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