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@@ -14,7 +14,8 @@ have demonstrated strong zero-shot recognition ability in various vision tasks,
 In this paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIP for accurate ZSAD across different domains. The key insight of AnomalyCLIP is to learn object-agnostic text prompts that capture generic normality and abnormality in an image regardless of its foreground objects. This allows our model to focus on the abnormal image regions rather than the object semantics, enabling generalized normality and abnormality recognition on diverse types of objects. Large-scale experiments on 17 real-world anomaly detection datasets show that AnomalyCLIP achieves superior zero-shot performance of detecting and segmenting anomalies in datasets of highly diverse class semantics from various defect inspection and medical imaging domains. All experiments are conducted in PyTorch-2.0.0 with a single NVIDIA RTX 3090 24GB. 
 
 ## Overview of AnomalyCLIP
-![Overview of AnomalyCLIP](./assets/overview.png)
+![overview](https://github.com/zqhang/AnomalyCLIP/assets/19222962/4ec3e5fc-9570-41f7-8067-6e7a515841be)
+
 
 ## Analysis of different text prompt templates
 ![analysis](./assets/analysis.png)