Datasets:

gfermoto
/

BirdLense_Detector

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Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
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                  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))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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                                      ^^^^^^^^^^^
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                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
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                        ^^^^^^^^^^^^^^^^^^^^^
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BirdLense Detector Dataset (Bird / Rodent / Background)

Overview

This dataset is prepared for training a YOLO detector for BirdLense real-world deployment scenarios:

feeder cameras
tree-mounted cameras
different weather/lighting conditions
mixed regions and bird species

Target classes:

0: Bird
1: Rodent (mostly squirrels + mice)
2: Background

The dataset is provided in YOLO detection format (images/ + labels/*.txt).

Included splits

train/
val/
dataset.yaml

YOLO label format: <class_id> <x_center> <y_center> <width> <height> (normalized to [0, 1]).

Data sources and curation

Data was assembled from:

local BirdLense detector pool
public open datasets and repositories
Open Images / COCO-derived subsets
curated hard negatives for background scenes

Curation and QA steps:

hash-based deduplication
train/val leakage checks
corrupt sample filtering
label sanity validation (class bounds, normalization, bbox shape)

Intended use

Wildlife object detection for BirdLense pipelines
Bird/Rodent/background filtering before species-level classification
Real-world monitoring in varied environmental conditions

Limitations

Rodent class still has domain bias (squirrel/mouse dominant)
Geographic and seasonal coverage is broad but not exhaustive
Background class may include hard edge cases (tiny distant animals)

Ethics and privacy

Use in compliance with local laws and privacy rules
Do not deploy in sensitive/private areas without proper consent

Training recommendation

Recommended two-stage training:

Stage A (stability): train on balanced subset
Stage B (diversity): fine-tune on full merged set

Typical baseline:

model: yolo11n or yolo11s
image size: 960

Citation

@dataset{birdlense_detector_dataset_2026,
  title={BirdLense Detector Dataset (Bird/Rodent/Background)},
  author={BirdLense contributors},
  year={2026},
  publisher={Hugging Face}
}

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