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vision-encoders-playground

This repository aims to summarize pretrained encoders (backbones) derived from different detection/classification models. These encoders can be used for several downstream vision tasks such as classification, object detection, body keypoint estimation, semantic segmentation, depth estimation, etc.

Requirements

to be updated

Encoder list

Notes

  • ❌ means the encoder is incompatible with TI
  • ✔️ means the encoder is compatible with TI
  • ❔ means the TI compatibility has not been checked
  • GMACs values were calculated with input size (224, 224)

Classification-based

Reference Pretrained Dataset Source Encoder name Param Num GMACs License TI compatibility
geffnet ImageNet geffnet_encoder.py efficientnetb0 4,007,548 0.387 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb1 6,513,184 0.578 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb2 7,700,994 0.668 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb3 10,696,232 0.976 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb4 17,548,616 1.53 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb5 28,340,784 2.4 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb6 40,735,704 3.41 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetb7 63,786,960 5.25 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b0 4,007,548 0.387 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b1 6,513,184 0.578 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b2 7,700,994 0.668 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b3 10,696,232 0.976 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b4 17,548,616 1.53 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b5 28,340,784 2.4 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b6 40,735,704 3.41 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetap_b7 63,786,960 5.25 Apache-2.0 ❌
geffnet ImageNet geffnet_encoder.py efficientnetlite0 3,371,008 0.386 Apache-2.0 ✔️
geffnet ImageNet geffnet_encoder.py efficientnetlite1 4,135,680 0.509 Apache-2.0 ✔️
geffnet ImageNet geffnet_encoder.py efficientnetlite2 4,811,072 0.584 Apache-2.0 ✔️
geffnet ImageNet geffnet_encoder.py efficientnetlite3 6,916,096 0.865 Apache-2.0 ✔️
geffnet ImageNet geffnet_encoder.py efficientnetlite4 11,725,568 1.37 Apache-2.0 ✔️
torchvision ImageNet torchvision_encoder.py torchefficientnetb0 4,007,548 0.409 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb1 6,513,184 0.603 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb2 7,700,994 0.693 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb3 10,696,232 1.01 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb4 17,548,616 1.56 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb5 28,340,784 2.44 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb6 40,735,704 3.47 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py torchefficientnetb7 63,786,960 5.32 BSD 3-Clause ❌
torchvision ImageNet torchvision_encoder.py mobilenetv2 2,223,872 0.319 BSD 3-Clause ✔️
torchvision ImageNet torchvision_encoder.py mobilenetv3small 927,008 0.059 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py mobilenetv3large 2,971,952 0.229 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py resnet18 11,176,512 1.82 BSD 3-Clause ✔️
torchvision ImageNet torchvision_encoder.py resnet34 21,284,672 3.68 BSD 3-Clause ✔️
torchvision ImageNet torchvision_encoder.py resnet50 23,508,032 4.13 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py resnet101 42,500,160 7.86 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py resnet152 58,143,808 11.6 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py resnext50_32x4d 22,979,904 4.28 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py resnext101_32x8d 86,742,336 16.54 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py resnext101_64x4d 81,406,272 15.58 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_400mf 3,903,144 0.418 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_800mf 5,647,512 0.856 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_1_6gf 10,313,430 1.65 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_3_2gf 17,923,338 3.22 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_8gf 37,364,472 8.56 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_16gf 80,565,140 16.01 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_y_32gf 141,333,770 32.41 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_x_400mf 5,094,976 0.426 BSD 3-Clause ✔️
torchvision ImageNet torchvision_encoder.py regnet_x_800mf 6,586,656 0.819 BSD 3-Clause ✔️
torchvision ImageNet torchvision_encoder.py regnet_x_1_6gf 8,277,136 1.63 BSD 3-Clause ✔️
torchvision ImageNet torchvision_encoder.py regnet_x_3_2gf 14,287,552 3.22 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_x_8gf 37,651,648 8.05 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_x_16gf 52,229,536 16.04 BSD 3-Clause ❔
torchvision ImageNet torchvision_encoder.py regnet_x_32gf 105,290,560 31.87 BSD 3-Clause ❔
apple ml-mobileone ImageNet mobileone_encoder.py mobileone_s0 4,268,272 1.25 Apple ❌
apple ml-mobileone ImageNet mobileone_encoder.py mobileone_s1 3,544,192 1.13 Apple ❌
apple ml-mobileone ImageNet mobileone_encoder.py mobileone_s2 5,835,648 1.67 Apple ❌
apple ml-mobileone ImageNet mobileone_encoder.py mobileone_s3 8,121,600 2.34 Apple ❌
apple ml-mobileone ImageNet mobileone_encoder.py mobileone_s4 12,902,248 3.56 Apple ❌
timm encoder* ImageNet timm_encoder.py - - - Apache-2.0 ❔
facebook ConvNeXt ImageNet convnext_encoder.py convnext_tiny 27,818,592 4.49 MIT ❌
facebook ConvNeXt ImageNet convnext_encoder.py convnext_small 49,453,152 8.73 MIT ❌
facebook ConvNeXt ImageNet convnext_encoder.py convnext_base 87,564,416 15.42 MIT ❌
facebook ConvNeXt ImageNet convnext_encoder.py convnext_large 196,227,264 34.46 MIT ❌
facebook ConvNeXt ImageNet convnext_encoder.py convnext_xlarge 196,227,264 34.46 MIT ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_atto 3,386,760 0.556 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_femto 4,847,472 0.790 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_pico 8,552,256 1.38 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_nano 13,301,520 2.13 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_tiny 49,547,904 8.73 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_base 87,690,752 15.42 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_large 196,416,768 34.46 CC BY-NC 4.0 ❌
facebook ConvNeXt-V2 ImageNet convnextv2_encoder.py convnextv2_huge 657,467,008 115.1 CC BY-NC 4.0 ❌

Detection-based

Reference Pretrained Dataset Source Encoder Param Num GMACs License TI compatibility
edgeai-yolox COCO yolox_encoder.py tiyoloxn 1,767,868 0.269 Apache-2.0 ✔️
edgeai-yolox COCO yolox_encoder.py tiyoloxt 3,968,748 0.578 Apache-2.0 ✔️
edgeai-yolox COCO yolox_encoder.py tiyoloxs 7,047,708 1.01 Apache-2.0 ✔️
edgeai-yolox COCO yolox_encoder.py tiyoloxm 21,032,508 3.09 Apache-2.0 ✔️
Megviii YOLOX COCO yolox_encoder.py yoloxn 1,767,520 0.264 Apache-2.0 ❌
Megviii YOLOX COCO yolox_encoder.py yoloxt 3,968,400 0.574 Apache-2.0 ❌
Megviii YOLOX COCO yolox_encoder.py yoloxs 7,047,360 1.0 Apache-2.0 ❌
Megviii YOLOX COCO yolox_encoder.py yoloxm 21,032,160 3.09 Apache-2.0 ❌
Megviii YOLOX COCO yolox_encoder.py yoloxl 46,599,040 7.0 Apache-2.0 ❌
Megviii YOLOX COCO yolox_encoder.py yoloxx 87,204,000 13.32 Apache-2.0 ❌
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_ns 1,373,992 0.224 Apache-2.0 ❔
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_nm 2,656,704 0.533 Apache-2.0 ❔
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_nl 5,625,520 0.872 Apache-2.0 ❔
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_t 8,278,544 1.06 Apache-2.0 ❔
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_s 15,741,728 2.21 Apache-2.0 ❔
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_m 28,047,808 3.71 Apache-2.0 ❔
DAMO-YOLO COCO damoyolo_encoder.py damoyolo_l 42,667,840 6.0 Apache-2.0 ❔
PPYOLOE backbone + neck COCO ppyoloe_encoder.py ppyoloe_s 6,410,864 0.89 Apache-2.0 ❌
PPYOLOE backbone + neck COCO ppyoloe_encoder.py ppyoloe_m 21,043,920 2.85 Apache-2.0 ❌
PPYOLOE backbone + neck COCO ppyoloe_encoder.py ppyoloe_l 49,183,264 6.58 Apache-2.0 ❌
PPYOLOE backbone + neck COCO ppyoloe_encoder.py ppyoloe_x 95,244,800 12.64 Apache-2.0 ❌
PPYOLOE backbone + neck (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_s_noattn 6,214,304 0.87 Apache-2.0 ✔️
PPYOLOE backbone + neck (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_m_noattn 20,602,200 2.79 Apache-2.0 ✔️
PPYOLOE backbone + neck (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_l_noattn 48,398,464 6.46 Apache-2.0 ❔
PPYOLOE backbone + neck (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_x_noattn 94,019,000 12.45 Apache-2.0 ❔
PPYOLOE backbone COCO ppyoloe_encoder.py ppyoloe_s_truncate 2,992,976 0.57 Apache-2.0 ❌
PPYOLOE backbone COCO ppyoloe_encoder.py ppyoloe_m_truncate 9,317,424 1.75 Apache-2.0 ❌
PPYOLOE backbone COCO ppyoloe_encoder.py ppyoloe_l_truncate 21,158,752 3.92 Apache-2.0 ❌
PPYOLOE backbone COCO ppyoloe_encoder.py ppyoloe_x_truncate 40,240,640 7.41 Apache-2.0 ❌
PPYOLOE backbone (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_s_noattn_truncate 2,796,416 0.54 Apache-2.0 ✔️
PPYOLOE backbone (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_m_noattn_truncate 8,875,704 1.68 Apache-2.0 ✔️
PPYOLOE backbone (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_l_noattn_truncate 20,373,952 3.81 Apache-2.0 ❔
PPYOLOE backbone (removed Squeeze-Excitation block) COCO ppyoloe_encoder.py ppyoloe_x_noattn_truncate 39,014,840 7.23 Apache-2.0 ❔
Jahongir YOLOv5-pt COCO yolov5_encoder.py yolov5n 1,757,152 0.256 AGPL-3.0 ❌
Jahongir YOLOv5-pt COCO yolov5_encoder.py yolov5s 7,006,144 0.971 AGPL-3.0 ❌
Jahongir YOLOv5-pt COCO yolov5_encoder.py yolov5m 20,847,072 2.94 AGPL-3.0 ❌
Jahongir YOLOv5-pt COCO yolov5_encoder.py yolov5l 46,105,984 6.61 AGPL-3.0 ❌
Jahongir YOLOv5-pt COCO yolov5_encoder.py yolov5x 86,177,440 12.51 AGPL-3.0 ❌
Custom Jahongir YOLOv5-pt for TI COCO yolov5_encoder.py tiyolov5n 1,757,152 0.256 AGPL-3.0 ✔️
Custom Jahongir YOLOv5-pt for TI COCO yolov5_encoder.py tiyolov5s 7,006,144 0.971 AGPL-3.0 ✔️
Custom Jahongir YOLOv5-pt for TI COCO yolov5_encoder.py tiyolov5m 20,847,072 2.94 AGPL-3.0 ❌
Custom Jahongir YOLOv5-pt for TI COCO yolov5_encoder.py tiyolov5l 46,105,984 6.61 AGPL-3.0 ❌
Custom Jahongir YOLOv5-pt for TI COCO yolov5_encoder.py tiyolov5x 86,177,440 12.51 AGPL-3.0 ❌
ultralytics YOLOv5 COCO ultralytics_encoder.py ultralytics_yolov5n 1,757,152 0.256 AGPL-3.0 ❌
ultralytics YOLOv5 COCO ultralytics_encoder.py ultralytics_yolov5s 7,006,144 0.971 AGPL-3.0 ❌
ultralytics YOLOv5 COCO ultralytics_encoder.py ultralytics_yolov5m 20,847,072 2.94 AGPL-3.0 ❌
ultralytics YOLOv5 COCO ultralytics_encoder.py ultralytics_yolov5l 46,105,984 6.61 AGPL-3.0 ❌
ultralytics YOLOv5 COCO ultralytics_encoder.py ultralytics_yolov5x 86,177,440 12.51 AGPL-3.0 ❌
ultralytics YOLOv5 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov5n 1,757,152 0.256 AGPL-3.0 ✔️
ultralytics YOLOv5 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov5s 7,006,144 0.971 AGPL-3.0 ✔️
ultralytics YOLOv5 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov5m 20,847,072 2.94 AGPL-3.0 ✔️
ultralytics YOLOv5 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov5l 46,105,984 6.61 AGPL-3.0 ✔️
ultralytics YOLOv5 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov5x 86,177,440 12.51 AGPL-3.0 ✔️
meituan YOLOv6 P5 COCO yolov6_encoder.py yolov6n 4,629,248 0.729 GPL-3.0 ❔
meituan YOLOv6 P5 COCO yolov6_encoder.py yolov6s 18,472,448 2.87 GPL-3.0 ❔
meituan YOLOv6 P5 COCO yolov6_encoder.py yolov6m 33,959,832 5.4 GPL-3.0 ❔
meituan YOLOv6 P5 COCO yolov6_encoder.py yolov6l 52,963,053 8.52 GPL-3.0 ❔
meituan YOLOv6 P6 COCO yolov6_encoder.py yolov6n6 7,193,472 1.51 GPL-3.0 ❔
meituan YOLOv6 P6 COCO yolov6_encoder.py yolov6s6 28,713,728 5.94 GPL-3.0 ❔
meituan YOLOv6 lite COCO yolov6lite_encoder.py yolov6lites 354,662 0.096 GPL-3.0 ❔
meituan YOLOv6 lite COCO yolov6lite_encoder.py yolov6litem 588,101 0.122 GPL-3.0 ❔
meituan YOLOv6 lite COCO yolov6lite_encoder.py yolov6litel 895,509 0.173 GPL-3.0 ❔
ultralytics YOLOv6 COCO ultralytics_encoder.py ultralytics_yolov6n 3,892,720 0.686 AGPL-3.0 ❌
ultralytics YOLOv6 COCO ultralytics_encoder.py ultralytics_yolov6s 15,554,528 2.62 AGPL-3.0 ❌
ultralytics YOLOv6 COCO ultralytics_encoder.py ultralytics_yolov6m 50,655,696 9.96 AGPL-3.0 ❌
ultralytics YOLOv6 COCO ultralytics_encoder.py ultralytics_yolov6l 108,779,456 24.49 AGPL-3.0 ❌
ultralytics YOLOv6 COCO ultralytics_encoder.py ultralytics_yolov6x 169,948,080 37.94 AGPL-3.0 ❌
ultralytics YOLOv6 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov6n 3,892,720 0.686 AGPL-3.0 ✔️
ultralytics YOLOv6 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov6s 15,554,528 2.62 AGPL-3.0 ✔️
ultralytics YOLOv6 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov6m 50,655,696 9.96 AGPL-3.0 ✔️
ultralytics YOLOv6 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov6l 108,779,456 24.49 AGPL-3.0 ✔️
ultralytics YOLOv6 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov6x 169,948,080 37.94 AGPL-3.0 ✔️
WongKinYiu YOLOv7 COCO yolov7_encoder.py yolov7-tiny 5,997,856 0.803 GPL-3.0 ❔
WongKinYiu YOLOv7 COCO yolov7_encoder.py yolov7 37,162,400 6.42 GPL-3.0 ❔
WongKinYiu YOLOv7 COCO yolov7_encoder.py yolov7x 70,772,424 11.55 GPL-3.0 ❔
Jahongir YOLOv8-pt COCO yolov8_encoder.py yolov8n 2,259,536 0.318 AGPL-3.0 ❌
Jahongir YOLOv8-pt COCO yolov8_encoder.py yolov8s 9,019,552 1.25 AGPL-3.0 ❌
Jahongir YOLOv8-pt COCO yolov8_encoder.py yolov8m 22,080,624 3.87 AGPL-3.0 ❌
Jahongir YOLOv8-pt COCO yolov8_encoder.py yolov8l 38,047,040 8.53 AGPL-3.0 ❌
Jahongir YOLOv8-pt COCO yolov8_encoder.py yolov8x 59,434,640 13.32 AGPL-3.0 ❌
Custom Jahongir YOLOv8-pt for TI COCO yolov8_encoder.py tiyolov8n 2,259,536 0.318 AGPL-3.0 ✔️
Custom Jahongir YOLOv8-pt for TI COCO yolov8_encoder.py tiyolov8s 9,019,552 1.25 AGPL-3.0 ✔️
Custom Jahongir YOLOv8-pt for TI COCO yolov8_encoder.py tiyolov8m 22,080,624 3.87 AGPL-3.0 ❌
Custom Jahongir YOLOv8-pt for TI COCO yolov8_encoder.py tiyolov8l 38,047,040 8.53 AGPL-3.0 ❌
Custom Jahongir YOLOv8-pt for TI COCO yolov8_encoder.py tiyolov8x 59,434,640 13.32 AGPL-3.0 ❌
ultralytics YOLOv8 COCO ultralytics_encoder.py ultralytics_yolov8n 2,259,536 0.318 AGPL-3.0 ❌
ultralytics YOLOv8 COCO ultralytics_encoder.py ultralytics_yolov8s 9,019,552 1.25 AGPL-3.0 ❌
ultralytics YOLOv8 COCO ultralytics_encoder.py ultralytics_yolov8m 22,080,624 3.87 AGPL-3.0 ❌
ultralytics YOLOv8 COCO ultralytics_encoder.py ultralytics_yolov8l 38,047,040 8.53 AGPL-3.0 ❌
ultralytics YOLOv8 COCO ultralytics_encoder.py ultralytics_yolov8x 59,434,640 13.32 AGPL-3.0 ❌
ultralytics YOLOv8 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov8n 2,259,536 0.318 AGPL-3.0 ❌
ultralytics YOLOv8 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov8s 9,019,552 1.25 AGPL-3.0 ❌
ultralytics YOLOv8 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov8m 22,080,624 3.87 AGPL-3.0 ❌
ultralytics YOLOv8 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov8l 38,047,040 8.53 AGPL-3.0 ❌
ultralytics YOLOv8 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov8x 59,434,640 13.32 AGPL-3.0 ❌
WongKinYiu YOLOv9 COCO yolov9_encoder.py yolov9-t 1,709,232 0.397 GPL-3.0 ❔
WongKinYiu YOLOv9 COCO yolov9_encoder.py yolov9-s 6,800,736 1.57 GPL-3.0 ❔
WongKinYiu YOLOv9 COCO yolov9_encoder.py yolov9-c 29,456,768 7.55 GPL-3.0 ❔
WongKinYiu YOLOv9 COCO yolov9_encoder.py yolov9-e 58,425,024 11.86 GPL-3.0 ❔
WongKinYiu YOLOv9 COCO yolov9_encoder.py gelan-c 19,946,432 4.75 GPL-3.0 ❔
WongKinYiu YOLOv9 COCO yolov9_encoder.py gelan-e 52,562,112 10.2 GPL-3.0 ❔
ultralytics YOLOv9 COCO ultralytics_encoder.py ultralytics_yolov9c 19,946,432 4.75 AGPL-3.0 ❌
ultralytics YOLOv9 COCO ultralytics_encoder.py ultralytics_yolov9e 52,562,112 10.2 AGPL-3.0 ❌
ultralytics YOLOv9 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov9c 19,946,432 4.75 AGPL-3.0 ❌
ultralytics YOLOv9 (custom) COCO ultralytics_encoder.py ultralytics_tiyolov9e 52,562,112 10.2 AGPL-3.0 ❌
THU-MIG YOLOv10 COCO yolov10_encoder.py yolov10n 1,845,712 0.301 AGPL-3.0 ❔
THU-MIG YOLOv10 COCO yolov10_encoder.py yolov10s 6,427,552 1.14 AGPL-3.0 ❔
THU-MIG YOLOv10 COCO yolov10_encoder.py yolov10m 14,203,152 3.34 AGPL-3.0 ❔
THU-MIG YOLOv10 COCO yolov10_encoder.py yolov10l 22,943,296 7.0 AGPL-3.0 ❔
THU-MIG YOLOv10 COCO yolov10_encoder.py yolov10x 27,269,840 9.24 AGPL-3.0 ❔
THU-MIG YOLOv10 (custom) COCO yolov10_encoder.py tiyolov10n 1,845,712 0.301 AGPL-3.0 ❔
THU-MIG YOLOv10 (custom) COCO yolov10_encoder.py tiyolov10s 6,427,552 1.14 AGPL-3.0 ❔
THU-MIG YOLOv10 (custom) COCO yolov10_encoder.py tiyolov10m 14,203,152 3.34 AGPL-3.0 ❔
THU-MIG YOLOv10 (custom) COCO yolov10_encoder.py tiyolov10l 22,943,296 7.0 AGPL-3.0 ❔
THU-MIG YOLOv10 (custom) COCO yolov10_encoder.py tiyolov10x 27,269,840 9.24 AGPL-3.0 ❔
Jahongir YOLOv11-pt COCO yolov11_encoder.py yolov11n 2,159,168 0.287 AGPL-3.0 ❔
Jahongir YOLOv11-pt COCO yolov11_encoder.py yolov11s 8,608,384 1.13 AGPL-3.0 ❔
Jahongir YOLOv11-pt COCO yolov11_encoder.py yolov11m 18,641,984 3.78 AGPL-3.0 ❔
Jahongir YOLOv11-pt COCO yolov11_encoder.py yolov11l 23,899,456 4.94 AGPL-3.0 ❔
Jahongir YOLOv11-pt COCO yolov11_encoder.py yolov11x 53,728,224 11.09 AGPL-3.0 ❔
sunsmarterjie YOLOv12 COCO yolov11_encoder.py yolov12n 2,137,376 0.289 AGPL-3.0 ❔
sunsmarterjie YOLOv12 COCO yolov12_encoder.py yolov12s 8,433,728 1.12 AGPL-3.0 ❔
sunsmarterjie YOLOv12 COCO yolov12_encoder.py yolov12m 18,726,464 3.75 AGPL-3.0 ❔
sunsmarterjie YOLOv12 COCO yolov12_encoder.py yolov12l 24,978,080 5.07 AGPL-3.0 ❔
sunsmarterjie YOLOv12 COCO yolov12_encoder.py yolov12x 55,972,832 11.35 AGPL-3.0 ❔
RT-DETR PResNet backbone COCO rtdetr_encoder.py rtdetr_r18vd_truncate 11,190,112 2.07 Apache-2.0 ❔
RT-DETR PResNet backbone COCO rtdetr_encoder.py rtdetr_r34vd_truncate 21,290,848 3.93 Apache-2.0 ❔
RT-DETR PResNet backbone COCO rtdetr_encoder.py rtdetr_r50vd_truncate 23,474,016 4.35 Apache-2.0 ❔
RT-DETR PResNet backbone COCO rtdetr_encoder.py rtdetr_r101vd_truncate 42,413,920 8.07 Apache-2.0 ❔
RT-DETR PResNet backbone COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r18vd_truncate 11,190,112 2.07 Apache-2.0 ❔
RT-DETR PResNet backbone COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r34vd_truncate 21,290,848 3.93 Apache-2.0 ❔
RT-DETR PResNet backbone COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r50vd_truncate 23,474,016 4.35 Apache-2.0 ❔
RT-DETR PResNet backbone COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r101vd_truncate 42,413,920 8.07 Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO rtdetr_encoder.py rtdetr_r18vd 16,155,232 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO rtdetr_encoder.py rtdetr_r34vd 26,255,968 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO rtdetr_encoder.py rtdetr_r50vd 35,424,864 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO rtdetr_encoder.py rtdetr_r101vd 68,204,000 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r18vd 16,155,232 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r34vd 26,255,968 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r50vd 35,424,864 - Apache-2.0 ❔
RT-DETR PResNet backbone + HybridEncoder COCO + Object365 rtdetr_encoder.py cocoobject365_rtdetr_r101vd 68,204,000 - Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO hgnetv2_encoder.py coco_hgnetv2_b0 1,850,336 0.326 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO hgnetv2_encoder.py coco_hgnetv2_b2 6,026,544 1.15 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO hgnetv2_encoder.py coco_hgnetv2_b4 13,507,680 2.74 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO hgnetv2_encoder.py coco_hgnetv2_b5 33,231,840 6.55 Apache-2.0 ❔
D-FINE HGNetv2 backbone Object365 hgnetv2_encoder.py object365_hgnetv2_b0 1,850,336 0.326 Apache-2.0 ❔
D-FINE HGNetv2 backbone Object365 hgnetv2_encoder.py object365_hgnetv2_b2 6,026,544 1.15 Apache-2.0 ❔
D-FINE HGNetv2 backbone Object365 hgnetv2_encoder.py object365_hgnetv2_b4 13,507,680 2.74 Apache-2.0 ❔
D-FINE HGNetv2 backbone Object365 hgnetv2_encoder.py object365_hgnetv2_b5 33,231,840 6.55 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO + Object365 hgnetv2_encoder.py cocoobject365_hgnetv2_b0 1,850,336 0.326 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO + Object365 hgnetv2_encoder.py cocoobject365_hgnetv2_b2 6,026,544 1.15 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO + Object365 hgnetv2_encoder.py cocoobject365_hgnetv2_b4 13,507,680 2.74 Apache-2.0 ❔
D-FINE HGNetv2 backbone COCO + Object365 hgnetv2_encoder.py cocoobject365_hgnetv2_b5 33,231,840 6.55 Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO hgnetv2_encoder.py coco_dfine_s 10,734,816 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO hgnetv2_encoder.py coco_dfine_m 13,825,584 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO hgnetv2_encoder.py coco_dfine_l 22,850,912 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO hgnetv2_encoder.py coco_dfine_x 53,931,872 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder Object365 hgnetv2_encoder.py object365_dfine_s 10,734,816 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder Object365 hgnetv2_encoder.py object365_dfine_m 13,825,584 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder Object365 hgnetv2_encoder.py object365_dfine_l 22,850,912 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder Object365 hgnetv2_encoder.py object365_dfine_x 53,931,872 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO + Object365 hgnetv2_encoder.py cocoobject365_dfine_s 10,734,816 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO + Object365 hgnetv2_encoder.py cocoobject365_dfine_m 13,825,584 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO + Object365 hgnetv2_encoder.py cocoobject365_dfine_l 22,850,912 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO + Object365 hgnetv2_encoder.py cocoobject365_dfine_x 53,931,872 - Apache-2.0 ❔
DEIM HGNetv2 backbone COCO hgnetv2_encoder.py deim_hgnetv2_b0 1,850,336 0.326 Apache-2.0 ❔
DEIM HGNetv2 backbone COCO hgnetv2_encoder.py deim_hgnetv2_b2 6,026,544 1.15 Apache-2.0 ❔
DEIM HGNetv2 backbone COCO hgnetv2_encoder.py deim_hgnetv2_b4 13,507,680 2.74 Apache-2.0 ❔
DEIM HGNetv2 backbone COCO hgnetv2_encoder.py deim_hgnetv2_b5 33,231,840 6.55 Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO deim_encoder.py deim_s 10,734,816 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO deim_encoder.py deim_m 13,825,584 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO deim_encoder.py deim_l 22,850,912 - Apache-2.0 ❔
D-FINE HGNetv2 backbone + HybridEncoder encoder COCO deim_encoder.py deim_x 53,931,872 - Apache-2.0 ❔

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