Datasets:

nikhilr01
/

sen-bridge

FID, LPIPS, PSNR and SSIM with the exact settings the SEN-Bridge benchmark specifies, in PyTorch and JAX, plus the code that builds the SEN-Bridge dataset (radar-to-optical Sentinel-1/2 pairs for flow-matching and bridge models).

The metrics package knows nothing about splits, loaders or models: you decide what to compare and when.

pip install "sen-bridge[torch]"     # or "sen-bridge[jax,convert]" for JAX
python -m sen_bridge.weights        # one-off: download, hash-check and convert the weights

The base install needs only numpy, scipy and Pillow. Optional dependencies are extras:

Extra	Installs	For
`torch`	torch, torchvision, safetensors	`sen_bridge.torch`
`jax`	jax, safetensors	`sen_bridge.jax`
`convert`	torch, safetensors	the one-off weight conversion (needed even with `jax`)
`eval`	datasets	`scripts/evaluate.py`, `scripts/make_fid_stats.py`
`build`	pandas, pyarrow, requests, huggingface_hub, datasets, pyyaml, numcodecs	the dataset build
`dev`	everything above plus ruff, ty, pytest, stubs	`./check.sh`

Metrics

Inputs are float images in [-1, 1], 3 channels, 128x128. sen_bridge.torch is channels-first, sen_bridge.jax channels-last. Every metric first rounds to uint8, so scores never depend on the float precision of your outputs.

from sen_bridge import torch as sb  # x, y: [B, 3, H, W]

(
    sb.psnr(x, y),
    sb.ssim(x, y),
    sb.lpips(x, y),
)  # each -> [B] (one value per image: average, bootstrap or slice them yourself)

fid = sb.FID.from_stats(
    "stats/fid_test.npz"
)  # real statistics ship with the dataset
fid.update(generated, real=False)  # batches; resized on the CPU
score = fid.compute()

from sen_bridge import jax as sb  # single image [H, W, C]; batch with jit + vmap

psnr = jax.jit(jax.vmap(sb.psnr))(x, y)  # likewise ssim, lpips
fid = sb.FID()
fid.update(images_nhwc, real=True)
...

Frozen settings (none are arguments): FID uses the pytorch-fid Inception-v3 weights and clean-fid's CPU bicubic resize to 299x299; LPIPS uses the VGG trunk with the v0.1 linear layers; SSIM uses an 11x11 Gaussian, sigma 1.5, data range 1; PSNR has data range 1.

Same weights, same numbers. Both backends load one set of safetensors files. The test suite checks: LPIPS and Inception features identical to the lpips and pytorch-fid packages, PSNR and SSIM within 1e-4 of scikit-image, and JAX versus PyTorch within 1e-4 on LPIPS and 0.01 on FID. (FID on very few images, where the covariance is singular, can differ slightly more; use thousands of images.)

Evaluating a model

python scripts/evaluate.py samples.npy --data <dataset> --split test --fid-stats stats/fid_test.npz

samples.npy holds one generated optical image per test example in dataset order. The script reports FID and per-image PSNR/SSIM/LPIPS with 95% bootstrap intervals resampled by geographic cluster. Choose checkpoints on validation only.

Licences

Code: Apache-2.0, Copyright 2026 Nikhil Raghavendra (LICENSE, NOTICE). Adapted third-party code and its licences are in NOTICE and licenses/.
Dataset: CC BY 4.0, the licence of its source, SSL4EO-S12 v1.1. Attribution and the Copernicus notice are in the dataset card and NOTICE.
Weights are not redistributed; they come from their original hosts and are SHA-256 checked. The FID and LPIPS-linear weights are Apache-2.0 and BSD-2-Clause. The VGG16 trunk (used by LPIPS) has no published weights licence upstream; docs/LICENSES.md records the position.

Development standards

One fixed standard, enforced by the tools rather than by convention:

Formatting: ruff format, 84 columns. ruff format --check must pass.
Lint: ruff check with select = ["ALL"] and preview rules. Only two rules are disabled, both because they conflict with the formatter; per-file exceptions (assert in tests, print in command-line scripts) are written down in pyproject.toml. Docstrings and comments obey the same 84-column limit.
Documentation: Google-style docstrings on every module, class, function and method, checked by the D (pydocstyle, convention = "google") and DOC (pydoclint) rules, so Args, Returns, Raises and Attributes must match the code.
Types: ty check with every rule set to error; all code is fully annotated.
All at once: ./check.sh (also available as a pre-commit config).

Layout

src/sen_bridge/ metrics only (numpy, scipy, Pillow; torch or jax by extra).
src/sen_bridge_build/ dataset build helpers; build/ the pipeline scripts; docs/ audit and licence notes.