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AI 모델 84759

• asr4all-s↗

  futo-org

• multiberts-seed_1-step_600k↗

  Google

• owlv2-large-patch14-ensemble↗

  Google

  The OWLv2 model (short for Open-World Localization) was proposed in Scaling Open-Vocabulary Object Detection by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. The model uses a CLIP backbone with a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective.

• test-patchtsmixer↗

  ibm-research

• LingBot-World-V2-14B-Causal-Fast-NVFP4↗

  igorls

• soc-triage-nemotron-bolt-e4b-lora-gguf↗

  infosecn1nja

• MamayLM-Gemma-3-12B-IT-v1.0↗

  INSAIT-Institute

• internlm2-wqx-20b↗

  internlm

• Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF↗

  jackasda211233

• Mellum2-12B-A2.5B-Base-Pretrain↗

  JetBrains