mlnomad/imagenet1k_classes
ImageNet-1k Individual Class Datasets Hub This dataset serves as a central hub and index for the 1,000 individual class datasets derived from the original imagenet-1k dataset. Each class has been separated into its own repository for easy access and analysis. This repository does not contain any images itself. Instead, it provides a class_mapping.csv file that maps each class ID and name to its corresponding dataset repository on the Hugging Face Hub. How to Use… See the full description on the dataset page: https://huggingface.co/datasets/mlnomad/imagenet1k_classes.
license: mit ---
ImageNet-1k Individual Class Datasets Hub
This dataset serves as a central hub and index for the 1,000 individual class datasets derived from the original imagenet-1k dataset. Each class has been separated into its own repository for easy access and analysis.
This repository does not contain any images itself. Instead, it provides a class_mapping.csv file that maps each class ID and name to its corresponding dataset repository on the Hugging Face Hub.
How to Use
The intended workflow is to use this repository to find the repo_id for a class you are interested in, and then load that specific dataset.
1. Load the Class Mapping File
First, load the class_mapping.csv file from this repository. You can do this directly using pandas.
import pandas as pd
from datasets import load_dataset
# Load the mapping file from the Hub
mapping_csv_url = "[https://huggingface.co/datasets/](https://huggingface.co/datasets/)mlnomad/imagenet1k_classes/resolve/main/class_mapping.csv"
class_df = pd.read_csv(mapping_csv_url)
print("Class mapping loaded. Here's a preview:")
print(class_df.head())2. Find and Load a Specific Class Dataset
Once you have the class_df DataFrame, you can easily find the repository for any class and load it using datasets.load_dataset.
Example: Loading the 'goldfish' class
# Find the entry for 'goldfish'
# The class names can be long, so we use .str.contains() for a flexible search
goldfish_entry = class_df[class_df['class_name'].str.contains("goldfish", case=False)]
if not goldfish_entry.empty:
# Get the repo_id from the DataFrame
goldfish_repo_id = goldfish_entry.iloc[0]['repo_id']
print(f"Found 'goldfish' dataset at: {goldfish_repo_id}")
# Load the actual dataset containing the images
print("Loading dataset...")
goldfish_dataset = load_dataset(goldfish_repo_id)
print("\nSuccessfully loaded!")
print(goldfish_dataset)
# You can now access the images
# example_image = goldfish_dataset['train'][0]['image']
# example_image.show()
else:
print("Could not find the 'goldfish' class.")
Class Mapping Preview (class_mapping.csv)
Here are the first 10 entries in the mapping file:
| class_id | class_name | repo_id |
|-----------:|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
| 0 | tench, Tinca tinca | mlnomad/imnet1k_tench_Tinca_tinca |
| 1 | goldfish, Carassius auratus | mlnomad/imnet1k_goldfish_Carassius_auratus |
| 2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias | mlnomad/imnet1k_great_white_shark_white_shark_man-eater_man-eating_shark_Carcharodon_carcharias |
| 3 | tiger shark, Galeocerdo cuvieri | mlnomad/imnet1k_tiger_shark_Galeocerdo_cuvieri |
| 4 | hammerhead, hammerhead shark | mlnomad/imnet1k_hammerhead_hammerhead_shark |
| 5 | electric ray, crampfish, numbfish, torpedo | mlnomad/imnet1k_electric_ray_crampfish_numbfish_torpedo |
| 6 | stingray | mlnomad/imnet1k_stingray |
| 7 | cock | mlnomad/imnet1k_cock |
| 8 | hen | mlnomad/imnet1k_hen |
| 9 | ostrich, Struthio camelus | mlnomad/imnet1k_ostrich_Struthio_camelus |