Add model card for SAM3-LiteText

README.md

@@ -1,3 +1,65 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-segmentation
+tags:
+- sam3
+- vision-language
+- segmentation
+- efficient-sam
+---
+
+# SAM3-LiteText
+
+SAM3-LiteText is a lightweight text encoding framework for vision-language segmentation, introduced in the paper [SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation](https://huggingface.co/papers/2602.12173).
+
+## Introduction
+
+Vision-language segmentation models like SAM3 enable flexible, prompt-driven visual grounding but often rely on large text encoders designed for open-ended language understanding. SAM3-LiteText addresses this by replacing the original SAM3 text encoder with a compact **MobileCLIP** student optimized via knowledge distillation. This approach reduces text encoder parameters by up to 88% and significantly lowers the memory footprint while maintaining segmentation performance comparable to the original model.
+
+## Resources
+
+- **Paper:** [SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation](https://huggingface.co/papers/2602.12173)
+- **Code:** [GitHub Repository (sam3_litetext branch)](https://github.com/SimonZeng7108/efficientsam3/tree/sam3_litetext)
+- **Project Page:** [EfficientSAM3](https://simonzeng7108.github.io/efficientsam3/)
+
+## Sample Usage
+
+The following example demonstrates how to perform inference with a text prompt using an EfficientSAM3 model variant with a distilled MobileCLIP text encoder:
+
+```python
+from sam3.model_builder import build_efficientsam3_image_model
+from sam3.model.sam3_image_processor import Sam3Processor
+
+# Load model with distilled text encoder
+model = build_efficientsam3_image_model(
+    checkpoint_path="efficient_sam3_tinyvit_m_mobileclip_s1.pt",
+    backbone_type="tinyvit",
+    model_name="11m",
+    text_encoder_type="MobileCLIP-S1"
+)
+
+# Process image and predict with text prompt
+processor = Sam3Processor(model)
+inference_state = processor.set_image(image)
+inference_state = processor.set_text_prompt(prompt="shoe", state=inference_state)
+
+masks = inference_state["masks"]
+scores = inference_state["scores"]
+print(f"Found {len(scores)} masks. Scores: {scores}")
+```
+
+## Citation
+
+If you use SAM3-LiteText or the EfficientSAM3 framework in your research, please cite:
+
+```bibtex
+@misc{zeng2025efficientsam3progressivehierarchicaldistillation,
+  title={EfficientSAM3: Progressive Hierarchical Distillation for Video Concept Segmentation from SAM1, 2, and 3}, 
+  author={Chengxi Zeng and Yuxuan Jiang and Gao Ge and Shuai Wang and Fan Aaron Zhang},
+  year={2025},
+  eprint={2511.15833},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2511.15833}, 
+}
+```