Datasets:

LibreYOLO
/

pests-2xlvx

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
text: string
image: null
label: null
to
{'image': Image(mode=None, decode=True), 'label': Value('string')}
because column names don't match
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 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              text: string
              image: null
              label: null
              to
              {'image': Image(mode=None, decode=True), 'label': Value('string')}
              because column names don't match

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Pests 2Xlvx

This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains.

Dataset Statistics

Split	Images
Train	509
Validation	153
Test	55
Total	717

Classes (28)

Agrotis
Athetis lepigone
Athetis lineosa
Chilo suppressalis
Cnaphalocrocis medinalis Guenee
Creatonotus transiens
Diaphania indica
Endotricha consocia
Euproctis sparsa
Gryllidae
Gryllotalpidae
Helicoverpa armigera
Holotrichia oblita Faldermann
Loxostege sticticalis
Mamestra brassicae
Maruca testulalis Geyer
Mythimna separata
Naranga aenescens Moore
Nilaparvata
Paracymoriza taiwanalis
Sesamia inferens
Sirthenea flavipes
Sogatella furcifera
Spodoptera exigua
Spoladea recurvalis
Staurophora celsia
Timandra Recompta
Trichoptera

Usage

With LibreYOLO

from libreyolo import LIBREYOLO

# Load a model
model = LIBREYOLO(model_path="libreyoloXnano.pt")

# Train on this dataset
model.train(data='path/to/data.yaml', epochs=100)

Download from HuggingFace

from huggingface_hub import snapshot_download

# Download the dataset
snapshot_download(
    repo_id="Libre-YOLO/pests-2xlvx",
    repo_type="dataset",
    local_dir="./pests-2xlvx"
)

Directory Structure

pests-2xlvx/
├── data.yaml           # Dataset configuration
├── README.md           # This file
├── train/
│   ├── images/         # Training images
│   └── labels/         # Training labels (YOLO format)
├── valid/
│   ├── images/         # Validation images
│   └── labels/         # Validation labels
└── test/
    ├── images/         # Test images (if available)
    └── labels/         # Test labels

Label Format

Labels are in YOLO format (one .txt file per image):

<class_id> <x_center> <y_center> <width> <height>

All coordinates are normalized to [0, 1].

Citation

If you use this dataset, please cite the Roboflow 100 benchmark:

@misc{rf100_2022,
    Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
    Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
    Year = {2022},
    Eprint = {arXiv:2211.13523},
}