Tasks and inputs
PekingU/rtdetr_r101vd_coco_o365 is listed for Object detection. It accepts image and produces bounding boxes. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
PekingU/rtdetr_r101vd_coco_o365 is a source-linked Hugging Face repository assigned to Object detection. Its recorded task interface accepts image and produces bounding boxes.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
PekingU/rtdetr_r101vd_coco_o365 is a Hugging Face repository returned by the public Object detection task feed. Its recorded download count is 405,736 and it has 19 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Object detection. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists transformers as its library metadata. Its recorded tags include transformers, safetensors, rt_detr, object-detection, vision, en, dataset:coco, arxiv:2304.08069. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Measure missed objects, false positives and localization at several IoU thresholds. Include small, overlapping and partially hidden objects; record input resolution and preprocessing.
The Hugging Face task assignment records image as input and bounding boxes as output for PekingU/rtdetr_r101vd_coco_o365. This interface summary comes from the repository’s recorded Object detection task classification. It helps readers distinguish a text, image, audio, video, document, embedding, or prediction workflow without inferring undocumented API behavior. Model wrappers can expose different preprocessing and response formats, so verify the upstream model card and the exact runtime before deployment.
Explore PekingU models and the object detection collection. Use the comparison tool to inspect a second candidate.
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PekingU/rtdetr_r101vd_coco_o365 is listed for Object detection. It accepts image and produces bounding boxes. Use the publisher’s input format and preprocessing for your chosen deployment.
Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
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