Tasks and inputs
facebook/PE-Core-S16-384 is listed for Zero-shot image classification. It accepts image, text and produces label. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
facebook/PE-Core-S16-384 is a source-linked Hugging Face repository assigned to Zero-shot image classification. Its recorded task interface accepts image, text and produces label.
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
facebook/PE-Core-S16-384 is a Hugging Face repository returned by the public Zero-shot image classification task feed. Its recorded download count is 734,682 and it has 0 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 Zero-shot image classification. 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 perception-encoder as its library metadata. Its recorded tags include perception-encoder, zero-shot-image-classification, arxiv:2504.13181, license:apache-2.0, region:us. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Use held-out images that resemble deployment traffic, including lighting changes and confusing classes. Examine per-class precision and recall and confidence calibration rather than relying only on top-line accuracy.
The Hugging Face task assignment records image, text as input and label as output for facebook/PE-Core-S16-384. This interface summary comes from the repository’s recorded Zero-shot image classification 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 facebook models and the zero-shot image classification collection. Use the comparison tool to inspect a second candidate.
Compare the model on the work you need to complete.
facebook/PE-Core-S16-384 is listed for Zero-shot image classification. It accepts image, text and produces label. 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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