# TF Vision Model Garden

## Introduction
TF Vision model garden provides a large collection of baselines and checkpoints for image classification, object detection, and instance 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 |
| ResNet-50    | 224x224       |    200   | 77.1 | 93.5 | config |
| ResNet-101   | 224x224       |    200   | 78.3 | 94.2 | config |
| ResNet-152   | 224x224       |    200   | 78.7 | 94.3 | config |


#### ResNet-RS models trained with settings including:

*   ResNet-RS architectural changes and Swish activation.
*   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](https://github.com/tensorflow/models/blob/master/official/vision/beta/configs/experiments/image_classification)

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


## Object Detection and Instance Segmentation
### Common Settings and Notes
* We provide models based on two detection frameworks, [RetinaNet](https://arxiv.org/abs/1708.02002) or [Mask R-CNN](https://arxiv.org/abs/1703.06870), and two backbones, [ResNet-FPN](https://arxiv.org/abs/1612.03144) or [SpineNet](https://arxiv.org/abs/1912.05027).
* Models are all trained on COCO train2017 and evaluated on COCO val2017.
* 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.

### 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       |    36    | 97.0 | 34.0 | 37.3 | config|

#### 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/beta/configs/experiments/retinanet/coco_spinenet49_tpu.yaml)|
| SpineNet-96  | 1024x1024     |    500    | 265.4 | 43.0 | 48.5 |  [config](https://github.com/tensorflow/models/blob/master/official/vision/beta/configs/experiments/retinanet/coco_spinenet96_tpu.yaml) |
| SpineNet-143 | 1280x1280     |    500    | 524.0 | 67.0 | 50.0 | [config](https://github.com/tensorflow/models/blob/master/official/vision/beta/configs/experiments/retinanet/coco_spinenet143_tpu.yaml)|


### Instance Segmentation Baselines
#### Mask R-CNN (ImageNet pretrained)


#### Mask R-CNN (Trained from scratch)
| backbone        | resolution    | epochs  | FLOPs (B)  | params (M)  |  box AP |  mask AP  |   download |
| ------------ |:-------------:| ---------:|-----------:|--------:|--------:|-----------:|-----------:|
| SpineNet-49  | 640x640       |    350    | 215.7 | 40.8 | 42.6 | 37.9 | config |


## Video Classification
### Common Settings and Notes
* We provide models for video classification with two backbones: [SlowOnly](https://arxiv.org/abs/1812.03982) and 3D-ResNet (R3D) used in [Spatiotemporal Contrastive Video Representation Learning](https://arxiv.org/abs/2008.03800).
* Training and evaluation details:
  * 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](https://arxiv.org/abs/1812.03982) to perform 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/beta/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/beta/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/beta/configs/experiments/video_classification/k400_3d-resnet50_tpu.yaml) |

### 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/beta/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/beta/configs/experiments/video_classification/k600_3d-resnet50_tpu.yaml) |
