---
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.")

# Download both splits
download_and_extract("train")
download_and_extract("val")