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Datasets:
kakasher
/
house-plant-species

Tasks:
Image Classification
Languages:
English
Size:
10K<n<100K
Tags:
biology
License:
Dataset card Files Community
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    KeyError
Message:      'jpg'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 90, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 68, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1816, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 238, in __iter__
                  for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 115, in _generate_examples
                  example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
              KeyError: 'jpg'

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This plant image dataset consists of 14,790 images categorized into 47 distinct plant species classes. The dataset was compiled by collecting images from Bing Images and manually curating them, although not by professional biologist. I collected this images for a project aimed at classifying plant species as either toxic or safe for cats.

Key Features:

  • Total Images: 14,790
  • Number of Classes: 47
  • Image Source: Collected from Bing Images
  • Curation Method: Manual cleaning by non-expert

Dataset Composition:

  • The number of images per class varies significantly, ranging from 66 (Yucca) to 547 (Monstera Deliciosa).
  • Some well-represented classes include Chinese evergreen (514 images), Dumb Cane (541 images), and Monstera Deliciosa (547 images).
  • Classes with fewer images include Yucca (66 images), Kalanchoe (130 images), and Asparagus Fern (169 images).

Image Characteristics:

  • Images vary in quality and resolution.
  • The dataset includes both whole plant images and close-ups of specific plant parts.
  • Plants are placed indoor and outdoors
  • Images are organized into separate folders for each plant category.

The current dataset is for personal use only due to copyright considerations

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