Third-party notices
YellowCab uses third-party runtime software and pretrained initialization. Those components remain subject to their respective licenses.
Runtime dependencies
| Component | Version | License | Project |
|---|---|---|---|
| NumPy | 2.2.6 |
BSD 3-Clause | https://github.com/numpy/numpy |
| Pillow | 11.0.0 |
HPND | https://github.com/python-pillow/Pillow |
| safetensors | 0.5.3 |
Apache License 2.0 | https://github.com/huggingface/safetensors |
| PyTorch | 2.7.1 |
BSD 3-Clause | https://github.com/pytorch/pytorch |
| torchvision | 0.22.1 |
BSD 3-Clause | https://github.com/pytorch/vision |
EfficientNet-B0 initialization
The image encoder was initialized from torchvision's
EfficientNet_B0_Weights.IMAGENET1K_V1 weights. Torchvision documents these
weights as ported from Ross Wightman's EfficientNet implementation and trained
on ImageNet-1K:
- https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b0.html
- https://github.com/rwightman/gen-efficientnet-pytorch
The repository distributes the resulting encoder parameters as part of the YellowCab checkpoint. It does not distribute ImageNet images or taxi-training images.
The torchvision BSD 3-Clause license is reproduced in
THIRD_PARTY_LICENSES/torchvision-BSD-3-Clause.txt. Ross Wightman's
EfficientNet implementation is licensed under Apache License 2.0; the license
text is reproduced in the top-level LICENSE.