imagent100

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label imagent100@d4415abb27f6672f9474b6cbeb789f265f73f9af
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label imagent100@d4415abb27f6672f9474b6cbeb789f265f73f9af

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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.

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")

Downloads last month: 9

Total file size:

8.53 GB