Imagenet C2i Fid Is

imagenet-c2i-fid-is

Generative Refinement Networks for Visual Synthesis — Jian Han et al. (arXiv:2604.13030, 2026)

What this evaluates

Evaluates class-conditional image generation fidelity and diversity on ImageNet 256x256. It measures how closely the distribution of generated images matches real images and how well the model covers all classes.

Datasets

• ImageNet — total ?; splits: test (50000)

Metrics

• FID (primary) — range: other
  • Fréchet Inception Distance computed over 50K generated samples against 50K validation samples. Lower is better.
• IS — range: other
  • Inception Score computed over 50K generated samples. Higher is better.

Input / output format

Input: Integer class label (0-999)

Output: 256x256 RGB image

Scoring recipe

images = []
for class_id in range(1000):
    for _ in range(50):
        images.append(model.generate(class_id))
fid = compute_fid(images, imagenet_val_50k)
is_score = compute_is(images)

Common pitfalls

• FID and IS are evaluated on exactly 50K samples, not the full validation set.
• Inference requires grid-searching Classifier-Free Guidance (CFG) scale and interval for optimal results.

Evidence (verbatim from paper)

FID [fid] and IS [inception_score] of 50K samples are evaluated.

Citation

@misc{han2026generativerefinement,
  title={Generative Refinement Networks for Visual Synthesis},
  author={Jian Han et al.},
  year={2026},
  note={arXiv:2604.13030}
}

• arXiv: 2604.13030