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AI 모델 84759

• Grok 4.20 0309 v2 (Non-reasoning)↗

  SpaceXAI

• Qwen3.8-27B-OBLITERATED-DYN-GGUF↗

  spicysharkco

• sunflower_language_classification_v2↗

  Sunbird

• WAMU-Merge-MotionBoost_WAN2.2_I2V_LIGHTNING↗

  TestOrganizationPleaseIgnore

• Sensualize-Mixtral-GGUF↗

  TheBloke

• incorrecter-GGUF↗

  tinyopsec

• Qwimi-3.6-27B-Coder-MTP-GGUF↗

  trjxter

• tiny-Idefics3ForConditionalGeneration↗

  trl-internal-testing

• ARK-270M-Base-GGUF↗

  usermma

• flexray↗

  VictorButoi

  FleXray is a single 2D UNet that segments anatomy from standard radiographs across body regions, projections, and acquisition settings. It predicts 60 anatomical structures (plus background) as independent sigmoid channels at 256 x 256 resolution. This repository holds the **flagship model** and the four sibling models of the FleXray ensemble, one bundle per `members/` subfolder (see Repository layout). `flexify` and `FleXraySegmenter.from_pretrained` load the flagship by default. `flexify` writes ` _masks.npy`, ` _probabilities.npy`, and ` _logits.npy` per image, each shaped `CxHxW`. The Python API keeps the batch dimension (`B=1` for a single image). Channel order follows `label_schema.json`. Pass `--binary LABEL` (for example `--binary femurs`) to write one label. See docs/inference.md for the full CLI and Python API.