Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix MD formatting of list #1

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
Open

fix MD formatting of list #1

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0892059
fix MD formatting of list
nuest May 24, 2022
e3011e1
Update README.md
nuest May 24, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 12 additions & 12 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,22 +6,22 @@ Script to classify images of plants and animals with the image-based species rec

1. (recommended but not absolutely necessary) create and activate own (conda) environment
2. install packages
```
pip install torch torchvision
```
```
pip install torch torchvision
```
3. Clone this repository
```
git clone https://github.com/EibSReM/iNaturalist_Competition.git
```
and change to respective directory
```
git clone https://github.com/EibSReM/iNaturalist_Competition.git
```
and change to respective directory
5. Download pretrained models from the paper [here](https://cornell.box.com/s/bnyhq5lwobu6fgjrub44zle0pyjijbmw), mentioned in the [papers repository](https://github.com/visipedia/newt/tree/main/benchmark).
6. Adapt path to pytorch model and images (folder) in the `inference.py` script (we used the model in: cvpr21_newt_pretrained_models\cvpr21_newt_pretrained_models\pt\inat2021_supervised_large_from_scratch.pth.tar)
6. Adapt path to pytorch model and images (folder) in the `inference.py` script. We used the model in: `cvpr21_newt_pretrained_models\cvpr21_newt_pretrained_models\pt\inat2021_supervised_large_from_scratch.pth.tar`. Image data we used is available upon request.
7. Run script
```
python inference.py
```
```
python inference.py
```
8. Find results in `Output.txt`


## References

Van Horn G, Cole E, Beery S, Wilber K, Belongie S, Mac Aodha O, et al. (2021) Benchmarking Representation Learning for Natural World Image Collections. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 12884‑12893. http://arxiv-export-lb.library.cornell.edu/pdf/2103.16483