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fruits-and-vegetables-detection-lrus9-k2yka/4 (latest)
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fruits-and-vegetables-detection-lrus9
Object Detection
Overview
Images
2271
Dataset
4
Model
2
API Docs
Analytics
Dataset Versions
Try Pre-Trained Model
Versions
2024-09-06 3:50pm
v5
·
7 months ago
2024-09-01 1:19pm
v4
·
7 months ago
2024-09-01 12:36pm
v3
·
7 months ago
2024-08-31 12:36am
v1
·
7 months ago
v5
2024-09-06 3:50pm
Generated on Sep 6, 2024
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Dataset
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YOLOv11
TXT annotations and YAML config
used with
YOLOv11
.
YOLOv9
TXT annotations and YAML config
used with
YOLOv9
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YOLOv8
TXT annotations and YAML config
used with
YOLOv8
.
YOLOv5
TXT annotations and YAML config
used with
YOLOv5
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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
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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
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3753
Total Images
View All Images
Dataset Split
Train Set
79%
2964
Images
Valid Set
13%
497
Images
Test Set
8%
292
Images
Preprocessing
Auto-Orient:
Applied
Resize:
Fill (with center crop) in 640x640
Augmentations
Outputs per training example:
2
Shear:
±13° Horizontal, ±13° Vertical
Saturation:
Between -19% and +19%
Brightness:
Between -23% and +23%
Exposure:
Between -11% and +11%
Blur:
Up to 1.6px
Noise:
Up to 1.99% of pixels
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