| # 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`. |
| |
