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Segment Anything Alternatives
Explore alternatives to the Segment Anything segmentation model.
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Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using
Roboflow Inference
.
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models.
Segment Anything Model (SAM)
Segment Anything (SAM) is an image segmentation model developed by Meta Research, capable of doing zero-shot segmentation.
Instance Segmentation
Deploy with Roboflow
YOLOv8 Instance Segmentation
The state-of-the-art YOLOv8 model comes with support for instance segmentation tasks.
Instance Segmentation
Deploy with Roboflow
Segment Anything 2
Segment Anything 2 (SAM 2) is a real-time image and video segmentation model.
Instance Segmentation
Deploy with Roboflow
YOLOv5 Instance Segmentation
YOLOv5 Instance Segmentation is a version of YOLOv5 that can be used for instance segmentation tasks.
Instance Segmentation
Deploy with Roboflow
Mask RCNN
Mask RCNN is a convolutional neural network for instance segmentation.
Instance Segmentation
Deploy with Roboflow
YOLOv7 Instance Segmentation
YOLOv7 Instance Segmentation lets you perform segmentation tasks with the YOLOv7 model.
Instance Segmentation
Deploy with Roboflow
FastSAM
FastSAM is an image segmentation model trained using 2% of the data in the Segment Anything Model SA-1B dataset.
Instance Segmentation
Deploy with Roboflow
YOLACT
A simple, fully convolutional model for real-time instance segmentation
Instance Segmentation
Deploy with Roboflow
DETIC
Detic is an open source segmentation model developed by Meta Research and released in 2022.
Instance Segmentation
Deploy with Roboflow
OneFormer
OneFormer is a state-of-the-art multi-task image segmentation framework that is implemented using transformers.
Instance Segmentation
Deploy with Roboflow
SAM-CLIP
Use Grounding DINO, Segment Anything, and CLIP to label objects in images.
Instance Segmentation
Deploy with Roboflow
Visual Question Answering
Image Tagging
Image Similarity
Image Captioning
Zero-shot Detection
Real-Time Vision
Image Embedding
LLMS with Vision Capabilities
Multimodal Vision
Foundation Vision