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README.md

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+ImageNet-100 Dataset (Zipped ImageFolder)OverviewThis dataset is a 100-class subset of the ImageNet-2012 (ILSVRC2012) dataset. It was specifically curated for academic research in computer vision, including tasks such as image compression and color-to-grayscale conversion.Dataset DetailsClass Selection: 100 classes were selected using a fixed random seed (42) to ensure reproducibility.Format: The dataset is stored as zipped chunks to facilitate stable uploads and downloads of large files.Total Splits: * Train: ~130,000 images (distributed across 5 ZIP chunks).Validation: 5,000 images (distributed across 5 ZIP chunks).Labels: Full mapping of WordNet IDs to human-readable labels is provided in Labels.json.📂 Class CategoriesThe 100 classes are distributed across several logical categories:CategoryCountExamplesCanines (Dogs)13Siberian husky, Bloodhound, Miniature schnauzerBirds8Hummingbird, Sulphur-crested cockatoo, GoosePrimates4Chimpanzee, Howler monkey, MacaqueWild Mammals12Polar bear, Hippopotamus, Red panda, White wolfReptiles & Fish8Stingray, Bullfrog, Alligator lizardVehicles5Minibus, Moped, Trailer truckInstruments3Flute, Bassoon, TromboneHousehold Items18Teapot, Hourglass, Vacuum cleaner, Reflex cameraFood & Nature7Banana, Mushroom, Seashore, PotpieSports & Other22Volleyball, Baseball, Scuba diver, Stone wallHow to Use1. Download & ExtractSince the data is split into chunks, you must download and extract them to recreate the ImageFolder structure.Pythonfrom huggingface_hub import hf_hub_download
+import zipfile
+
+# Example for one chunk
+path = hf_hub_download(repo_id="asafaa/imagent100", filename="imagenet100_train_part1.zip", repo_type="dataset")
+with zipfile.ZipFile(path, 'r') as zip_ref:
+    zip_ref.extractall("./data")
+2. Local Training (PyTorch)Once extracted, use torchvision.datasets.ImageFolder:Pythonfrom torchvision.datasets import ImageFolder