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add model links

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lorenzo 2019-05-24 17:18:19 +02:00
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README.md
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@ -20,6 +20,9 @@ We further share insights on our model of uncertainty in case of limited observa
```
Add link paper
Add link video
![overview_paper](docs/pull.png)
# Setup
@ -49,7 +52,8 @@ mkdir arrays models kitti nuscenes logs
```
### Pre-trained Models
* Download a MonoLoco pre-trained model from Google Drive: ADD LINK and save it in `data/models`
* Download a MonoLoco pre-trained model from
[Google Drive](https://drive.google.com/open?id=1F7UG1HPXGlDD_qL-AN5cv2Eg-mhdQkwv) and save it in `data/models`
* Download a Pifpaf pre-trained model from [openpifpaf](https://github.com/vita-epfl/openpifpaf) project
and save it into `data/models`
@ -81,7 +85,8 @@ Output options include json files and/or visualization of the predictions on the
* In case you provide a ground-truth json file to compare the predictions of MonoLoco,
the script will match every detection using Intersection over Union metric.
The ground truth file can be generated using the subparser `prep` and called with the command `--path_gt`.
Check preprocess section for more details.
Check preprocess section for more details or download the file from
[here](https://drive.google.com/open?id=1F7UG1HPXGlDD_qL-AN5cv2Eg-mhdQkwv).
* In case you don't provide a ground-truth file, the script will look for a predefined path.
If it does not find the file, it will generate images
@ -119,13 +124,13 @@ To extract pifpaf joints, you also need to download training images, put it in a
data/kitti/images`
#### 2) nuScenes dataset
Download nuScenes dataset (any version: Mini, Teaser or TrainVal) from [nuScenes](https://www.nuscenes.org/download),
Download nuScenes dataset from [nuScenes](https://www.nuscenes.org/download) (either Mini or Full),
save it anywhere and soft link it in `data/nuscenes`
### Annotations to preprocess
MonoLoco is trained using 2D human pose joints. To create them run pifaf over KITTI or nuScenes training images.
You can create them running the predict script and using `--network pifpaf`.
You can create them running the predict script and using `--networks pifpaf`.
### Inputs joints for training
MonoLoco is trained using 2D human pose joints matched with the ground truth location provided by