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AI 모델 84759

• Ninja-v1-RP-expressive-v2-GGUF↗

  Aratako

• prabhasa-nyaya-element-judge-4b-arm0↗

  AxisMeru

• Acestep1.5-qinglong-lokr↗

  bdsqlsz

• octocoder↗

  bigcode

• higgs-audio-v3-8b-stt↗

  bosonai

• MegaDescriptor-T-CNN-288↗

  BVRA

  A EfficientNetB3 based image feature model. Supervisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Architecture: EfficientNetB3 - Params (M): 12.2 - GMACs: 1.6 - Activations (M): 21.5 - Image size: 288 x 288 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets

• twitter-roberta-large-topic-sentiment-latest↗

  cardiffnlp

• vit-Facial-Expression-Recognitio↗

  Casanovafeat

  --- base_model: motheecreator/vit-Facial-Expression-Recognition tags: - generated_from_trainer - facial-expression-recognition - image-classification - vision-transformer metrics: - accuracy model-index: - name: vit-Facial-Expression-Recognitio results: [] --- This model is based on a Vision Transformer (ViT) architecture fine-tuned for facial expression recognition. The associated research, *Facial Expression Recognition with CNNs and Vision Transformers: Full Images, Landmarks, and a Hybrid Method on FER-2013, FER+ and RAF-DB*, investigates different approaches to facial expression recognition using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).

• Kimi-K3-Audit-240E-MLX-MXFP4↗

  catalystsec

• Qwen3.8-27B-Uncensored-OrcaRouter-MLX-4bit↗

  chimingw