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update modelzoo links
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Xingyi Zhou authored and Xingyi Zhou committed Nov 16, 2022
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -13,7 +13,7 @@ Simultaneous object detection and tracking using center points:
year={2020}
}

Contact: [[email protected]](mailto:[email protected]). Any questions or discussion are welcome!
Contact: [[email protected]](mailto:[email protected]). Any questions or discussion are welcome!

## Abstract
Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection. In this paper, we present a simultaneous detection and tracking algorithm that is simpler, faster, and more accurate than the state of the art. Our tracker, CenterTrack, applies a detection model to a pair of images and detections from the prior frame. Given this minimal input, CenterTrack localizes objects and predicts their associations with the previous frame. That's it. CenterTrack is simple, online (no peeking into the future), and real-time. It achieves 67.3% MOTA on the MOT17 challenge at 22 FPS and 89.4% MOTA on the KITTI tracking benchmark at 15 FPS, setting a new state of the art on both datasets. CenterTrack is easily extended to monocular 3D tracking by regressing additional 3D attributes. Using monocular video input, it achieves 28.3% [email protected] on the newly released nuScenes 3D tracking benchmark, substantially outperforming the monocular baseline on this benchmark while running at 28 FPS.
Expand Down Expand Up @@ -66,8 +66,8 @@ Please refer to [INSTALL.md](readme/INSTALL.md) for installation instructions.

We support demo for videos, webcam, and image folders.

First, download the models (By default, [nuscenes\_3d\_tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt) for monocular 3D tracking, [coco_tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40) for 80-category detection and
[coco_pose_tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) for pose tracking)
First, download the models (By default, [nuscenes\_3d\_tracking](https://drive.google.com/file/d/1gPQFzqneDtT_PjJRRuyskRsNTRHXovw1) for monocular 3D tracking, [coco_tracking](https://drive.google.com/file/d/11DEfWa0TKYzNqY3CXR51WVvjMb4oRl08) for 80-category detection and
[coco_pose_tracking](https://drive.google.com/file/d/1yGFC_Q9wzSHL1d4eZW_44EBB2H42YKYt) for pose tracking)
from the [Model zoo](readme/MODEL_ZOO.md) and put them in `CenterNet_ROOT/models/`.

We provide a video clip from the [nuScenes dataset](https://www.nuscenes.org/?externalData=all&mapData=all&modalities=Any) in `videos/nuscenes_mini.mp4`.
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26 changes: 13 additions & 13 deletions readme/MODEL_ZOO.md
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Expand Up @@ -5,19 +5,19 @@
- The experiments are run with PyTorch 1.0, CUDA 10.0, and CUDNN 7.5.
- Training times are measured on our servers with TITAN V GPUs (12 GB Memory).
- Testing times are measured on our local machine with TITAN Xp GPU.
- The models can be downloaded directly from [Google drive](https://drive.google.com/open?id=1u4n_WwvDOJz4ws_KKQUMCpHXyvA6tj-I).
- The models can be downloaded directly from [Google drive](https://drive.google.com/drive/folders/1y_CWlbboW_dfOx6zT9MU4ugLaLc6FEE8).

## 2D bounding box Tracking

### MOT17

| Model | GPUs |Train time| Test time | Valication MOTA | Test MOTA | Download |
|-----------------------|------|----------|-----------|------------------|------------|----------|
| [mot17_fulltrain](../experiments/mot17_fulltrain.sh) | 4 | 4h | 45ms | - |67.3 (Private Detection)| [model](https://drive.google.com/open?id=1h_8Ts11rf0GQ4_n6FgmCeBuFcWrRjJfa) |
| [mot17_fulltrain_sc](../experiments/mot17_fulltrain_sc.sh) | 4 | 4h | 45ms | - |61.4 (Public Detection) | [model](https://drive.google.com/open?id=1WXBlzHsxHQTELvusJSgEWw_wydC6u7XB) |
| [mot17_half](../experiments/mot17_half.sh) | 4 | 2h | 45ms | 66.1 | - | [model](https://drive.google.com/open?id=1sf1bWJ1LutwQ_wp176nd2Y3HII9WeFf0) |
| [mot17_half_sc](../experiments/mot17_half_sc.sh) | 4 | 2h | 45ms | 60.7 | - | [model](https://drive.google.com/open?id=12xnXeY-kW3otNjCoQtyJAayHFiQdTTAU) |
| [crowdhuman](../experiments/crowdhuman.sh) | 4 | 21h | 45ms | 52.2 | - |[model](https://drive.google.com/open?id=1rIVl-jSG6oiBdiJmCvIAUOeasT7YllRZ) |
| [mot17_fulltrain](../experiments/mot17_fulltrain.sh) | 4 | 4h | 45ms | - |67.3 (Private Detection)| [model](https://drive.google.com/file/d/1JYqO_IEoHpd7JEzZRXZSVesnEL4e-tnf) |
| [mot17_fulltrain_sc](../experiments/mot17_fulltrain_sc.sh) | 4 | 4h | 45ms | - |61.4 (Public Detection) | [model](https://drive.google.com/file/d/17rtVMuFOnRzXj0_3egrFI5j-wc8XviDZ) |
| [mot17_half](../experiments/mot17_half.sh) | 4 | 2h | 45ms | 66.1 | - | [model](https://drive.google.com/file/d/1rJ0fzRcpRQPjaN17lcqfKgsz-wJRifHh) |
| [mot17_half_sc](../experiments/mot17_half_sc.sh) | 4 | 2h | 45ms | 60.7 | - | [model](https://drive.google.com/file/d/1o_cCo92WiVg8mgwyESd1Gg1AZYnq1iAJ) |
| [crowdhuman](../experiments/crowdhuman.sh) | 4 | 21h | 45ms | 52.2 | - |[model](https://drive.google.com/file/d/1SD31FLwbXArcX3LXnRCqh6RF-q38nO7f) |

#### Notes

Expand All @@ -37,9 +37,9 @@

| Model |GPUs| Train time| Test time | Validation MOTA | Test MOTA | Download |
|-----------------------|----|-----------|-----------|------------------|------------|-----------|
| [kitti_fulltrain](../experiments/kitti_fulltrain.sh) (flip)| 2 | 9h | 66 | - | 89.44 | [model](https://drive.google.com/open?id=1kBX4AgQj7R7HvgMdbgBcwvIac-IFp95h) |
| [kitti_half](../experiments/kitti_half.sh) | 2 | 4.5h | 40 | 88.7 | - | [model](https://drive.google.com/open?id=1_VtGal9UzZE3n3QcVa0brZ7nNAwqPzd-) |
| [kitti_half_sc](../experiments/kitti_half_sc.sh) | 2 | 4.5h | 40 | 84.5 | - | [model](https://drive.google.com/open?id=1Kv8kA7VLBqVst1ZcfB9gRH8TWs5oPN_h)|
| [kitti_fulltrain](../experiments/kitti_fulltrain.sh) (flip)| 2 | 9h | 66 | - | 89.44 | [model](https://drive.google.com/file/d/13oUEpeZ8bVQ6z7A6SH88de4SwLgh_kMB) |
| [kitti_half](../experiments/kitti_half.sh) | 2 | 4.5h | 40 | 88.7 | - | [model](https://drive.google.com/file/d/1AZiFG0p3VxB2pA_5XIkbue4ASfxaA3e1) |
| [kitti_half_sc](../experiments/kitti_half_sc.sh) | 2 | 4.5h | 40 | 84.5 | - | [model](https://drive.google.com/file/d/13rmdfi1rX3X7yFOndzyARTYO51uSNW0Z)|

#### Notes

Expand All @@ -54,8 +54,8 @@

| Model | GPUs |Train time| Test time | Val [email protected] | Val AMOTA | Val mAP | Download |
|--------------------------|------|----------|-----------|---------------|-----------|---------|-----------|
| [nuScenes_3Ddetection_e140](../experiments/nuScenes_3Ddetection_e140.sh)| 8 | 72h | 28ms | - | - | 30.27 | [model](https://drive.google.com/open?id=1ZSG9swryMEfBJ104WH8CP7kcypCobFlU) |
| [nuScenes_3Dtracking](../experiments/nuScenes_3Dtracking.sh) | 8 | 40h | 28ms | 28.3 | 6.8 | - | [model](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt) |
| [nuScenes_3Ddetection_e140](../experiments/nuScenes_3Ddetection_e140.sh)| 8 | 72h | 28ms | - | - | 30.27 | [model](https://drive.google.com/file/d/1o989b1tANh49uHhNbsCCJ5J57FGiaFut) |
| [nuScenes_3Dtracking](../experiments/nuScenes_3Dtracking.sh) | 8 | 40h | 28ms | 28.3 | 6.8 | - | [model](https://drive.google.com/file/d/1gPQFzqneDtT_PjJRRuyskRsNTRHXovw1) |

#### Notes

Expand All @@ -68,8 +68,8 @@

| Model |GPUs| Train time| Test time | Download |
|-----------------------|----|-----------|-----------|-----------|
| [coco_tracking](../experiments/coco_tracking.sh) | 8 | 39h | 30ms | [model](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40) |
| [coco_pose_tracking](../experiments/coco_pose_tracking.sh) | 8 | 19h | 33ms | [model](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH)|
| [coco_tracking](../experiments/coco_tracking.sh) | 8 | 39h | 30ms | [model](https://drive.google.com/file/d/11DEfWa0TKYzNqY3CXR51WVvjMb4oRl08) |
| [coco_pose_tracking](../experiments/coco_pose_tracking.sh) | 8 | 19h | 33ms | [model](https://drive.google.com/file/d/1yGFC_Q9wzSHL1d4eZW_44EBB2H42YKYt)|

- Both models are trained with the "training on static image data" technic in our paper.
- The models are not evaluated on any benchmarks since there are no suitable ones in this setting. We provide them for demo purpose only.

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