Hugging Face's logo

Datasets:
TSY-0408
/
Plant_Detection_Classification

Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
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:    HfHubHTTPError
Message:      404 Client Error: Not Found for url: https://cas-bridge-direct.xethub.hf.co/xet-bridge-us/69e1ffce96382320aed995a2/812f07caa429303fba6eaa06d1731535aab8d7a8f725f317a617c50b0507a16b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20260417%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20260417T122112Z&X-Amz-Expires=3600&X-Amz-Signature=c183c2353c24cce25532dcaf74d55d0f3c8d6ac8d468e6255d345f59d7587b89&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=app%3A6241c288797aadd4ac9dd1a9&response-content-disposition=inline%3B%20filename%2A%3DUTF-8%27%270038b49e0352646885a8899be350813d927f34a5.jpg%3B%20filename%3D%220038b49e0352646885a8899be350813d927f34a5.jpg%22%3B&response-content-type=image%2Fjpeg&x-amz-checksum-mode=ENABLED&x-id=GetObject

request_id: 01KPDP6PA75NNB5N6WGD85YZE0; (1) not found
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
                  response.raise_for_status()
                File "/usr/local/lib/python3.12/site-packages/requests/models.py", line 1026, in raise_for_status
                  raise HTTPError(http_error_msg, response=self)
              requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://cas-bridge-direct.xethub.hf.co/xet-bridge-us/69e1ffce96382320aed995a2/812f07caa429303fba6eaa06d1731535aab8d7a8f725f317a617c50b0507a16b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20260417%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20260417T122112Z&X-Amz-Expires=3600&X-Amz-Signature=c183c2353c24cce25532dcaf74d55d0f3c8d6ac8d468e6255d345f59d7587b89&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=app%3A6241c288797aadd4ac9dd1a9&response-content-disposition=inline%3B%20filename%2A%3DUTF-8%27%270038b49e0352646885a8899be350813d927f34a5.jpg%3B%20filename%3D%220038b49e0352646885a8899be350813d927f34a5.jpg%22%3B&response-content-type=image%2Fjpeg&x-amz-checksum-mode=ENABLED&x-id=GetObject
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2159, in _apply_feature_types_on_example
                  decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2204, in decode_example
                  column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 189, in decode_example
                  bytes_ = BytesIO(f.read())
                                   ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                        ^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 1012, in read
                  out = f.read()
                        ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 1078, in read
                  hf_raise_for_status(self.response)
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 482, in hf_raise_for_status
                  raise _format(HfHubHTTPError, str(e), response) from e
              huggingface_hub.errors.HfHubHTTPError: 404 Client Error: Not Found for url: https://cas-bridge-direct.xethub.hf.co/xet-bridge-us/69e1ffce96382320aed995a2/812f07caa429303fba6eaa06d1731535aab8d7a8f725f317a617c50b0507a16b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20260417%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20260417T122112Z&X-Amz-Expires=3600&X-Amz-Signature=c183c2353c24cce25532dcaf74d55d0f3c8d6ac8d468e6255d345f59d7587b89&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=app%3A6241c288797aadd4ac9dd1a9&response-content-disposition=inline%3B%20filename%2A%3DUTF-8%27%270038b49e0352646885a8899be350813d927f34a5.jpg%3B%20filename%3D%220038b49e0352646885a8899be350813d927f34a5.jpg%22%3B&response-content-type=image%2Fjpeg&x-amz-checksum-mode=ENABLED&x-id=GetObject
              
              request_id: 01KPDP6PA75NNB5N6WGD85YZE0; (1) not found

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Plant Species Classification Dataset

A comprehensive dataset containing 64 different plant species with high-quality images for machine learning and computer vision applications. Model Trainig code and trainned model with detailed performance analysis is present on the Github https://github.com/jameelkhalidawan/Plant-Detection-Model-using-Yolo

Dataset Overview

This dataset is designed for plant species classification tasks and contains images of various plant species organized in a structured format suitable for training deep learning models.

Key Statistics

  • Total Classes: 64 plant species
  • Total Images: 152,042 images
  • Image Format: JPG
  • Dataset Split:
    • Training: 106,395 images
    • Validation: 22,779 images
    • Test: 22,868 images

Dataset Structure 

Plants_Datadet/
├── train/                    # Training data (106,395 images)
│   ├── [Plant Species 1]/    # Each folder contains images of one species
│   ├── [Plant Species 2]/
│   └── ...
├── val/                      # Validation data (22,779 images)
│   ├── [Plant Species 1]/
│   ├── [Plant Species 2]/
│   └── ...
├── test/                     # Test data (22,868 images)
│   ├── [Plant Species 1]/
│   ├── [Plant Species 2]/
│   └── ...
├── train_split/              # Additional split for training (generated by training code)
│   ├── train/
│   ├── val/
│   ├── train.cache
│   └── val.cache
└── image_counts.xlsx         # Detailed image count statistics

Plant Species Included

The dataset contains 64 diverse plant species including:

Trees and Shrubs

  • Acacia dealbata Link
  • Liriodendron tulipifera L
  • Nandina domestica Thunb
  • Pyracantha coccinea M.Roem
  • Schefflera arboricola (Hayata) Merr
  • Smilax aspera L
  • Trachelospermum jasminoides (Lindl.) Lem
  • Zamioculcas zamiifolia (Lodd.) Engl

Herbs and Wildflowers

  • Aegopodium podagraria L
  • Anemone alpina L
  • Anemone hepatica L
  • Anemone hupehensis (Lemoine) Lemoine
  • Anemone nemorosa L
  • Angelica sylvestris L
  • Barbarea vulgaris R.Br
  • Cirsium arvense (L.) Scop
  • Cirsium vulgare (Savi) Ten
  • Cymbalaria muralis P.Gaertn., B.Mey. & Scherb
  • Dryopteris filix-mas (L.) Schott
  • Epipactis helleborine (L.) Crantz
  • Fragaria vesca L
  • Helminthotheca echioides (L.) Holub
  • Humulus lupulus L
  • Hypericum androsaemum L
  • Hypericum calycinum L
  • Kniphofia uvaria (L.) Hook
  • Lactuca serriola L
  • Lamium album L
  • Lamium galeobdolon (L.) L
  • Lamium maculatum (L.) L
  • Lamium purpureum L
  • Lapsana communis L
  • Lupinus polyphyllus Lindl
  • Melilotus albus Medik
  • Mercurialis annua L
  • Nymphaea alba L
  • Ophrys apifera Huds
  • Pancratium maritimum L
  • Papaver rhoeas L
  • Papaver somniferum L
  • Perovskia atriplicifolia Benth
  • Trifolium incarnatum L

Succulents and Sedums

  • Sedum acre L
  • Sedum album L
  • Sedum rupestre L
  • Sedum sediforme (Jacq.) Pau

Ornamental Plants

  • Anthurium andraeanum Linden ex André
  • Fittonia albivenis (Lindl. ex Veitch) Brummitt
  • Lavandula angustifolia Mill
  • Lavandula stoechas L
  • Pelargonium graveolens L'Hér
  • Pelargonium inquinans (L.) Aiton
  • Pelargonium zonale (L.) L'Hér
  • Pelargonium zonale (L.) L'Hér. ex Aiton
  • Punica granatum L
  • Tagetes erecta L
  • Tagetes patula L

Vegetables and Fruits

  • Cucurbita maxima Duchesne
  • Cucurbita pepo L

Tradescantia Varieties

  • Tradescantia fluminensis Vell
  • Tradescantia pallida (Rose) D.R.Hunt
  • Tradescantia spathacea Sw
  • Tradescantia virginiana L
  • Tradescantia zebrina Bosse

Image Characteristics

  • Format: JPG
  • Naming Convention: Images use hash-based filenames (e.g., 0038b49e0352646885a8899be350813d927f34a5.jpg)
  • Quality: High-quality images suitable for detailed plant identification
  • Content: Various parts of plants including flowers, leaves, stems, and full plant views

Usage

This dataset is suitable for:

  1. Plant Species Classification: Train models to identify and classify different plant species
  2. Computer Vision Research: Develop and test image classification algorithms
  3. Botanical Studies: Analyze plant characteristics and features
  4. Educational Applications: Create learning tools for plant identification
  5. Agricultural Applications: Assist in crop and weed identification

Dataset Splits

The dataset is pre-split into training, validation, and test sets to ensure proper model evaluation:

  • Training Set: Used for model training and parameter optimization
  • Validation Set: Used for hyperparameter tuning and model selection
  • Test Set: Used for final model evaluation and performance assessment

Additional Files

  • image_counts.xlsx: Contains detailed statistics about the number of images per class
  • train_split/: Contains additional splits generated during the training process with cache files for faster data loading

Citation

If you use this dataset in your research or projects, please cite it appropriately and acknowledge the contributors.

License

Please check the license terms before using this dataset for commercial purposes.

This dataset provides a comprehensive collection of plant species images suitable for various machine learning and computer vision applications in botany, agriculture, and environmental studies.

Downloads last month
71,041
Total file size:
51.2 GB