Invoice01 Image Dataset
Versions
2023-09-01 6:08pm
v34
Sep 1, 2023
2023-09-01 5:34pm
v33
Sep 1, 2023
2022-11-22 10:20pm
v32
Nov 22, 2022
2022-11-15 5:36pm
v31
Nov 15, 2022
2022-11-08 3:10pm
v30
Nov 8, 2022
2022-10-07 10:51am
v29
Oct 7, 2022
2022-10-06 2:23pm
v28
Oct 6, 2022
2022-10-05 5:03pm
v27
Oct 5, 2022
2022-09-19 12:18am
v26
Sep 18, 2022
2022-09-18 2:33pm
v25
Sep 18, 2022
2022-09-17 10:48am
v24
Sep 17, 2022
2022-09-16 6:11pm
v23
Sep 16, 2022
2022-09-14 6:41pm
v22
Sep 14, 2022
2022-08-10 6:07pm
v21
Aug 10, 2022
2022-08-10 11:13am
v20
Aug 10, 2022
2022-08-10 10:40am
v19
Aug 10, 2022
2022-05-31 5:36pm
v18
May 31, 2022
Invoice 11
v17
May 27, 2022
Invoice 10
v16
May 26, 2022
Invoice 9
v15
May 23, 2022
Invoice 8
v14
May 23, 2022
Invoice 7
v13
May 16, 2022
Invoice 6
v12
May 16, 2022
Invoice 6
v11
May 16, 2022
Invoice 5
v10
May 16, 2022
Invoice 4
v9
May 16, 2022
Invoice 3
v3
May 13, 2022
Invoice 2022-05-13
v2
May 13, 2022
INVOICE01
v1
May 11, 2022
v23
2022-09-16 6:11pm
Generated on Sep 16, 2022
Popular Download Formats
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.
936 Total Images
View All Images
Dataset Split
Train Set 87%
818Images
Valid Set 8%
73Images
Test Set 5%
45Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Modify Classes: 5 remapped, 6 dropped
Augmentations
Outputs per training example: 3
Brightness: Between -15% and +15%
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