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# Model Zoo

All models and part of benchmark results are recorded below.

## Pre-trained models

Algorithm | Config | Download
--- | --- | ---
Relative Location | relative-loc_resnet50_8xb64-steplr-70e_in1k | model | log
Rotation Prediction | rotation-pred_resnet50_8xb16-steplr-70e_in1k | model | log
DeepCluster | deepcluster-sobel_resnet50_8xb64-steplr-200e_in1k | model
NPID | npid_resnet50_8xb32-steplr-200e_in1k | model | log
ODC | odc_resnet50_8xb64-steplr-440e_in1k | model | log
SimCLR | simclr_resnet50_8xb32-coslr-200e_in1k | model | log
| simclr_resnet50_16xb256-coslr-200e_in1k | model | log
MoCo v2 | mocov2_resnet50_8xb32-coslr-200e_in1k | model | log
BYOL | byol_resnet50_8xb32-accum16-coslr-200e_in1k | model | log
| byol_resnet50_16xb256-coslr-200e_in1k | model | log
| byol_resnet50_8xb32-accum16-coslr-300e_in1k | model | log
SwAV | swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96 | model | log
DenseCL | densecl_resnet50_8xb32-coslr-200e_in1k | model | log
SimSiam | simsiam_resnet50_8xb32-coslr-100e_in1k | model | log
| simsiam_resnet50_8xb32-coslr-200e_in1k | model | log
BarlowTwins | barlowtwins_resnet50_8xb256-coslr-300e_in1k | model | log
MoCo v3 | mocov3_vit-small-p16_32xb128-fp16-coslr-300e_in1k-224 | model | log
MAE | mae_vit-base-p16_8xb512-coslr-400e_in1k | model | log
SimMIM | simmim_swin-base_16xb128-coslr-100e_in1k-192 | model | log
CAE | cae_vit-base-p16_8xb256-fp16-coslr-300e_in1k | model | log

Remarks:

- The training details are recorded in the config names.

- You can click algorithm name to obtain more information.

## Benchmarks

In the following tables, we only display ImageNet linear evaluation, ImageNet fine-tuning, COCO17 object detection and instance segmentation, and PASCAL VOC12 Aug semantic segmentation. You can click algorithm name above to check more comprehensive benchmark results.

### ImageNet Linear Evaluation

If not specified, we use linear evaluation setting from MoCo as default. Other settings are mentioned in Remarks.

Algorithm | Config | Remarks | Top-1 (%)
--- | --- | --- | ---
Relative Location | relative-loc_resnet50_8xb64-steplr-70e_in1k |  | 38.78
Rotation Prediction | rotation-pred_resnet50_8xb16-steplr-70e_in1k |  | 48.12
DeepCluster | deepcluster-sobel_resnet50_8xb64-steplr-200e_in1k.py |  | 46.92
NPID | npid_resnet50_8xb32-steplr-200e_in1k |  | 58.97
ODC | odc_resnet50_8xb64-steplr-440e_in1k |  | 53.43
SimCLR | simclr_resnet50_8xb32-coslr-200e_in1k | SimSiam paper setting | 62.56
| simclr_resnet50_16xb256-coslr-200e_in1k | SimSiam paper setting | 66.66
MoCo v2 | mocov2_resnet50_8xb32-coslr-200e_in1k |  | 67.58
BYOL | byol_resnet50_8xb32-accum16-coslr-200e_in1k | SimSiam paper setting | 71.72
| byol_resnet50_16xb256-coslr-200e_in1k | SimSiam paper setting | 71.88
| byol_resnet50_8xb32-accum16-coslr-300e_in1k | SimSiam paper setting | 72.93
SwAV | swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96 | SwAV paper setting | 70.47
DenseCL | densecl_resnet50_8xb32-coslr-200e_in1k |  | 63.62
SimSiam | simsiam_resnet50_8xb32-coslr-100e_in1k | SimSiam paper setting | 68.28
| simsiam_resnet50_8xb32-coslr-200e_in1k | SimSiam paper setting | 69.84
Barlow Twins | barlowtwins_resnet50_8xb256-coslr-300e_in1k | Barlow Twins paper setting | 71.66
MoCo v3 | mocov3_vit-small-p16_32xb128-fp16-coslr-300e_in1k-224 | MoCo v3 paper setting | 73.19

### ImageNet Fine-tuning

Algorithm | Config | Remarks | Top-1 (%)
--- | --- | --- | ---
MAE | mae_vit-base-p16_8xb512-coslr-400e_in1k |  | 83.1
SimMIM | simmim_swin-base_16xb128-coslr-100e_in1k-192 |  | 82.9
CAE | cae_vit-base-p16_8xb256-fp16-coslr-300e_in1k |  | 83.2

### COCO17 Object Detection and Instance Segmentation

In COCO17 object detection and instance segmentation task, we choose the evaluation protocol from MoCo, with Mask-RCNN FPN architecture. The results below are fine-tuned with the same config.

Algorithm | Config | mAP (Box) | mAP (Mask)
--- | --- | --- | ---
Relative Location | relative-loc_resnet50_8xb64-steplr-70e_in1k | 37.5 | 33.7
Rotation Prediction | rotation-pred_resnet50_8xb16-steplr-70e_in1k | 37.9 | 34.2
NPID | npid_resnet50_8xb32-steplr-200e_in1k | 38.5 | 34.6
SimCLR | simclr_resnet50_8xb32-coslr-200e_in1k | 38.7 | 34.9
MoCo v2 | mocov2_resnet50_8xb32-coslr-200e_in1k | 40.2 | 36.1
BYOL | byol_resnet50_8xb32-accum16-coslr-200e_in1k | 40.9 | 36.8
SwAV | swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96 | 40.2 | 36.3
SimSiam | simsiam_resnet50_8xb32-coslr-100e_in1k | 38.6 | 34.6
| simsiam_resnet50_8xb32-coslr-200e_in1k | 38.8 | 34.9

### Pascal VOC12 Aug Semantic Segmentation

In Pascal VOC12 Aug semantic segmentation task, we choose the evaluation protocol from MMSeg, with FCN architecture. The results below are fine-tuned with the same config.

Algorithm | Config | mIOU
--- | --- | ---
Relative Location | relative-loc_resnet50_8xb64-steplr-70e_in1k | 63.49
Rotation Prediction | rotation-pred_resnet50_8xb16-steplr-70e_in1k | 64.31
NPID | npid_resnet50_8xb32-steplr-200e_in1k | 65.45
SimCLR | simclr_resnet50_8xb32-coslr-200e_in1k | 64.03
MoCo v2 | mocov2_resnet50_8xb32-coslr-200e_in1k | 67.55
BYOL | byol_resnet50_8xb32-accum16-coslr-200e_in1k | 67.16
SwAV | swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96 | 63.73
DenseCL | densecl_resnet50_8xb32-coslr-200e_in1k | 69.47
SimSiam | simsiam_resnet50_8xb32-coslr-100e_in1k | 48.35
| simsiam_resnet50_8xb32-coslr-200e_in1k | 46.27