Dataset Versions
Versions
2023-05-07 3:56pm
v16
· 2 years ago
2023-05-07 3:53pm
v15
· 2 years ago
2023-05-06 6-50pm - Chip Isolation Original Dataset
v12
· 2 years ago
2023-05-06 1-12pm - Original Dataset
v11
· 2 years ago
2023-05-06 12-01pm - Chip Isolation
v7
· 2 years ago
2023-05-05 2-55am - Added Photos
v6
· 2 years ago
2023-04-09 10-23am - Preprocessing 3
v4
· 2 years ago
2023-04-07 8-37pm - Preprocessing 2
v3
· 2 years ago
2023-04-07 6-36pm - Preprocessing 1
v2
· 2 years ago
v3
2023-04-07 8-37pm - Preprocessing 2
Generated on Apr 8, 2023
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.
Other Formats
Choose another format.
289 Total Images
View All Images
Dataset Split
Train Set 87%
252Images
Valid Set 8%
23Images
Test Set 5%
14Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Grayscale: Applied
Filter Null: Require at least 50% of images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal
Rotation: Between -30° and +30°
Shear: ±30° Horizontal, ±30° Vertical
Hue: Between -180° and +180°
Brightness: Between -46% and +46%
Exposure: Between -40% and +40%
Blur: Up to 1.5px
Noise: Up to 5% of pixels
Cutout: 1 boxes with 10% size each