Update README.md

README.md

@@ -5,19 +5,20 @@ license: bsd-3-clause
 tags:
 - arxiv:2506.23151
 - optical-flow-estimation
+- optical-flow
 pipeline_tag: image-to-image
 library_name: pytorch
 base_model:
-- egorchistov/MEMFOF-Tartan-T-TSKH
+- egorchistov/optical-flow-MEMFOF-Tartan-T-TSKH
 ---
 
 # MEMFOF-Tartan-T-TSKH-spring
 
-<a href="https://arxiv.org/abs/2506.23151" style="text-decoration: none;">📄 Paper</a> | <a href="https://msu-video-group.github.io/memfof" style="text-decoration: none;">🌐 Project Page</a> | <a href="https://github.com/msu-video-group/memfof" style="text-decoration: none;">💻 Code</a> | <a href="https://colab.research.google.com/github/msu-video-group/memfof/blob/dev/demo.ipynb" style="text-decoration: none;">🚀 Colab</a> | <a href="https://huggingface.co/spaces/egorchistov/MEMFOF" style="text-decoration: none;">🤗 Demo</a>
+<a href="https://arxiv.org/abs/2506.23151" style="text-decoration: none;">📄 Paper</a> | <a href="https://msu-video-group.github.io/memfof" style="text-decoration: none;">🌐 Project Page</a> | <a href="https://github.com/msu-video-group/memfof" style="text-decoration: none;">💻 Code</a> | <a href="https://colab.research.google.com/github/msu-video-group/memfof/blob/dev/demo.ipynb" style="text-decoration: none;">🚀 Colab</a> | <a href="https://huggingface.co/spaces/egorchistov/optical-flow-MEMFOF" style="text-decoration: none;">🤗 Demo</a>
 
-🔍 This is a MEMFOF checkpoint trained from **MEMFOF-Tartan-T-TSKH** on the **Spring** dataset.
+🔍 **MEMFOF** is a **memory-efficient optical flow method** for **Full HD video** that combines **high accuracy** with **low VRAM usage**.
 
-✅ **Note:** This model is intended for submission to **Spring benchmark**.
+✅ **Note:** This particular checkpoint is intended for submission to **Spring benchmark**.
 
 ## 🛠️ Usage
 
@@ -32,7 +33,7 @@ import torch
 from core.memfof import MEMFOF
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model = MEMFOF.from_pretrained("egorchistov/MEMFOF-Tartan-T-TSKH-spring").eval().to(device)
+model = MEMFOF.from_pretrained("egorchistov/optical-flow-MEMFOF-Tartan-T-TSKH-spring").eval().to(device)
 
 with torch.inference_mode():
     example_input = torch.randint(0, 256, [1, 3, 3, 1080, 1920], device=device)  # [B=1, T=3, C=3, H=1080, W=1920]