Dataset Versions
Versions
Modelo F-200
v26
· 8 months ago
Modelo D-200
v25
· 8 months ago
Modelo H
v24
· 8 months ago
Modelo G
v23
· 8 months ago
Modelo F
v22
· 8 months ago
Modelo E
v21
· 8 months ago
Modelo D
v20
· 8 months ago
Modelo C
v19
· 8 months ago
MODELO B
v18
· 8 months ago
MODELO A
v17
· 8 months ago
Modelo 8 - 2024-04-10 6-49pm - 100 Epochs
v12
· 8 months ago
Modelo 7- 2024-04-07 7-00pm - NO ARG. 2.5 IMG- 23K Classes
v11
· 8 months ago
Modelo 6- 2024-04-07 6-51pm - 3.5 Images - 12k Classes - ARG. Blur 3px- Cutout 10 10-
v10
· 8 months ago
Modelo 5- 2024-04-04 8-11pm - WITH ARGS - BLUR AND CUTOFF
v8
· 9 months ago
Modelo 4- 2024-04-04 6-05pm - 1.5k Images - 8K Classes - NO ARG
v7
· 9 months ago
2024-04-02 3-08pm - Test With Blur
v5
· 9 months ago
2024-04-02 3-03pm - Test With 604 Images-
v3
· 9 months ago
2024-04-01 11-25am - TEST of Bounding Box
v2
· 9 months ago
v10
Modelo 6- 2024-04-07 6-51pm - 3.5 Images - 12k Classes - ARG. Blur 3px- Cutout 10 10-
Generated on Apr 7, 2024
Popular Download Formats
YOLOv11
TXT annotations and YAML config used with YOLOv11.
YOLOv9
TXT annotations and YAML config used with YOLOv9.
YOLOv8
TXT annotations and YAML config used with YOLOv8.
YOLOv5
TXT annotations and YAML config used with YOLOv5.
YOLOv7
TXT annotations and YAML config used with YOLOv7.
COCO JSON
COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.
YOLO Darknet
Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.
Pascal VOC XML
Common XML annotation format for local data munging (pioneered by ImageNet).
TFRecord
TFRecord binary format used for both Tensorflow 1.5 and Tensorflow 2.0 Object Detection models.
PaliGemma
PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create tools.
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6134 Total Images
View All Images
Dataset Split
Train Set 87%
5364Images
Valid Set 8%
514Images
Test Set 4%
256Images
Preprocessing
Auto-Orient: Applied
Static Crop: 10-90% Horizontal Region, 10-90% Vertical Region
Augmentations
Outputs per training example: 3
Blur: Up to 3px
Cutout: 10 boxes with 10% size each