Tasks and inputs
facebook/dinov3-vitb16-pretrain-lvd1689m is listed for Image feature extraction. It accepts image and produces embeddings. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
facebook/dinov3-vitb16-pretrain-lvd1689m is a source-linked Hugging Face repository assigned to Image feature extraction. Its recorded task interface accepts image and produces embeddings.
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/dinov3-vitb16-pretrain-lvd1689m is a Hugging Face repository returned by the public Image feature extraction task feed. Its recorded download count is 761,236 and it has 203 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 Image feature extraction. 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, dinov3_vit, image-feature-extraction, dino, dinov3, arxiv:2508.10104, en. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Confirm tensor shape, pooling and normalization in the runtime you will use. Evaluate the downstream task on held-out data and compare memory and throughput at realistic batch sizes.
The Hugging Face task assignment records image as input and embeddings as output for facebook/dinov3-vitb16-pretrain-lvd1689m. This interface summary comes from the repository’s recorded Image feature extraction 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 image feature extraction collection. Use the comparison tool to inspect a second candidate.
Compare the model on the work you need to complete.
facebook/dinov3-vitb16-pretrain-lvd1689m is listed for Image feature extraction. It accepts image and produces embeddings. 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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camenduru/dinov3-vitl16-pretrain-lvd1689m is a source-linked Hugging Face repository assigned to Image feature extraction. Its recorded task interface accepts image and produces embeddings.
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facebook/dinov2-base is a source-linked Hugging Face repository assigned to Image feature extraction. Its recorded task interface accepts image and produces embeddings.
facebook/dinov2-giant is a source-linked Hugging Face repository assigned to Image feature extraction. Its recorded task interface accepts image and produces embeddings.