Cottontail-Rabbits Dataset Augmented Data
Export Created
4 years ago
2021-06-27 12:35am
Export Size
2009 images
Annotations
Cottontail-Rabbit
Available Download Formats
COCO JSON
COCO
JSON annotations are used with EfficientDet Pytorch and Detectron 2.
COCO-MMDetection
COCO-MMDetection JSON annotations are used with MMDetection.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create
tools.
PaliGemma
JSONL
PaliGemma format is used with Google's Multimodal Vision
Model.
Pascal VOC XML
Common XML annotation format for local data munging (pioneered by
ImageNet).
YOLO Darknet
TXT
Darknet TXT annotations used with YOLO Darknet (both v3 and
v4) and YOLOv3
PyTorch.
YOLO v3 Keras
TXT
TXT annotations used with YOLOv3
Keras.
YOLO v4
PyTorch
Darknet TXT annotations used with YOLOv4 PyTorch (deprecated).
MT-YOLOv6
MT-YOLOv6 TXT annotations used with meituan/YOLOv6.
YOLO v5
PyTorch
TXT annotations and YAML config used with YOLOv5.
YOLO v7
PyTorch
TXT annotations and YAML config used with YOLOv7.
YOLOv8
TXT annotations and YAML config used with YOLOv8.
YOLOv9
TXT annotations and YAML config used with YOLOv9.
YOLOv11
TXT annotations and YAML config used with YOLOv11.
YOLOv12 TXT
annotations and YAML config used with YOLOv12
Tensorflow
Object Detection CSV
CSV
format used with Tensorflow (usually converted before training so you probably want
to export as a TFRecord instead unless you need to inspect the human-readable CSV).
RetinaNet Keras
CSV
A custom CSV format used by Keras implementation of RetinaNet.
Multiclass
Classification
Converts your object detection dataset into a classification dataset CSV.
OpenAI CLIP
Classification
Converts your object detection dataset a classification dataset for
use with OpenAI
CLIP.
Tensorflow
TFRecord
TFRecord binary format used for both Tensorflow 1.5
and Tensorflow 2.0
Object Detection models.
Train/Test Split
Your images are split at upload time. Learn more.
Preprocessing Options
Applied to all images in dataset
Auto-Orient
Resize
Stretch to 416x416
Preprocessing can decrease training time and increase inference speed. Learn more on our blog.
Augmentation Output
Save
For each image in your training set, how many augmented versions do you want to generate?
(Max: 50)
Size: 2009 images.
Augmentation Options
Randomly applied to images in your training set
Saturation
Between -25% and +25%
Brightness
Between -25% and +25%
Exposure
Between -25% and +25%
Cutout
3 boxes with 10% size each
Augmentations create new training examples for your model to learn from. Learn more on our blog.