TF-Vision Model Garden

⚠️ Disclaimer: Checkpoints are based on training with publicly available datasets. Some datasets contain limitations, including non-commercial use limitations. Please review the terms and conditions made available by third parties before using the datasets provided. Checkpoints are licensed under Apache 2.0.

⚠️ Disclaimer: Datasets hyperlinked from this page are not owned or distributed by Google. Such datasets are made available by third parties. Please review the terms and conditions made available by the third parties before using the data.

Table of Contents

• Introduction
• Backbones
• Decoders
• Heads
• Image Classification
  • ResNet models trained with vanilla settings
  • ResNet-RS models trained with various settings
  • Vision Transformer (ViT)
• Object Detection and Instance Segmentation
  • Common Settings and Notes
• COCO Object Detection Baselines
  • RetinaNet (ImageNet pretrained)
  • RetinaNet (Trained from scratch)
  • Mobile-size RetinaNet (Trained from scratch))
  • YOLOv7 (Trained from scratch)
• Instance Segmentation Baselines
  • Mask R-CNN (Trained from scratch)
  • Cascade RCNN-RS (Trained from scratch)
• Semantic Segmentation
  • PASCAL-VOC
  • CITYSCAPES
• Video Classification
  • Common Settings and Notes
  • Kinetics-400 Action Recognition Baselines
  • Kinetics-600 Action Recognition Baselines

Introduction

TF-Vision modeling library for computer vision provides a collection of baselines and checkpoints for image classification, object detection, and segmentation.

Backbones

Backbones
DilatedResNet
EfficientNet
MobileDet
MobileNet
ResNet
ResNet3D
RevNet
SpineNet
SpineNetMobile
VisionTransformer

Decoders

Decoders
ASPP
FPN
NASFPN

Heads

Heads
DetectionHead
MaskHead
MaskScoring
RPNHead
RetinaNetHead
SegmentationHead

Image Classification

ResNet models trained with vanilla settings

<details>

• Models are trained from scratch with batch size 4096 and 1.6 initial learning rate.
• Linear warmup is applied for the first 5 epochs.
• Models trained with l2 weight regularization and ReLU activation.

Model	Resolution	Epochs	Top-1	Top-5	Download
ResNet-50	224x224	90	76.1	92.9	config
ResNet-50	224x224	200	77.1	93.5	config | ckpt
ResNet-101	224x224	200	78.3	94.2	config | ckpt
ResNet-152	224x224	200	78.7	94.3	config | ckpt

</details>

ResNet-RS models trained with various settings

<details>

We support state-of-the-art ResNet-RS image classification models with features:

• ResNet-RS architectural changes and Swish activation. (Note that ResNet-RS adopts ReLU activation in the paper.)
• Regularization methods including Random Augment, 4e-5 weight decay, stochastic depth, label smoothing and dropout.
• New training methods including a 350-epoch schedule, cosine learning rate and EMA.
• Configs are in this directory.

Model	Resolution	Params (M)	Top-1	Top-5	Download
ResNet-RS-50	160x160	35.7	79.1	94.5	config | ckpt
ResNet-RS-101	160x160	63.7	80.2	94.9	config | ckpt
ResNet-RS-101	192x192	63.7	81.3	95.6	config | ckpt
ResNet-RS-152	192x192	86.8	81.9	95.8	config | ckpt
ResNet-RS-152	224x224	86.8	82.5	96.1	config | ckpt
ResNet-RS-152	256x256	86.8	83.1	96.3	config | ckpt
ResNet-RS-200	256x256	93.4	83.5	96.6	config | ckpt
ResNet-RS-270	256x256	130.1	83.6	96.6	config | ckpt
ResNet-RS-350	256x256	164.3	83.7	96.7	config | ckpt
ResNet-RS-350	320x320	164.3	84.2	96.9	config | ckpt

</details>

Vision Transformer (ViT)

<details>

We support ViT and DEIT implementations. ViT models trained under the DEIT settings:

model	resolution	Top-1	Top-5	Download
ViT-ti16	224x224	73.4	91.9	ckpt
ViT-s16	224x224	79.4	94.7	ckpt
ViT-b16	224x224	81.8	95.8	ckpt
ViT-l16	224x224	82.2	95.8	ckpt

</details>

Object Detection and Instance Segmentation

Common Settings and Notes

<details>

• We provide models adopting ResNet-FPN and SpineNet backbones based on detection frameworks:
  • RetinaNet and RetinaNet-RS
  • Mask R-CNN
  • Cascade RCNN and Cascade RCNN-RS
• Models are all trained on COCO train2017 and evaluated on COCO val2017.
  • The checkpoints were trained on annotations owned and licensed by the COCO Consortium under a Creative Commons Attribution 4.0 License.
  • The COCO Consortium does not own the copyright of the images corresponding to the annotations. The images are made available by Flickr under various Creative Commons licenses, and users of the images accept full responsibility for the use of the dataset.
• Training details:
  • Models finetuned from ImageNet pretrained checkpoints adopt the 12 or 36 epochs schedule. Models trained from scratch adopt the 350 epochs schedule.
  • The default training data augmentation implements horizontal flipping and scale jittering with a random scale between [0.5, 2.0].
  • Unless noted, all models are trained with l2 weight regularization and ReLU activation.
  • We use batch size 256 and stepwise learning rate that decays at the last 30 and 10 epoch.
  • We use square image as input by resizing the long side of an image to the target size then padding the short side with zeros.

</details>

COCO Object Detection Baselines

RetinaNet (ImageNet pretrained)

<details>

Backbone	Resolution	Epochs	FLOPs (B)	Params (M)	Box AP	Download
R50-FPN	640x640	12	97.0	34.0	34.3	config
R50-FPN	640x640	72	97.0	34.0	36.8	config | ckpt

</details>

RetinaNet (Trained from scratch)

<details>

training features including:

• Stochastic depth with drop rate 0.2.
• Swish activation.

Backbone	Resolution	Epochs	FLOPs (B)	Params (M)	Box AP	Download
SpineNet-49	640x640	500	85.4	28.5	44.2	config | ckpt
SpineNet-96	1024x1024	500	265.4	43.0	48.5	config | ckpt
SpineNet-143	1280x1280	500	524.0	67.0	50.0	config | ckpt

</details>

Mobile-size RetinaNet (Trained from scratch):

<details>

Backbone	Resolution	Epochs	FLOPs (B)	Params (M)	Box AP	Download
MobileNetv2	256x256	600	-	2.27	23.5	config
Mobile SpineNet-49	384x384	600	1.0	2.32	28.1	config | ckpt

</details>

YOLOv7 (Trained from scratch)

<details>

Variant	Resolution	Epochs	FLOPs (B)	Params (M)	Box AP	Download
YOLOv7	640x640	300	53.16	44.57	50.5	config | ckpt

</details>

Instance Segmentation Baselines

Mask R-CNN (Trained from scratch)

<details>

Backbone	Resolution	Epochs	FLOPs (B)	Params (M)	Box AP	Mask AP	Download
ResNet50-FPN	640x640	350	227.7	46.3	42.3	37.6	config
SpineNet-49	640x640	350	215.7	40.8	42.6	37.9	config
SpineNet-96	1024x1024	500	315.0	55.2	48.1	42.4	config
SpineNet-143	1280x1280	500	498.8	79.2	49.3	43.4	config

</details>

Cascade RCNN-RS (Trained from scratch)

<details>

Backbone	Resolution	Epochs	Params (M)	Box AP	Mask AP	Download
SpineNet-49	640x640	500	56.4	46.4	40.0	config
SpineNet-96	1024x1024	500	70.8	50.9	43.8	config
SpineNet-143	1280x1280	500	94.9	51.9	45.0	config

</details>

Semantic Segmentation

• We support DeepLabV3 and DeepLabV3+ architectures, with Dilated ResNet backbones.
• Backbones are pre-trained on ImageNet.

PASCAL-VOC

<details>

Model	Backbone	Resolution	Steps	mIoU	Download
DeepLabV3	Dilated Resnet-101	512x512	30k	78.7
DeepLabV3+	Dilated Resnet-101	512x512	30k	79.2	ckpt

</details>

CITYSCAPES

<details>

Model	Backbone	Resolution	Steps	mIoU	Download
DeepLabV3+	Dilated Resnet-101	1024x2048	90k	78.79

</details>

Video Classification

Common Settings and Notes

<details>

• We provide models for video classification with backbones:

  • SlowOnly in SlowFast Networks for Video Recognition.
  • ResNet-3D (R3D) in Spatiotemporal Contrastive Video Representation Learning.
  • ResNet-3D-RS (R3D-RS) in Revisiting 3D ResNets for Video Recognition.
  • Mobile Video Networks (MoViNets) in MoViNets: Mobile Video Networks for Efficient Video Recognition.

• Training and evaluation details (SlowFast and ResNet):

  • All models are trained from scratch with vision modality (RGB) for 200 epochs.
  • We use batch size of 1024 and cosine learning rate decay with linear warmup in first 5 epochs.
  • We follow SlowFast to perform 30-view evaluation.

</details>

Kinetics-400 Action Recognition Baselines

<details>

Model	Input (frame x stride)	Top-1	Top-5	Download
SlowOnly	8 x 8	74.1	91.4	config
SlowOnly	16 x 4	75.6	92.1	config
R3D-50	32 x 2	77.0	93.0	config
R3D-RS-50	32 x 2	78.2	93.7	config
R3D-RS-101	32 x 2	79.5	94.2	-
R3D-RS-152	32 x 2	79.9	94.3	-
R3D-RS-200	32 x 2	80.4	94.4	-
R3D-RS-200	48 x 2	81.0	-	-
MoViNet-A0-Base	50 x 5	69.40	89.18	-
MoViNet-A1-Base	50 x 5	74.57	92.03	-
MoViNet-A2-Base	50 x 5	75.91	92.63	-
MoViNet-A3-Base	120 x 2	79.34	94.52	-
MoViNet-A4-Base	80 x 3	80.64	94.93	-
MoViNet-A5-Base	120 x 2	81.39	95.06	-

</details>

Kinetics-600 Action Recognition Baselines

<details>

Model	Input (frame x stride)	Top-1	Top-5	Download
SlowOnly	8 x 8	77.3	93.6	config
R3D-50	32 x 2	79.5	94.8	config
R3D-RS-200	32 x 2	83.1	-	-
R3D-RS-200	48 x 2	83.8	-	-
MoViNet-A0-Base	50 x 5	72.05	90.92	config
MoViNet-A1-Base	50 x 5	76.69	93.40	config
MoViNet-A2-Base	50 x 5	78.62	94.17	config
MoViNet-A3-Base	120 x 2	81.79	95.67	config
MoViNet-A4-Base	80 x 3	83.48	96.16	config
MoViNet-A5-Base	120 x 2	84.27	96.39	config

</details>

More Documentations

Please read through the references in the examples/starter.