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Couldn't get the size of external files in `_split_generators` because a request failed:
404 Client Error: Not Found for url: https://huggingface.co/datasets/auto-exp-2/resolve/main/train.tar.gz
Please consider moving your data files in this dataset repository instead (e.g. inside a data/ folder).
Exception: HTTPError
Message: 404 Client Error: Not Found for url: https://huggingface.co/datasets/auto-exp-2/resolve/main/train.tar.gz
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 484, in _is_too_big_from_external_data_files
for i, size in enumerate(pool.imap_unordered(get_size, ext_data_files)):
File "/usr/local/lib/python3.9/multiprocessing/pool.py", line 870, in next
raise value
File "/usr/local/lib/python3.9/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 383, in _request_size
response.raise_for_status()
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/auto-exp-2/resolve/main/train.tar.gzNeed help to make the dataset viewer work? Open a discussion for direct support.
YAML Metadata
Warning:
The task_ids "other-image-classification" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
YAML Metadata
Warning:
The task_ids "image-classification" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
nateraw/auto-exp-2
Image Classification Dataset
Usage
from PIL import Image
from datasets import load_dataset
def pil_loader(path: str):
with open(path, 'rb') as f:
im = Image.open(f)
return im.convert('RGB')
def image_loader(example_batch):
example_batch['image'] = [
pil_loader(f) for f in example_batch['file']
]
return example_batch
ds = load_dataset('nateraw/auto-exp-2')
ds = ds.with_transform(image_loader)
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