Add pipeline tag and improve model card

Hi! I'm Niels from the Hugging Face community team. I've improved the model card for PFGNet by:
- Adding the `image-to-video` pipeline tag to the metadata to improve discoverability on the Hub.
- Adding links to the official GitHub repository and project page.
- Including a BibTeX citation section.
- Providing sample training and testing commands as found in the official repository.
- Organizing the content for better readability.

README.md

@@ -1,31 +1,35 @@
 ---
+datasets:
+- moving-mnist
+- taxibj
+- kth
+- human3.6m
 license: mit
+pipeline_tag: image-to-video
 tags:
 - computer-vision
 - video-prediction
 - spatiotemporal-prediction
 - pytorch
-datasets:
-- moving-mnist
-- taxibj
-- kth
-- human3.6m
 paper:
 - https://huggingface.co/papers/2602.20537
 ---
-# PFGNet
 
-PFGNet is a fully convolutional framework for efficient spatiotemporal predictive learning.
+# PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning
+
+PFGNet is a fully convolutional framework for efficient spatiotemporal predictive learning (STPL), presented at CVPR 2026. It aims to forecast future frames from past observations by dynamically modulating receptive fields through pixel-wise frequency-guided gating. 
 
-This repository provides dataset-specific trained checkpoints of PFGNet on multiple benchmarks.
+Inspired by biological center-surround organization, the core Peripheral Frequency Gating (PFG) block extracts localized spectral cues to adaptively fuse multi-scale large-kernel peripheral responses with learnable center suppression, forming spatially adaptive band-pass filters.
 
-**Code:** https://github.com/fhjdqaq/PFGNet  
-**Paper:** https://huggingface.co/papers/2602.20537  
-**Provided by:** WYK Lab  
-**Project page:** https://kaimaoge.github.io  
+**Resources:**
+- **Paper:** [PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning](https://huggingface.co/papers/2602.20537)
+- **Code:** [Official GitHub Repository](https://github.com/fhjdqaq/PFGNet)
+- **Project Page:** [kaimaoge.github.io](https://kaimaoge.github.io)
 
 ## Available checkpoints
 
+This repository provides dataset-specific trained checkpoints of PFGNet on multiple benchmarks:
+
 | Dataset | Checkpoint |
 |---|---|
 | Moving MNIST | `pfg_mmnist.ckpt` |
@@ -37,4 +41,31 @@ This repository provides dataset-specific trained checkpoints of PFGNet on multi
 
 ## Usage
 
-Please refer to the official code repository for training and inference scripts.
+PFGNet directly inherits the codebase and dependencies of [OpenSTL](https://github.com/chengtan9907/OpenSTL). Please refer to the official repository for detailed environment setup and data preparation instructions.
+
+### Training (Moving MNIST example)
+From the repository root, run:
+```bash
+python tools/train.py -d mmnist -c configs/mmnist/PFG.py --ex_name mmnist_pfg --test
+```
+
+### Testing (Moving MNIST example)
+From the repository root, run:
+```bash
+python tools/test.py -d mmnist -c configs/mmnist/PFG.py --ex_name mmnist_pfg --test
+```
+
+## Citation
+
+If you find this work helpful, please consider citing:
+
+```bibtex
+@misc{cai2026pfgnetfullyconvolutionalfrequencyguided,
+      title={PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning}, 
+      author={Xinyong Cai and Changbin Sun and Yong Wang and Hongyu Yang and Yuankai Wu},
+      year={2026},
+      eprint={2602.20537},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```