# 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](https://github.com/tensorflow/models/blob/master/LICENSE).

⚠️ 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.

## Introduction

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

## Image Classification

### ImageNet Baselines

#### ResNet models trained with vanilla settings

* 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](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet50_tpu.yaml) |
| ResNet-50    | 224x224       |    200   | 77.1 | 93.5 | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet50_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet/resnet-50-i224.tar.gz) |
| ResNet-101   | 224x224       |    200   | 78.3 | 94.2 | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet101_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet/resnet-101-i224.tar.gz) |
| ResNet-152   | 224x224       |    200   | 78.7 | 94.3 | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet152_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet/resnet-152-i224.tar.gz) |

#### ResNet-RS models trained with various settings

We support state-of-the-art [ResNet-RS](https://arxiv.org/abs/2103.07579) image
classification models with features:

* ResNet-RS architectural changes and Swish activation. (Note that ResNet-RS
  adopts ReLU activation in the paper.)
* Regularization methods include 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](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification).

| Model     | Resolution | Params (M) | Top-1 | Top-5 | Download |
| --------- | :--------: | ---------: | ----: | ----: | --------:|
| ResNet-RS-50 | 160x160    | 35.7    | 79.1  | 94.5  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs50_i160.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-50-i160.tar.gz) |
| ResNet-RS-101 | 160x160    | 63.7    | 80.2  | 94.9  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs101_i160.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-101-i160.tar.gz) |
| ResNet-RS-101 | 192x192    | 63.7    | 81.3  | 95.6  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs101_i192.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-101-i192.tar.gz) |
| ResNet-RS-152 | 192x192    | 86.8    | 81.9  | 95.8  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs152_i192.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-152-i192.tar.gz) |
| ResNet-RS-152 | 224x224    | 86.8    | 82.5  | 96.1  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs152_i224.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-152-i224.tar.gz) |
| ResNet-RS-152 | 256x256    | 86.8    | 83.1  | 96.3  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs152_i256.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-152-i256.tar.gz) |
| ResNet-RS-200 | 256x256    | 93.4    | 83.5  | 96.6  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs200_i256.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-200-i256.tar.gz) |
| ResNet-RS-270 | 256x256    | 130.1    | 83.6  | 96.6  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs270_i256.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-270-i256.tar.gz) |
| ResNet-RS-350 | 256x256    |  164.3   | 83.7  | 96.7  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs350_i256.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-350-i256.tar.gz) |
| ResNet-RS-350 | 320x320    | 164.3   | 84.2  | 96.9  | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnetrs350_i320.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-350-i320.tar.gz) |


#### Vision Transformer (ViT)

We support [ViT](https://arxiv.org/abs/2010.11929) and [DEIT](https://arxiv.org/abs/2012.12877) implementations.
ViT models trained under the DEIT settings:

model     | resolution | Top-1 | Top-5 | Download |
--------- | :--------: | ----: | ----: | :-------: |
ViT-ti16  | 224x224    | 73.4  | 91.9  | [ckpt](https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-ti16.tar.gz) |
ViT-s16  | 224x224    | 79.4  | 94.7  | [ckpt](https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-s16.tar.gz) |
ViT-b16  | 224x224    | 81.8  | 95.8  | [ckpt](https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-b16.tar.gz) |
ViT-l16  | 224x224    | 82.2  | 95.8  | [ckpt](https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-l16.tar.gz) |


## Object Detection and Instance Segmentation

### Common Settings and Notes

* We provide models adopting [ResNet-FPN](https://arxiv.org/abs/1612.03144) and
  [SpineNet](https://arxiv.org/abs/1912.05027) backbones  based on detection frameworks:
  * [RetinaNet](https://arxiv.org/abs/1708.02002) and [RetinaNet-RS](https://arxiv.org/abs/2107.00057)
  * [Mask R-CNN](https://arxiv.org/abs/1703.06870)
  * [Cascade RCNN](https://arxiv.org/abs/1712.00726) and [Cascade RCNN-RS](https://arxiv.org/abs/2107.00057)
* Models are all trained on [COCO](https://cocodataset.org/) train2017 and
evaluated on [COCO](https://cocodataset.org/) val2017.
* Training details:
  * Models finetuned from [ImageNet](https://www.image-net.org/) pre-trained
    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 a stepwise learning rate that decays at the last 30
    and 10 epochs.
  * We use a square image as input by resizing the long side of an image to the
    target size and then padding the short side with zeros.

### COCO Object Detection Baselines

#### RetinaNet (ImageNet pretrained)

| 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](https://github.com/tensorflow/models/blob/master/official/vision/configs/retinanet.py#L187-L258) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/retinanet/retinanet-resnet50fpn.tar.gz) |

#### RetinaNet (Trained from scratch) with 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](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/retinanet/coco_spinenet49_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/spinenet/spinenet-49-i640.tar.gz) \| [TB.dev](https://tensorboard.dev/experiment/n2UN83TkTdyKZn3slCWulg/#scalars&_smoothingWeight=0)|
| SpineNet-96  | 1024x1024     |    500    | 265.4 | 43.0 | 48.5 |  [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/retinanet/coco_spinenet96_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/spinenet/spinenet-96-i1024.tar.gz) \| [TB.dev](https://tensorboard.dev/experiment/n2UN83TkTdyKZn3slCWulg/#scalars&_smoothingWeight=0)|
| SpineNet-143 | 1280x1280     |    500    | 524.0 | 67.0 | 50.0 | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/retinanet/coco_spinenet143_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/spinenet/spinenet-143-i1280.tar.gz) \| [TB.dev](https://tensorboard.dev/experiment/n2UN83TkTdyKZn3slCWulg/#scalars&_smoothingWeight=0)|

#### Mobile-size RetinaNet (Trained from scratch):

| Backbone    | Resolution | Epochs | FLOPs (B) | Params (M) | Box AP | Download |
| ----------- | :--------: | -----: | --------: | ---------: | -----: | --------:|
| MobileNetv2 | 256x256    | 600    | -         | 2.27       | 23.5   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/retinanet/coco_mobilenetv2_tpu.yaml) |
| Mobile SpineNet-49  | 384x384    | 600    | 1.0      | 2.32       | 28.1   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/retinanet/coco_spinenet49_mobile_tpu.yaml) \| [ckpt](https://storage.googleapis.com/tf_model_garden/vision/retinanet/spinenet49mobile.tar.gz) |

### Instance Segmentation Baselines

#### Mask R-CNN (Trained from scratch)

| 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](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/r50fpn_640_coco_scratch_tpu4x4.yaml) |
| SpineNet-49  | 640x640       |  350    | 215.7      | 40.8       | 42.6   | 37.9    | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/coco_spinenet49_mrcnn_tpu.yaml) |
| SpineNet-96  | 1024x1024  | 500    | 315.0     | 55.2       | 48.1   | 42.4    | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/coco_spinenet96_mrcnn_tpu.yaml) |
| SpineNet-143 | 1280x1280  | 500    | 498.8     | 79.2       | 49.3   | 43.4    | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/coco_spinenet143_mrcnn_tpu.yaml) |


#### Cascade RCNN-RS (Trained from scratch)

| Backbone     | Resolution | Epochs | Params (M) | Box AP | Mask AP | Download
------------ | :--------: | -----: | ---------: | -----: | ------: | -------:
| SpineNet-49  | 640x640    | 500    | 56.4       | 46.4   | 40.0    | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/coco_spinenet49_cascadercnn_tpu.yaml)|
| SpineNet-96 | 1024x1024  | 500    | 70.8   | 50.9   | 43.8    | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/coco_spinenet96_cascadercnn_tpu.yaml)|
| SpineNet-143 | 1280x1280  | 500    | 94.9       | 51.9   | 45.0    | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/maskrcnn/coco_spinenet143_cascadercnn_tpu.yaml)|

## Semantic Segmentation

* We support [DeepLabV3](https://arxiv.org/pdf/1706.05587.pdf) and
  [DeepLabV3+](https://arxiv.org/pdf/1802.02611.pdf) architectures, with
  Dilated ResNet backbones.
* Backbones are pre-trained on ImageNet.

### PASCAL-VOC

| Model      | Backbone           | Resolution | Steps | mIoU | Download |
| ---------- | :----------------: | :--------: | ----: | ---: | --------:|
| DeepLabV3  | Dilated Resnet-101 | 512x512    | 30k   | 78.7 |          |
| DeepLabV3+ | Dilated Resnet-101 | 512x512    | 30k   | 79.2 | [ckpt](https://storage.googleapis.com/tf_model_garden/vision/deeplabv3plus/dilated-resnet-101-deeplabv3plus.tar.gz) |

### CITYSCAPES

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

## Video Classification

### Common Settings and Notes

*   We provide models for video classification with backbones:
    *   SlowOnly in
        [SlowFast Networks for Video Recognition](https://arxiv.org/abs/1812.03982).
    *   ResNet-3D (R3D) in
        [Spatiotemporal Contrastive Video Representation Learning](https://arxiv.org/abs/2008.03800).
    *   ResNet-3D-RS (R3D-RS) in
        [Revisiting 3D ResNets for Video Recognition](https://arxiv.org/pdf/2109.01696.pdf).
    *   Mobile Video Networks (MoViNets) in
        [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://arxiv.org/abs/2103.11511).

* Training and evaluation details (SlowFast and ResNet):
  * All models are trained from scratch with vision modality (RGB) for 200
    epochs.
  * We use a batch size of 1024 and cosine learning rate decay with a linear warmup
    in the first 5 epochs.
  * We follow [SlowFast](https://arxiv.org/abs/1812.03982) to perform a 30-view
    evaluation.

### Kinetics-400 Action Recognition Baselines

| Model    | Input (frame x stride) |  Top-1  |  Top-5  | Download |
| -------- |:----------------------:|--------:|--------:|---------:|
| SlowOnly | 8 x 8                  |  74.1   |  91.4   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/video_classification/k400_slowonly8x8_tpu.yaml) |
| SlowOnly | 16 x 4                 |  75.6   |  92.1   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/video_classification/k400_slowonly16x4_tpu.yaml) |
| R3D-50   | 32 x 2                 |  77.0   |  93.0   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/video_classification/k400_3d-resnet50_tpu.yaml) |
| R3D-RS-50   | 32 x 2                 |  78.2   |  93.7   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/video_classification/k400_resnet3drs_50_tpu.yaml) |
| 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 | -

### Kinetics-600 Action Recognition Baselines

| Model    | Input (frame x stride) |  Top-1  |  Top-5  | Download |
| -------- |:----------------------:|--------:|--------:|---------:|
| SlowOnly | 8 x 8                  |  77.3   |  93.6   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/video_classification/k600_slowonly8x8_tpu.yaml) |
| R3D-50   | 32 x 2                 |  79.5   |  94.8   | [config](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/video_classification/k600_3d-resnet50_tpu.yaml) |
| 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](https://github.com/tensorflow/models/blob/master/official/projects/movinet/configs/yaml/movinet_a0_k600_8x8.yaml) |
| MoViNet-A1-Base | 50 x 5            | 76.69 | 93.40 | [config](https://github.com/tensorflow/models/blob/master/official/projects/movinet/configs/yaml/movinet_a1_k600_8x8.yaml) |
| MoViNet-A2-Base | 50 x 5            | 78.62 | 94.17 | [config](https://github.com/tensorflow/models/blob/master/official/projects/movinet/configs/yaml/movinet_a2_k600_8x8.yaml) |
| MoViNet-A3-Base | 120 x 2           | 81.79 | 95.67 | [config](https://github.com/tensorflow/models/blob/master/official/projects/movinet/configs/yaml/movinet_a3_k600_8x8.yaml) |
| MoViNet-A4-Base | 80 x 3            | 83.48 | 96.16 | [config](https://github.com/tensorflow/models/blob/master/official/projects/movinet/configs/yaml/movinet_a4_k600_8x8.yaml) |
| MoViNet-A5-Base | 120 x 2           | 84.27 | 96.39 | [config](https://github.com/tensorflow/models/blob/master/official/projects/movinet/configs/yaml/movinet_a5_k600_8x8.yaml) |
