ContainersNVIDIADeepSeek-R1NVIDIALlama-3.1-Nemotron-70B-InstructNVIDIAPyTorch
CollectionsNVIDIAOmniverse Kit (FB)NVIDIADeepStream SDKNVIDIAOmniverse Kit App Streaming
ModelsNVIDIAStyleGAN3 pretrained modelsNVIDIAPeopleNetNVIDIATrafficCamNet
ResourcesNVIDIARiva Skills Quick Start
Helm ChartsNVIDIAGPU OperatorNVIDIANVIDIA NIM Operator
NVIDIA
NVIDIA
NVOneFormer Commercial
Model
NVIDIA
NVIDIA
NVOneFormer Commercial

OneFormer - a unified AI model for multiple image segmentation tasks - trained on commercial data.

This model is backed by NVIDIA's Plus Plus (++) Promise
to learn more about the quality of the datasets used to train this model.
FieldResponse
Intended Task/Domain:Segmentation (specifically instance, semantic, and panoptic)
Model Type:Transformer (Masked-attention Mask Transformer)
Intended Users:Generative AI creators working with conversational AI models and image content.
Output:Label, Mask and Score for each detected object in the input image.
Describe how the model works:The model takes an RGB image as input. A backbone feature extractor (like a Swin Transformer) creates image features. These features are fed into a transformer decoder that uses masked attention. This attention mechanism extracts localized features by constraining cross-attention within predicted mask regions, allowing it to output a set of segmentation masks and their associated class labels and confidence scores.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:Not Applicable
Technical Limitations & Mitigation:This model is designed for use with the NVIDIA TAO Toolkit and requires an NVIDIA GPU with sufficient memory (e.g., >12GB). Like many segmentation models, its performance may degrade on object classes that were under-represented in its training data (e.g., unusual objects or scenarios). Mitigation involves fine-tuning the model on a custom dataset that includes more examples of these under-represented classes.
Verified to have met prescribed NVIDIA quality standards:Yes
Performance Metrics:mean Intersection over Union (mIoU)
Potential Known Risks:The model may inaccurately segment objects or fail to detect them entirely, especially if they are small, heavily occluded, or belong to a class not well-represented in the training data. This could lead to incorrect object counting, identification, or area measurement in a downstream application.
Licensing:Use of this model is governed by the NVIDIA Open Model Agreement