Tasks and inputs
usyd-community/vitpose-base-coco-aic-mpii is listed for Keypoint detection. It accepts image and produces keypoints. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
usyd-community/vitpose-base-coco-aic-mpii is a source-linked Hugging Face repository assigned to Keypoint detection. Its recorded task interface accepts image and produces keypoints.
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
usyd-community/vitpose-base-coco-aic-mpii is a Hugging Face repository returned by the public Keypoint detection task feed. Its recorded download count is 2,549 and it has 1 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 Keypoint 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, vitpose, keypoint-detection, en, arxiv:2204.12484, license:apache-2.0, endpoints_compatible. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Use inputs that resemble deployment traffic, including difficult and out-of-domain cases. Compare output correctness, the response format, latency and memory requirements with a simple baseline under the same conditions.
The Hugging Face task assignment records image as input and keypoints as output for usyd-community/vitpose-base-coco-aic-mpii. This interface summary comes from the repository’s recorded Keypoint 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 usyd-community models and the keypoint detection collection. Use the comparison tool to inspect a second candidate.
Compare the model on the work you need to complete.
usyd-community/vitpose-base-coco-aic-mpii is listed for Keypoint detection. It accepts image and produces keypoints. 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.
akore
akore/rtmw-l-256x192 is a source-linked Hugging Face repository assigned to Keypoint detection. Its recorded task interface accepts image and produces keypoints.
amirmmahdavikia
Hugging Face repository for Keypoint detection.
ETH-CVG
Hugging Face repository for Keypoint detection.
ETH-CVG
ETH-CVG/lightglue_superpoint is a source-linked Hugging Face repository assigned to Keypoint detection. Its recorded task interface accepts image and produces keypoints.
facebook/sapiens2-pose-0.4b is a source-linked Hugging Face repository assigned to Keypoint detection. Its recorded task interface accepts image and produces keypoints.
facebook/sapiens2-pose-0.8b is a source-linked Hugging Face repository assigned to Keypoint detection. Its recorded task interface accepts image and produces keypoints.