltocg/ood-detection) at `https://huggingface.co/spaces/edadaltocg/ood-detection`
 
-## Running Gradio app locally:
+## Running Gradio app locally
 
 1. Install dependencies:
 
@@ -31,9 +31,3 @@ python app.py
 ```
 
 3. Open the app in your browser at `http://localhost:7860`.
-
-## Methods implemented
-
-- [ ] [Mahalanobis Distance](https://arxiv.org/abs/1807.03888)
-- [x] [Maximum Softmax Probability](https://arxiv.org/abs/1610.02136)
-- [x] [Energy Based Out-of-Distribution Detection](https://arxiv.org/abs/2010.03759)

app.py

@@ -25,7 +25,7 @@ TOPK = 3
 
 # load model
 print("Loading model...")
-model = timm.create_model("resnet50.tv2_in1k", pretrained=True, checkpoint_path="resnet50.tv2_in1k.bin")
+model = timm.create_model("resnet50.tv2_in1k", pretrained=True)
 model.to(device)
 model.eval()
 

centroids_resnet50.tv2_in1k_igeood_logits.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8079b4fc02b6542210d147d98d08b6220372534a18ba7ef9e844b17ab0a1d7e
+size 4000163

imagenet_ood.py

@@ -0,0 +1,132 @@
+import logging
+import os
+from typing import Callable, Optional
+
+from torchvision.datasets import ImageFolder
+from torchvision.datasets.utils import check_integrity, download_and_extract_archive, verify_str_arg
+
+_logger = logging.getLogger(__name__)
+
+
+class ImageNetA(ImageFolder):
+    """ImageNetA dataset.
+
+    - Paper: [https://arxiv.org/abs/1907.07174](https://arxiv.org/abs/1907.07174).
+    """
+
+    base_folder = "imagenet-a"
+    url = "https://people.eecs.berkeley.edu/~hendrycks/imagenet-a.tar"
+    filename = "imagenet-a.tar"
+    tgz_md5 = "c3e55429088dc681f30d81f4726b6595"
+
+    def __init__(self, root: str, split=None, transform: Optional[Callable] = None, download: bool = False, **kwargs):
+        self.root = root
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+
+        super().__init__(root=os.path.join(root, self.base_folder), transform=transform, **kwargs)
+
+    def _check_exists(self) -> bool:
+        return os.path.exists(os.path.join(self.root, self.base_folder))
+
+    def _check_integrity(self) -> bool:
+        return check_integrity(os.path.join(self.root, self.filename), self.tgz_md5)
+
+    def download(self) -> None:
+        if self._check_integrity() and self._check_exists():
+            _logger.debug("Files already downloaded and verified")
+            return
+        download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
+
+
+class ImageNetO(ImageNetA):
+    """ImageNetO datasets.
+
+    Contains unknown classes to ImageNet-1k.
+
+
+    - Paper: [https://arxiv.org/abs/1907.07174](https://arxiv.org/abs/1907.07174)
+    """
+
+    base_folder = "imagenet-o"
+    url = "https://people.eecs.berkeley.edu/~hendrycks/imagenet-o.tar"
+    filename = "imagenet-o.tar"
+    tgz_md5 = "86bd7a50c1c4074fb18fc5f219d6d50b"
+
+
+class ImageNetR(ImageNetA):
+    """ImageNet-R(endition) dataset.
+
+    Contains art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings,
+    patterns, plastic objects,plush objects, sculptures, sketches, tattoos, toys,
+    and video game renditions of ImageNet-1k classes.
+
+    - Paper: [https://arxiv.org/abs/2006.16241](https://arxiv.org/abs/2006.16241)
+    """
+
+    base_folder = "imagenet-r"
+    url = "https://people.eecs.berkeley.edu/~hendrycks/imagenet-r.tar"
+    filename = "imagenet-r.tar"
+    tgz_md5 = "a61312130a589d0ca1a8fca1f2bd3337"
+
+
+class NINCOFull(ImageFolder):
+    """`NINCO` Dataset subset.
+
+    Args:
+        root (string): Root directory of dataset where directory
+            exists or will be saved to if download is set to True.
+        split (string, optional): The dataset split, not used.
+        transform (callable, optional): A function/transform that takes in an PIL image
+            and returns a transformed version. E.g, `transforms.RandomCrop`.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+        **kwargs: Additional arguments passed to :class:`~torchvision.datasets.ImageFolder`.
+    """
+
+    PAPER_URL = "https://arxiv.org/pdf/2306.00826.pdf"
+    base_folder = "ninco"
+    filename = "NINCO_all.tar.gz"
+    file_md5 = "b9ffae324363cd900a81ce3c367cd834"
+    url = "https://zenodo.org/record/8013288/files/NINCO_all.tar.gz"
+    # size: 15393
+
+    def __init__(
+        self, root: str, split=None, transform: Optional[Callable] = None, download: bool = False, **kwargs
+    ) -> None:
+        self.root = os.path.expanduser(root)
+        self.dataset_folder = os.path.join(self.root, self.base_folder)
+        self.archive = os.path.join(self.root, self.filename)
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+
+        super().__init__(self.dataset_folder, transform=transform, **kwargs)
+
+    def _check_integrity(self) -> bool:
+        return check_integrity(self.archive, self.file_md5)
+
+    def _check_exists(self) -> bool:
+        return os.path.exists(self.dataset_folder)
+
+    def download(self) -> None:
+        if self._check_integrity() and self._check_exists():
+            return
+        download_and_extract_archive(
+            self.url, download_root=self.root, extract_root=self.dataset_folder, md5=self.file_md5
+        )
+
+
+if __name__ == "__main__":
+    ImageNetR(root="data", download=True)
+    ImageNetO(root="data", download=True)
+    ImageNetA(root="data", download=True)
+    NINCOFull(root="data", download=True)