Dataset Viewer
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: ArrowInvalid
Message: Mismatching child array lengths
Traceback: 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 2543, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2083, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 87, in _generate_tables
pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 294, in _recursive_load_arrays
sarr = pa.StructArray.from_arrays(values, names=keys)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 4294, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Mismatching child array lengthsNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Project Documentation: Identification of Medicinal Plants/Raw Materials through Image Processing Using Machine Learning
Ministry of AYUSH - Student Innovation Hackathon (SIH1343)
Project Overview:
Our project aims to address the challenge of accurately identifying medicinal plant species and raw materials through the use of a web application. Leveraging image processing and machine learning algorithms, the application allows users to upload or capture images of plant specimens, providing precise identification and detailed information relevant to the field of medicine.
Technology Stack:
- Python: 3.11.5
- Flask Framework: 2.3.3
- Plant Species & Raw Materials Image Dataset
- Visual Studio Code: 1.82
- Firebase Database: 12.5.1
- Google Colaboratory
Approach:
Our sophisticated solution involves a robust system that processes images through advanced algorithms. The key steps include:
- Image Input: Users can upload or capture images using the mobile app.
- Image Processing: Advanced image processing algorithms examine the morphological attributes of the images.
- Machine Learning: The system employs machine learning algorithms for accurate classification of plant species and raw materials.
Use Cases:
- Image Upload/Capture: Users can upload images or capture photos of plant species or raw materials.
- Species Identification: Advanced image recognition technology identifies the species, providing comprehensive information for medicinal purposes.
Features:
- User-Friendly UI: The mobile application boasts a thoughtfully designed interface for ease of use.
- Text-to-Speech Functionality: Manual activation of text-to-speech enhances accessibility for users with hearing impairments or physical challenges.
- Cross-Domain Applicability: Valuable for Ayurvedic practitioners, paramedical personnel, biomedical experts, scientists, botanists, and students.
Dependencies/Show Stoppers:
- Web Application: Seamlessly integrated for convenient access and user-friendliness.
- Text-to-Speech Functionality: Enhances accessibility and usability.
- Versatility: Applicable across various domains, facilitating research, identification, and learning.
Getting Started:
- Clone the repository:
git clone [repository_url] - Install dependencies:
pip install -r requirements.txt - Open in Visual Studio Code:
code . - Run the Flask app:
python app.py - Access the application through the provided URL.
Future Enhancements:
- Integration of additional image datasets for expanded plant species recognition.
- Continuous refinement of machine learning algorithms for improved accuracy.
- Inclusion of real-time collaboration features in the research repository.
GitHub Link: Identification of Medicinal Plants/Raw Materials through Image Processing Using Machine Learning
Conclusion:
The Identification of Medicinal Plants/Raw Materials through Image Processing Using Machine Learning project is a powerful tool in the field of MedTech/BioTech/HealthTech. Its potential applications in Ayurvedic research, pharmaceuticals, and collaborative learning make it a valuable asset for professionals and enthusiasts alike.
Acknowledgments:
We extend our gratitude to the Ministry of AYUSH and the Student Innovation Hackathon for providing the platform and support for this innovative project.
Feel free to contribute, report issues, or suggest improvements. Happy coding!
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