Models by task

Every family the library ships, generated from the v1.7.0 model registry: 115 families across 22 tasks.

Detection

47 families

Boxes around objects. The task most of the library is built for.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

⭐YOLOv9

yolo9: t, s, m, c at 640 px; yolo9_p2: t, s at 640 px

D-FINE

n, s, m, l, x at 640 px

DEIM

deim: n, s, m, l, x at 640 px

EdgeCrafter

s, m, l, x at 640 px

RT-DETR

rtdetr: r18, r34, r50, r50m, r101, l, x at 640 px; rtdetrv2: r18, r34, r50, r50m, r101 at 640 px; rtdetrv4: s, m, l, x at 640 px

TinyFormer
YOLO-NAS

s, m, l at 640 px

Dome-DETR

s, m, l at 800 px

PicoDet

s, m, l at 320 to 640 px

PP-YOLOE

s, m, l, x at 640 px

RTMDet

t, s, m, l, x at 640 px

YOLOv7

b at 640 px

YOLOX

n, t, s, m, l, x at 416 to 640 px

Florence-2

base, large at 768 px

Gemma 4

e2b, e4b at 896 px

Grounding DINO

t, b at 800 px

InternVL3

1b, 2b, 8b at 448 px

Kosmos-2

224 at 224 px

LFM2-VL

450m, 3b at 512 px

LibreMODUS
LocateAnything

3b at 2500 px

Moondream

2, 3 at 384 to 512 px

North Micro Vision
OMDet-Turbo

t at 640 px

OV-DEIM

s, m, l at 640 px

OWLv2

b16, l14 at 960 to 1008 px

Qwen3-VL

2b, 4b, 8b at 1024 px

SenseNova-Vision

7b at 1024 px

SmolVLM2

500m at 512 px

WeVisOne-VL

2b, 4b at 1440 px

CenterNet

resdcn18, dla34 at 512 px

Deformable DETR

r50ss, r50ssdc5, r50, r50refine, r50twostage at 800 px

DETR

r50, r50dc5, r101, r101dc5 at 800 px

DINO-DETR

r50, r50s5, swinl at 800 px

EfficientDet
Faster R-CNN

n, s, m, l at 320 to 800 px

FCOS

r50 at 800 px

LW-DETR

t, s, m, l, x at 640 px

Mask R-CNN

r50 at 800 px

RetinaNet

r50, r50v2 at 800 px

SSD

300 at 300 px

YOLOv1

t, b at 448 px

YOLOv2

t, b at 416 to 608 px

YOLOv3

t, b, spp at 416 to 608 px

YOLOv4

t, b at 416 to 608 px

Segmentation

15 families

Per-instance masks rather than boxes.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

D-FINE

n, s, m, l, x at 640 px

EdgeCrafter

s, m, l, x at 640 px

RTMDet

t, s, m, l, x at 640 px

MobileSAM

tiny at 1024 px

PicoSAM3

pico at 96 px

SAM

base, large, huge at 1024 px

SAM 3

large at 1008 px

SenseNova-Vision

7b at 1024 px

WeVisOne-VL

2b, 4b at 1440 px

EoMT

s, b, l at 512 px

Mask R-CNN

r50 at 800 px

Keypoints

7 families

Skeletons and landmarks on each detected instance.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

EdgeCrafter

s, m, l, x at 640 px

YOLO-NAS

s, m, l at 640 px

SenseNova-Vision

7b at 1024 px

DEKR

w32 at 640 px

Classification

15 families

One label for the whole image, and the backbones behind everything else.

ConvNeXt

t, s, b at 224 px

ConvNeXt V2
DINOv2

n, s, m, l at 518 px

EfficientNetV2

b0, b1, b2, b3 at 224 to 300 px

MobileNetV4

s, m, l at 224 to 256 px

ResNet

18, 34, 50, 101 at 224 px

V-JEPA 2

l256, h256, g256, g384 at 256 to 384 px

Perception Encoder
AlexNet

b at 224 px

Swin Transformer

t, s, b, l at 224 px

VGG

16, 19, 16bn, 19bn at 224 px

ViT

ti, s, b, l at 224 px

DeiT

t, s, b at 224 px

Oriented boxes

4 families

Rotated boxes, for aerial imagery and anything that does not sit axis-aligned.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

RT-DETR

rtdetr: r18, r34, r50, r50m, r101, l, x at 640 px; rtdetrv2: r18, r34, r50, r50m, r101 at 640 px; rtdetrv4: s, m, l, x at 640 px

YOLO-NAS

s, m, l at 640 px

Depth

8 families

Distance per pixel from a single image.

LibreMODUS
SenseNova-Vision

7b at 1024 px

Depth Anything 3

l at 504 px

Depth Anything V2

s, b, l, g at 518 px

Marigold V2

b at 1024 px

MiDaS

s, l at 256 to 384 px

ZipDepth

b, bnpu at 384 px

Semantic segmentation

10 families

A class for every pixel, without separating instances.

DINOv2

n, s, m, l at 518 px

LingBot-Vision
PP-LiteSeg
SegFormer
DeepLabv3
EoMT

s, b, l at 512 px

FCN

r50, r101 at 520 px

PIDNet

s, m, l at 1024 px

Panoptic segmentation

2 families

Semantic and instance masks in one pass.

SenseNova-Vision

7b at 1024 px

EoMT

s, b, l at 512 px

Points and counting

9 families

Centroids instead of boxes, cheap enough for microcontrollers.

Florence-2 grounding

base, large at 768 px

LocateAnything

3b at 2500 px

Molmo2

4b, 8b at 378 px

Moondream

2, 3 at 384 to 512 px

Qwen3-VL grounding

2b, 4b, 8b at 1024 px

SenseNova-Vision

7b at 1024 px

ShowUI

2b at 1344 px

WeVisOne-VL

2b, 4b at 1440 px

Text recognition

3 families

Finding and reading text in an image.

SenseNova-Vision

7b at 1024 px

WeVisOne-VL

2b, 4b at 1440 px

PP-OCRv5

t, l at 960 px

Embeddings

7 families

Vectors for search, clustering and retrieval.

DINOv2

n, s, m, l at 518 px

V-JEPA 2

l256, h256, g256, g384 at 256 to 384 px

Perception Encoder
LeVJEPA

l at 224 px

LibreFaceRec

Gaze

1 families

Where a person is looking.

L2CS-Net

r18, r34, r50, r101, r152 at 448 px

Edge detection

3 families

Contours and boundaries.

LibreMODUS
DexiNed

b at 352 px

TEED

t at 352 px

Surface normals

3 families

Which way each surface faces.

LibreMODUS
Marigold V2

b at 1024 px

MoGe-2

s, b, l at 518 px

Matting

5 families

Alpha cutouts, hair and edges included.

WeVisOne-VL

2b, 4b at 1440 px

BEN2

b at 1024 px

BiRefNet

t, l at 1024 px

FeyNobg

l at 1024 px

ViTMatte

s at 512 px

Restoration

7 families

Denoising, deblurring and upscaling.

NAFNet

s, l at 256 px

HVI-CIDNet

t at 256 px

QuickSRNet

m2 at 64 px

Real-ESRGAN

x4, x2, x4t at 64 px

SwinIR

s, m, l at 64 px

Mesh

1 families

3D geometry from an image.

SAM 3D Body

d3, h at 512 px

Robot policies

Robot policies

1 families

Action chunks from camera observations and robot state.

LibreVLA

smolvla: base at 512 px; act_policy: base at 224 px; diffusion_policy: base at 224 px

Albedo

Albedo

1 families

Intrinsic surface color in linear RGB.

Marigold V2

b at 1024 px

3D detection

3D detection

4 families

Camera-frame cuboids from a single image.

DetAny3D

h at 896 px

FCOS3D

r101 at 1600 px

WildDet3D

l at 1008 px

Image generation

Image generation

2 families

An image from a text prompt, with the seed that makes it again.

FLUX.2 klein

4b at 1024 px

SANA-Sprint
3D generation

3D generation

1 families

The whole object as 3D Gaussian splats, from one picture of it.

Speridlabs Idol