Model Detail
clip-vit-base-patch32
—clip-vit-base-patch32 is an AI model released by OpenAI. The model is registered under the zero-shot-image-classification pipeline tag on Hugging Face.
clip-vit-base-patch32 is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
clip-vit-base-patch32 is best fit for workloads that match the zero-shot-image-classification pipeline tag. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning
arXiv:2511.15633v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) enables models to learn new classes continually while preserving past knowledge. Recently, vision-language models like CLIP offer transferable features via multimodal pre-training, making them well-suited for
RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods
arXiv:2507.22075v2 Announce Type: replace Abstract: In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shifts or in visually complex scenarios. Conventional pseudo-label filte
Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder
arXiv:2610.11374v2 Announce Type: replace-cross Abstract: CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a lightweight route to refine CLIP, but recent work argues that the standard contrastive loss is unsui
Visual Branch is What You Need for CLIP-based Class-Incremental Learning
arXiv:2609.37888v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) requires models to recognize new classes over time without forgetting previously learned ones. With the rise of vision-language pre-training, CLIP has become a strong foundation for CIL. A common design in CLI
FoCLIP: A Feature-Space Misalignment Framework for CLIP-Based Image Manipulation and Detection
arXiv:2511.06947v2 Announce Type: replace-cross Abstract: The well-aligned attribute of CLIP-based models enables its effective application like CLIPscore as a widely adopted image quality assessment metric. However, such a CLIP-based metric is vulnerable for its delicate multimodal alignment. In th
