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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
+---
+license: apache-2.0
+task_categories:
+- image-classification
+tags:
+- imagenet
+- computer-vision
+- imagenet-100
+- image-compression
+- decolorization
+size_categories:
+- 100K<n<1M
+---
+
+# ImageNet-100 Dataset (Zipped ImageFolder)
+
+### Overview
+This 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 (decolorization).
+
+### Dataset Details
+* **Class Selection**: 100 classes were selected using a fixed random seed (42) to ensure reproducibility.
+* **Format**: The dataset is stored as zipped chunks (10 files total) to facilitate stable uploads and high-speed downloads.
+* **Total Splits**:
+    * **Train**: ~130,000 images, distributed across 5 ZIP chunks (`part1` to `part5`).
+    * **Validation**: 5,000 images, distributed across 5 ZIP chunks (`part1` to `part5`).
+* **Labels**: A complete mapping of WordNet IDs to human-readable labels is included in the `Labels.json` file.
+
+### 📂 Class Categories
+The 100 selected classes cover a diverse range of categories:
+
+| Category | Count | Examples |
+| :--- | :---: | :--- |
+| **Canines (Dogs)** | 13 | Siberian husky, Bloodhound, Miniature schnauzer |
+| **Birds** | 8 | Hummingbird, Sulphur-crested cockatoo, Goose |
+| **Primates** | 4 | Chimpanzee, Howler monkey, Macaque |
+| **Wild Mammals** | 12 | Polar bear, Hippopotamus, Red panda, White wolf |
+| **Reptiles & Fish** | 8 | Stingray, Bullfrog, Alligator lizard |
+| **Vehicles** | 5 | Minibus, Moped, Trailer truck |
+| **Instruments** | 3 | Flute, Bassoon, Trombone |
+| **Household Items** | 18 | Teapot, Hourglass, Vacuum cleaner, Reflex camera |
+| **Food & Nature** | 7 | Banana, Mushroom, Seashore, Potpie |
+| **Sports & Other** | 22 | Volleyball, Baseball, Scuba diver, Stone wall |
+
+---
+
+### How to Use
+
+#### 1. Automatic Download and Extraction
+Since the data is split into multiple chunks, use the following script to reconstruct the `ImageFolder` structure.
+
+```python
+import os
 import zipfile
+from huggingface_hub import hf_hub_download
+
+repo_id = "asafaa/imagent100"
+target_dir = "./imagenet100"
+
+def download_and_extract(split, num_parts=5):
+    for i in range(1, num_parts + 1):
+        filename = f"imagenet100_{split}_part{i}.zip"
+        print(f"Downloading {filename}...")
+        path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
+        
+        with zipfile.ZipFile(path, 'r') as zip_ref:
+            zip_ref.extractall(target_dir)
+    print(f"Finished extracting {split} split.")
 
-# 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
+# Download both splits
+download_and_extract("train")
+download_and_extract("val")