fix README.md

README.md

@@ -3,13 +3,13 @@ license: mit
 ---
 
 
-# IflyBotVLM
+# iFlyBotVLM
 
 ## 🔥Introduction
 
-We introduce IflyBotVLM, a general-purpose Vision-Language-Model (VLM) specifically engineered for the domain of Embodied Intelligence. The primary objective of this model is to bridge the cross-modal semantic gap between high-dimensional environmental perception and low-level robot motion control. It achieves this by abstracting complex scene information into an "Operational Language" that is body-agnostic and transferable, thus enabling seamless perception-to-action closed-loop coordination.
+We introduce iFlyBotVLM, a general-purpose Vision-Language-Model (VLM) specifically engineered for the domain of Embodied Intelligence. The primary objective of this model is to bridge the cross-modal semantic gap between high-dimensional environmental perception and low-level robot motion control. It achieves this by abstracting complex scene information into an "Operational Language" that is body-agnostic and transferable, thus enabling seamless perception-to-action closed-loop coordination.
 
-The architecture of IflyBotVLM is designed to realize four critical functional capabilities in the embodied domain:  
+The architecture of iFlyBotVLM is designed to realize four critical functional capabilities in the embodied domain:  
 **🧭Spatial Understanding and Metric**: Provides the model with the capacity to understand spatial relationships and perform relative position estimation among objects in the environment.  
 **🎯Interactive Target Grounding**: Supports diverse grounding mechanisms, including 2D/3D object detection in the visual modality, language-based object and spatial referring, and the prediction of critical object affordance regions.  
 **🤖Action Abstraction and Control Parameter Generation**: Generates outputs directly relevant to the manipulation domain, providing grasp poses and manipulation trajectories.  
@@ -20,12 +20,12 @@ We anticipate that iFlyBotVLM will serve as an efficient and scalable foundation
 
 <div style="display: flex; gap: 1em; max-width: 100%;">
   <img 
-    src="https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/smart_donut_chart.png" 
+    src="https://huggingface.co/datasets/iFlyBot/iFlyBotVLM-Repo/resolve/main/images/smart_donut_chart.png" 
     style="flex: 1; max-width: 60%; height: auto; object-fit: contain;" 
     alt="iFlyBotVLM Traning Data"
   >
   <img 
-    src="https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/radar_performance.png" 
+    src="https://huggingface.co/datasets/iFlyBot/iFlyBotVLM-Repo/resolve/main/images/radar_performance.png" 
     style="flex: 1; max-width: 40%; height: auto; object-fit: contain;" 
     alt="iFlyBotVLM Performance"
   >
@@ -38,15 +38,15 @@ iFlyBotVLM inherits the robust, three-stage "ViT-Projector-LLM" paradigm from es
 
 The core enhancement lies in the ViT's Positional Encoding (PE) layer. Instead of relying solely on the original 448 dimension PE, we employ Bicubic Interpolation to intelligently upsample the learned positional embeddings from 448 to an enriched dimension of 896. This novel approach, termed Dimension-Expanded Position Embedding (DEPE), provides a significantly more nuanced spatial context vector for each visual token. This dimensional enrichment allows the model to capture more complex positional and relative spatial information without increasing the sequence length, thereby enhancing the model's ability to perform fine-grained visual reasoning and detailed localization tasks.
 
-![image/png](https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/architecture.png)
+![image/png](https://huggingface.co/datasets/iFlyBot/iFlyBotVLM-Repo/resolve/main/images/architecture.png)
 
 ## 📊Model Performance
 
 iFlyBotVLM demonstrates superior performance across various challenging benchmarks.
 
-![image/png](https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/benchmark_performance.png)
+![image/png](https://huggingface.co/datasets/iFlyBot/iFlyBotVLM-Repo/resolve/main/images/benchmark_performance.png)
 
-![image/png](https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/table-performances.png)
+![image/png](https://huggingface.co/datasets/iFlyBot/iFlyBotVLM-Repo/resolve/main/images/table-performances.png)
 
 iFlyBotVLM-8B achieves state-of-the-art (SOTA) or near-SOTA performance on ten spatial comprehension, spatial perception, and temporal task planning benchmarks: Where2Place, Refspatial-bench, ShareRobot-affordance, ShareRobot-trajectory, BLINK(spatial), EmbSpatial, ERQA, CVBench, SAT, EgoPlan2.
 
@@ -177,7 +177,7 @@ class IflyRoboInference:
 
 
 def test_spatial_from_blink():
-    hf_path = "IflyBot/IflyBotVLM"
+    hf_path = "iFlyBot/iFlyBotVLM"
     ifly_robo_infer = IflyRoboInference(hf_path)
     question = {
         "idx": "val_Spatial_Relation_143",
@@ -191,7 +191,7 @@ def test_spatial_from_blink():
 
 
 def test_visual_correspondence_from_blink():
-    hf_path = "IflyBot/IflyBotVLM"
+    hf_path = "iFlyBot/iFlyBotVLM"
     ifly_robo_infer = IflyRoboInference(hf_path)
     question = {
         "idx": "val_Visual_Correspondence_1",