模型 13
depth-anything/Depth-Anything-V2-Base-hf
Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth e…
depth-anything/Depth-Anything-V2-Large-hf
Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth e…
IDEA-Research/grounding-dino-base
The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong L…
facebook/mask2former-swin-large-ade-semantic
Mask2Former model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attent…
PekingU/rtdetr_v2_r50vd
The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer by Wenyu Lv, Yian…
facebook/sam-vit-huge
Detailed architecture of Segment Anything Model (SAM).
facebook/sam-vit-large
Detailed architecture of Segment Anything Model (SAM).
facebook/sam2.1-hiera-base-plus
Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and…
facebook/sam2.1-hiera-large
Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and…
facebook/sam2.1-hiera-small
Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and…
facebook/sam2.1-hiera-tiny
Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and…
facebook/sam3
SAM 3 is a unified foundation model for promptable segmentation in images and videos. It can detect, segment, and track objects using text o…
nvidia/segformer-b5-finetuned-ade-640-640
SegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper SegFormer: Simple and Efficient Design for Semant…