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Instance segmentation model for detection of the character Baby Yoda, from the Disney TV Show The Mandalorian.
This repository depends on baby-yoda-segmentation-dataset, built as a living-dataset.
Install the project dependencies with poetry
Change params, code or data as needed
Run
dvc repro auto-train
The model will be saved in models/model.pth
src/ColabNotebook.ipynb
In a DVC repository run:
dvc import https://dagshub.com/simon/baby-yoda-segmentor models/model.pth
curl -O https://dagshub.com/Simon/baby-yoda-segmentor/raw/master/models/model.pth
from PIL import Image
from torchvision.transforms import ToTensor
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
img = Image.open("image.png")
img_t = ToTensor()(img)
model.eval()
with torch.no_grad():
prediction = model([img_t.to(device)])
Fork the repository
git clone <fork-url>
dvc pull -r origin
Do your changes
Train the model
Add a local remote to push your data
dvc remote add --local fork <dagshub-remote-url.dvc>
# Additional commands to set up credentials should appear on you fork homepage
Push your code and data
dvc push -r fork
git add .
git commit -m "Changes to dataset"
git push
Open a PR
Press p or to see the previous file or, n or to see the next file
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