Modelos por tarea

Todas las familias que incluye la biblioteca, generadas desde el registro de modelos v1.7.0: 115 familias en 22 tareas.

Detección

47 familias

Cajas alrededor de los objetos. La tarea para la que está hecha la mayor parte de la biblioteca.

⭐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

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

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

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

Segmentación

15 familias

Máscaras por instancia en lugar de cajas.

⭐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

Puntos clave

7 familias

Esqueletos y landmarks sobre cada instancia detectada.

⭐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

Clasificación

15 familias

Una etiqueta para toda la imagen, y los backbones detrás de todo lo demás.

ConvNeXt

t, s, b at 224 px

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

Cajas orientadas

4 familias

Cajas rotadas, para imágenes aéreas y todo lo que no esté alineado con los ejes.

⭐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

Profundidad

8 familias

Distancia por píxel a partir de una sola imagen.

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

Segmentación semántica

10 familias

Una clase para cada píxel, sin separar instancias.

DINOv2

n, s, m, l at 518 px

LingBot-Vision
EoMT

s, b, l at 512 px

FCN

r50, r101 at 520 px

PIDNet

s, m, l at 1024 px

Segmentación panóptica

2 familias

Máscaras semánticas y de instancia en una sola pasada.

SenseNova-Vision

7b at 1024 px

EoMT

s, b, l at 512 px

Puntos y conteo

9 familias

Centroides en lugar de cajas, tan barato que corre en microcontroladores.

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

Reconocimiento de texto

3 familias

Encontrar y leer texto en una imagen.

SenseNova-Vision

7b at 1024 px

WeVisOne-VL

2b, 4b at 1440 px

PP-OCRv5

t, l at 960 px

Vectores de embedding

7 familias

Vectores para búsqueda, clustering y recuperación.

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

Mirada

1 familias

Hacia dónde mira una persona.

L2CS-Net

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

Detección de bordes

3 familias

Contornos y límites.

DexiNed

b at 352 px

TEED

t at 352 px

Normales de superficie

3 familias

Hacia dónde apunta cada superficie.

Marigold V2

b at 1024 px

MoGe-2

s, b, l at 518 px

Recorte alfa

5 familias

Recortes con canal alfa, pelo y bordes incluidos.

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

Restauración

7 familias

Eliminación de ruido, corrección de desenfoque y reescalado.

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

Malla

1 familias

Geometría 3D a partir de una imagen.

SAM 3D Body

d3, h at 512 px

Robot policies

Robot policies

1 familias

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 familias

Intrinsic surface color in linear RGB.

Marigold V2

b at 1024 px

3D detection

3D detection

4 familias

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 familias

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

FLUX.2 klein

4b at 1024 px

3D generation

3D generation

1 familias

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

Speridlabs Idol