update model zoo links

xingyizhou · Nov 20, 2022 · bfc8b72 · bfc8b72
1 parent 8745e01
commit bfc8b72
Showing 1 changed file with 15 additions and 15 deletions.
diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md
@@ -5,7 +5,7 @@
 - Multiscale training is used by default in all models. The results are all reported using single-scale testing. 
 - We report runtime on our local workstation with a TitanXp GPU and a Titan RTX GPU.
 - All models are trained on 8-GPU servers by default. The 1280 models are trained on 24G GPUs. Reducing the batchsize with the linear learning rate rule should be fine.
-- All models can be downloaded directly from [Google drive](https://drive.google.com/drive/folders/1eae1cTX8tvIaCeof36sBgxrXEXALYlf-?usp=sharing).
+- All models can be downloaded directly from [Google drive](https://drive.google.com/drive/folders/1meZIsz8E3Ia9CRxLOAULDLeYrKMhhjJE).
 
 
 ## COCO
@@ -14,8 +14,8 @@
 
 | Model                                     | val mAP | FPS (Titan Xp/ Titan RTX) | links     |
 |-------------------------------------------|---------|---------|-----------|
-| CenterNet-S4_DLA_8x                       |  42.5   | 50 / 71 |[config](../configs/CenterNet-S4_DLA_8x.yaml)/[model](https://drive.google.com/file/d/1lNBhVHnZAEBRD66MFaHjm5Ij6Z4KYrJq/view?usp=sharing)|
-| CenterNet-FPN_R50_1x                      |  40.2   | 20 / 24 |[config](../configs/CenterNet-FPN_R50_1x.yaml)/[model](https://drive.google.com/file/d/1rVG1YTthMXvutC6jr9KoE2DthT5-jhGj/view?usp=sharing)|
+| CenterNet-S4_DLA_8x                       |  42.5   | 50 / 71 |[config](../configs/CenterNet-S4_DLA_8x.yaml)/[model](https://drive.google.com/file/d/1AVfs9OoLePk_sqTPvqdRi1cXmO2cD0W_)|
+| CenterNet-FPN_R50_1x                      |  40.2   | 20 / 24 |[config](../configs/CenterNet-FPN_R50_1x.yaml)/[model](https://drive.google.com/file/d/1iYlmjsBt9YIcaI8NzEwiMoaDDMHRmcR9)|
 
 #### Note
 
@@ -33,14 +33,14 @@
 | Model                                     | val mAP | FPS (Titan Xp/ Titan RTX) | links     |
 |-------------------------------------------|---------|---------|-----------|
 | CenterNet2-F_R50_1x                       |   41.7  | 22 / 27  |[config](../configs/CenterNet2-F_R50_1x.yaml)/[model](X)|
-| CenterNet2_R50_1x                         |  42.9   | 18 / 24 |[config](../configs/CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1Osu1J_sskt_1FaGdfJKa4vd2N71TWS9W/view?usp=sharing)|
-| CenterNet2_X101-DCN_2x                    |  49.9   | 6 / 8  |[config](../configs/CenterNet2_X101-DCN_2x.yaml)/[model](https://drive.google.com/file/d/1IHgpUHVJWpvMuFUUetgKWsw27pRNN2oK/view?usp=sharing)|
-| CenterNet2_DLA-BiFPN-P3_4x                |  43.8   | 40 / 50|[config](../configs/CenterNet2_DLA-BiFPN-P3_4x.yaml)/[model](https://drive.google.com/file/d/12GUNlDW9RmOs40UEMSiiUsk5QK_lpGsE/view?usp=sharing)|
-| CenterNet2_DLA-BiFPN-P3_24x               |  45.6   | 40 / 50  |[config](../configs/CenterNet2_DLA-BiFPN-P3_24x.yaml)/[model](https://drive.google.com/file/d/15ZES1ySxubDPzKsHPA7pYg8o_Vwmf-Mb/view?usp=sharing)|
-| CenterNet2_R2-101-DCN_896_4x              |  51.2   | 9 / 13 |[config](../configs/CenterNet2_R2-101-DCN_896_4x.yaml)/[model](https://drive.google.com/file/d/1S7_GE8ZDQBWuLEfKHkxzeF3KBsxsbABg/view?usp=sharing)|
-| CenterNet2_R2-101-DCN-BiFPN_1280_4x       |  52.9   | 6 / 8 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml)/[model](https://drive.google.com/file/d/14EBHNMagBCNTQjOXcHoZwLYIi2lFIm7F/view?usp=sharing)|
-| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST |  56.1   | 3 / 5 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml)/[model](https://drive.google.com/file/d/11ww9VlOi_nhpdsU_vBAecSxBU0dR_JzW/view?usp=sharing)|
-| CenterNet2_DLA-BiFPN-P5_640_24x_ST        |  49.2   | 33 / 38 |[config](../configs/CenterNet2_DLA-BiFPN-P5_640_24x_ST.yaml)/[model](https://drive.google.com/file/d/1qsHp2HrM1u8WrtBzF5S0oCoLMz-B40wk/view?usp=sharing)|
+| CenterNet2_R50_1x                         |  42.9   | 18 / 24 |[config](../configs/CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1Qn0E_F1cmXtKPEdyZ_lSt-bnM9NueQpq)|
+| CenterNet2_X101-DCN_2x                    |  49.9   | 6 / 8  |[config](../configs/CenterNet2_X101-DCN_2x.yaml)/[model](https://drive.google.com/file/d/1yuJbIlUgMiXdaDWRWArcsRsSoHti9e1y)|
+| CenterNet2_DLA-BiFPN-P3_4x                |  43.8   | 40 / 50|[config](../configs/CenterNet2_DLA-BiFPN-P3_4x.yaml)/[model](https://drive.google.com/file/d/1UGrnOE0W8Tgu6ffcCOQEbeUgThtDkbuQ)|
+| CenterNet2_DLA-BiFPN-P3_24x               |  45.6   | 40 / 50  |[config](../configs/CenterNet2_DLA-BiFPN-P3_24x.yaml)/[model](https://drive.google.com/file/d/17osgvr_Zhp9SS2uMa_YLiKwkKJIDtwPZ)|
+| CenterNet2_R2-101-DCN_896_4x              |  51.2   | 9 / 13 |[config](../configs/CenterNet2_R2-101-DCN_896_4x.yaml)/[model](https://drive.google.com/file/d/1YiJm7UtMstl63E8I4qQ8owteYC5zRFuQ)|
+| CenterNet2_R2-101-DCN-BiFPN_1280_4x       |  52.9   | 6 / 8 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml)/[model](https://drive.google.com/file/d/1BIfEH04Lm3EvW9ov76yEPntUOJxaVoKd)|
+| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST |  56.1   | 3 / 5 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml)/[model](https://drive.google.com/file/d/1GZyzJLB3FTcs8C7MpZRQWw44liYPyOMD)|
+| CenterNet2_DLA-BiFPN-P5_640_24x_ST        |  49.2   | 33 / 38 |[config](../configs/CenterNet2_DLA-BiFPN-P5_640_24x_ST.yaml)/[model](https://drive.google.com/file/d/1pGXpnHhvi66my_p5dASTnTjvaaj0FEvE)|
 
 #### Note
 
@@ -49,15 +49,15 @@
 - `CenterNet2_DLA-BiFPN-P3_24x` is trained by repeating the `4x` schedule (starting from learning rate 0.01) 6 times.
 - R2 means [Res2Net](https://github.com/Res2Net/Res2Net-detectron2) backbone. To train Res2Net models, you need to download the ImageNet pre-trained weight [here](https://github.com/Res2Net/Res2Net-detectron2) and place it in `output/r2_101.pkl`.
 - The last 4 models in the table are trained with the EfficientDet-style resize-and-crop augmentation, instead of the default random resizing short edge in detectron2. We found this trains faster (per-iteration) and gives better performance under a long schedule.
-- `_ST` means using [self-training](https://arxiv.org/abs/2006.06882) using pseudo-labels produced by [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) on COCO unlabeled images, with a hard score threshold 0.5. Our processed pseudo-labels can be downloaded [here](https://drive.google.com/file/d/1LMBjtHhLp6dYf6MjwEQmzCLWQLkmWPpw/view?usp=sharing).
+- `_ST` means using [self-training](https://arxiv.org/abs/2006.06882) using pseudo-labels produced by [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) on COCO unlabeled images, with a hard score threshold 0.5. Our processed pseudo-labels can be downloaded [here](https://drive.google.com/file/d/1R9tHlUaIrujmK6T08yJ0T77b2XzekisC).
 - `CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST` finetunes from `CenterNet2_R2-101-DCN-BiFPN_1280_4x` for an additional `4x` schedule with the self-training data. It is trained under `1280x1280` but tested under `1560x1560`.
 
 ## LVIS v1
 
 | Model                                     |  val mAP box | links     |
 |-------------------------------------------|--------------|-----------|
-| LVIS_CenterNet2_R50_1x                    |  26.5        |[config](../configs/LVIS_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1gT9e-tNw8uzEBaCadQuoOOP2TEYa4kKP/view?usp=sharing)|
-| LVIS_CenterNet2_R50_Fed_1x            |  28.3        |[config](../configs/LVIS_CenterNet2_R50_Fed_1x.yaml)/[model](https://drive.google.com/file/d/1a9UjheMCKax0qAKEwPVpq2ZHN6vpqJv8/view?usp=sharing)|
+| LVIS_CenterNet2_R50_1x                    |  26.5        |[config](../configs/LVIS_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1oOOKEDQIWW19AHhfnTb7HYZ3Z9gkZn_K)|
+| LVIS_CenterNet2_R50_Fed_1x            |  28.3        |[config](../configs/LVIS_CenterNet2_R50_Fed_1x.yaml)/[model](https://drive.google.com/file/d/1ETurGA7KIC5XMkMBI8MOIMDD_iJyMTif)|
 
 - The models are trained with repeat-factor sampling.
 - `LVIS_CenterNet2_R50_Fed_1x` is CenterNet2 with our federated loss. Check our Appendix D of our [paper](https://arxiv.org/abs/2103.07461) or our [technical report at LVIS challenge](https://www.lvisdataset.org/assets/challenge_reports/2020/CenterNet2.pdf) for references.
@@ -66,7 +66,7 @@
 
 | Model                                     |  val mAP| links     |
 |-------------------------------------------|---------|-----------|
-| O365_CenterNet2_R50_1x                    |  22.6   |[config](../configs/O365_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/18fG6xGchAlpNp5sx8RAtwadGkS-gdIBU/view?usp=sharing)|
+| O365_CenterNet2_R50_1x                    |  22.6   |[config](../configs/O365_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/11d1Qx75otBAQQL2raxMTVJb17Qr56M3O)|
 
 #### Note
 - Objects365 dataset can be downloaded [here](https://www.objects365.org/overview.html).