Download 170 Townsends warbler labeled images
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170 images·ZIP download·No signup required·~87 KB
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Train / val / test split
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Make your dataset more diverse by applying random transformations.
Note: ~30% of your train images will be augmented. For each one, one random augmentation is picked from your selection — so you can safely "check all" without stacking effects and ruining the image.
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Cite this dataset (BibTeX)
@misc{imagescv_townsends_warbler_2026,
title = {Townsends warbler Image Classification Dataset},
author = {{images.cv}},
year = {2026},
howpublished = {\url{https://images.cv/dataset/townsends_warbler-image-classification-dataset}},
note = {Labeled image dataset for computer vision research.}
}What's in your ZIP
images.cv_townsends_warbler/
├── meta.json # labels, split counts, augmentation info, ImageNet normalization
└── data/
├── train/ # ~70% of images
│ └── <class>/*.jpg
├── val/ # ~15% of images
│ └── <class>/*.jpg
└── test/ # ~15% of images
└── <class>/*.jpgQuick-start (PyTorch)
# Install
pip install torch torchvision
# Load the dataset (PyTorch ImageFolder)
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
])
train_ds = datasets.ImageFolder('./townsends_warbler/data/train', transform=transform)
val_ds = datasets.ImageFolder('./townsends_warbler/data/val', transform=transform)
train_loader = DataLoader(train_ds, batch_size=16, shuffle=True)
val_loader = DataLoader(val_ds, batch_size=16)
print(f'Classes: {train_ds.classes}')
print(f'Train: {len(train_ds)} images')
print(f'Val: {len(val_ds)} images')Suggested training settings
16
3e-4
60–100
Small dataset — rely on heavy augmentation + a pretrained backbone, and watch for overfitting on val.
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