Promptable segmentation (LibreSAM tier): point and box prompts, encode once and prompt many. Image inference; video is out of scope in v1.
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Recent Activity
COCO-pretrained D-FINE-seg weights (Apache-2.0, converted from ArgoSA/D-FINE-seg) for LibreYOLO task=segment.
Models released with the LibreYOLO use-cases repo (https://github.com/LibreYOLO/use-cases).
All LibreYOLO pretrained model weights
COCO dataset subsets for testing and validation
EoMT (Encoder-only Mask Transformer, DINOv2 backbone) semantic and instance segmentation weights for LibreYOLO.
Image-classification model families packaged for LibreYOLO (ImageNet-1k, Apache-2.0). MobileNetV4-conv and ConvNeXt V1.
RF-DETR segmentation models (Nano, Small, Medium, Large) for LibreYOLO. Based on the RF-DETR architecture with a MaskDINO-inspired segmentation head.
Roboflow 100 datasets for training and evaluation
Promptable segmentation (LibreSAM tier): point and box prompts, encode once and prompt many. Image inference; video is out of scope in v1.
EoMT (Encoder-only Mask Transformer, DINOv2 backbone) semantic and instance segmentation weights for LibreYOLO.
COCO-pretrained D-FINE-seg weights (Apache-2.0, converted from ArgoSA/D-FINE-seg) for LibreYOLO task=segment.
Image-classification model families packaged for LibreYOLO (ImageNet-1k, Apache-2.0). MobileNetV4-conv and ConvNeXt V1.
Models released with the LibreYOLO use-cases repo (https://github.com/LibreYOLO/use-cases).
RF-DETR segmentation models (Nano, Small, Medium, Large) for LibreYOLO. Based on the RF-DETR architecture with a MaskDINO-inspired segmentation head.
All LibreYOLO pretrained model weights
Roboflow 100 datasets for training and evaluation
COCO dataset subsets for testing and validation