GCNet-L (Cityscapes mirror)
This is a redistribution (a "mirror"), not a new model. The weights are the official GCNet-L Cityscapes checkpoint from gyyang23/GCNet, re-hosted here for convenient, stable, programmatic access via
huggingface_hub. All credit to the original authors.
What this is
| Architecture | Golden Cudgel Network (CVPR 2025) |
| Variant | L |
| Params / FLOPs | ~45.2 M / 232.0 GFLOPs |
| Pretraining | Cityscapes semantic segmentation, 19 classes (full fine-tuned checkpoint) |
| Reported metric | 79.6 mIoU (single-scale) on Cityscapes val |
| Upstream repository | https://github.com/gyyang23/GCNet |
| Upstream weights | https://drive.google.com/file/d/1FCQJB0kRni7PoMTkLOjzuPi9KI868XB6/view |
| Upstream license | MIT; mirrored as LICENSE in this repo |
What was changed vs the upstream file
Nothing in the weights. The upstream .pth is an mmengine training checkpoint (weights + optimizer state + logging buffers). Only what is needed to load the model is kept:
| Component | Upstream | This mirror |
|---|---|---|
state_dict (model weights, fp32) | yes | yes, unchanged at top level |
meta (mmengine env / config / dataset_meta) | yes | yes, kept for provenance |
optimizer (SGD momentum buffers) | yes | removed |
message_hub, param_schedulers | yes | removed |
| File size | 866 MB | ~426 MB |
The tensors are byte-identical to the upstream file. checksums.txt in this repo carries the
SHA-256 of the mirrored file and of the upstream original.
Usage
These weights use the mmsegmentation module layout. The GCNet backbone/head are
registered by the vendored mmseg inside the upstream repo (stock pip install mmsegmentation does not have them), so install the repo's copy:
git clone https://github.com/gyyang23/GCNet
cd GCNet/mmsegmentation
pip install -v -e . # registers GCNet / GCNetHead with mmseg
pip install huggingface_hub
cd .. # run from the repo root
Upstream environment: python==3.8, pytorch==1.12.1, mmcv==2.0.0, mmengine==0.10.2.
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download
from mmseg.apis import init_model, inference_model
repo = "dronefreak/gcnet-l-cityscapes"
config = hf_hub_download(repo, "gcnet-l_4xb3-120k_cityscapes-1024x1024.py") # mirrored from the upstream repo
weights = hf_hub_download(repo, "gcnet-l_cityscapes.pth")
model = init_model(config, weights, device="cuda:0") # use "cpu" if no GPU
# --- inference on a single image ---
result = inference_model(model, "street.jpg") # any RGB road-scene image
seg = result.pred_sem_seg.data[0].cpu().numpy().astype(np.uint8) # (H, W), trainId 0..18
palette = np.array(model.dataset_meta["palette"], dtype=np.uint8) # Cityscapes 19-class colours
Image.fromarray(palette[seg]).save("street_pred.png")
print(model.dataset_meta["classes"])
The config carries training/val dataloaders pointing at ./data/cityscapes/; those are
unused for single-image inference. Class names and the colour palette are read from the
checkpoint's retained meta["dataset_meta"].
Citing the original work
% Golden Cudgel Network, CVPR 2025 (arXiv:2503.03325)
@misc{yang2025goldencudgel,
title = {Golden Cudgel Network for Real-Time Semantic Segmentation},
author = {Guoyu Yang and Yuan Wang and Daming Shi and Yanzhong Wang},
year = {2025},
eprint = {2503.03325},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2503.03325}
}
Provenance & license
- Model weights © the original GCNet authors, released under MIT.
- Trained on Cityscapes, which carries its own terms (academic / research use); the derived weights are redistributed on that same basis.
- Mirrored by @dronefreak for convenient access. No affiliation with the original authors.