Open MrOCW opened 3 months ago
Hi @MrOCW,
coco-format
, refer to: https://cocodataset.org/#format-dataMixedGroundingDataset
to load your dataset. @wondervictor
coco_train_dataset = dict(
_delete_=True,
type='MultiModalDataset',
dataset=dict(
type='YOLOv5CocoDataset',
data_root='data/coco',
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=False, min_size=32)),
class_text_path='data/texts/coco_class_texts.json',
pipeline=train_pipeline)
What should be in class_text_path? Do i create a list of classes that matches the unique captions in my VLM data?
3. MixedGroundingDataset How about the num_classes, num_training_classes?whats the difference of them?I need the answer too
Hi @MrOCW,
- You need to organize your dataset into a
coco-format
, refer to: https://cocodataset.org/#format-data- You can add the caption for each box annotation.
- You need to use
MixedGroundingDataset
to load your dataset.
what is MixedGroundingDataset?In finetune config there is type='MultiModalDataset'
Hi, i'd like to fine tune YOLO-World on a custom dataset In this dataset, I have images, respective captions and bounding boxes matching the caption. e.g. image1 of multiple colored candies. caption: blue crystal candy. bbox = [x,y,w,h] image2 of multiple colored candies. caption: red sweet. bbox = [x,y,w,h] How should I structure my dataset, number of classes etc? Are they simply the number of unique captions in my training dataset?