Update README.md

README.md

@@ -12,44 +12,43 @@ M3D-CLIP is a 3D medical CLIP model, which aligns vision and language through co
 The vision encoder uses 3D ViT with 32*256*256 image size and 4*16*16 patch size.
 The text encoder utilizes a pre-trained BERT as initialization.
 
-![M3D_CLIP_table]([M3D_CLIP_table.png]#pic_center)
-![itr_result]([itr_result.png]#pic_center)
+![M3D_CLIP_table]([./M3D_CLIP_table.png]#pic_center)
+![itr_result]([./itr_result.png]#pic_center)
 
 # Quickstart
 
 ```python
-    device = torch.device("cuda") # or cpu
+device = torch.device("cuda") # or cpu
 
-    tokenizer = AutoTokenizer.from_pretrained(
-        "GoodBaiBai88/M3D-CLIP",
-        model_max_length=512,
-        padding_side="right",
-        use_fast=False
-    )
-    model = AutoModel.from_pretrained(
-        "GoodBaiBai88/M3D-CLIP",
-        trust_remote_code=True
-    )
-    model = model.to(device=device)
+tokenizer = AutoTokenizer.from_pretrained(
+    "GoodBaiBai88/M3D-CLIP",
+    model_max_length=512,
+    padding_side="right",
+    use_fast=False
+)
+model = AutoModel.from_pretrained(
+    "GoodBaiBai88/M3D-CLIP",
+    trust_remote_code=True
+)
+model = model.to(device=device)
 
-    # Prepare your 3D medical image:
-    # 1. The image shape needs to be processed as 1*32*256*256, considering resize and other methods.
-    # 2. The image needs to be normalized to 0-1, considering Min-Max Normalization.
-    # 3. The image format needs to be converted to .npy 
-    # 4. Although we did not train on 2D images, in theory, the 2D image can be interpolated to the shape of 1*32*256*256 for input.
-        
-    image_path = ""
-    input_txt = ""
+# Prepare your 3D medical image:
+# 1. The image shape needs to be processed as 1*32*256*256, considering resize and other methods.
+# 2. The image needs to be normalized to 0-1, considering Min-Max Normalization.
+# 3. The image format needs to be converted to .npy 
+# 4. Although we did not train on 2D images, in theory, the 2D image can be interpolated to the shape of 1*32*256*256 for input.
+    
+image_path = ""
+input_txt = ""
 
-    text_tensor = tokenizer(input_txt, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
-    input_id = text_tensor["input_ids"].to(device=device)
-    attention_mask = text_tensor["attention_mask"].to(device=device)
-    image = np.load(image_path).to(device=device)
-
-    with torch.inference_mode():
-        image_features = model.encode_image(image)[:, 0]
-        text_features = model.encode_text(input_id, attention_mask)[:, 0]
+text_tensor = tokenizer(input_txt, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
+input_id = text_tensor["input_ids"].to(device=device)
+attention_mask = text_tensor["attention_mask"].to(device=device)
+image = np.load(image_path).to(device=device)
 
+with torch.inference_mode():
+    image_features = model.encode_image(image)[:, 0]
+    text_features = model.encode_text(input_id, attention_mask)[:, 0]
 ```
 
 # Citation