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@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/cir_candi_2.png filter=lfs diff=lfs merge=lfs -text
+assets/cir_query.png filter=lfs diff=lfs merge=lfs -text
+assets/res-ft-mmeb.png filter=lfs diff=lfs merge=lfs -text
+assets/res-scaling.png filter=lfs diff=lfs merge=lfs -text
+assets/res-zs-cir.png filter=lfs diff=lfs merge=lfs -text
+assets/res-zs-mmeb.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -86,11 +86,43 @@ with torch.no_grad():
 print(scores)
 ```
 
+### 2. MMRet-MLLM Models
+```python
+import torch
+from transformers import AutoModel
+from PIL import Image
 
+MODEL_NAME= "JUNJIE99/MMRet-MLLM-S1"
 
+model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
+model.eval()
+model.cuda()
+
+with torch.no_grad():
+    model.set_processor(MODEL_NAME)
+
+    query_inputs = model.data_process(
+        text="Make the background dark, as if the camera has taken the photo at night", 
+        images="./assets/cir_query.png",
+        q_or_c="q",
+        task_instruction="Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: "
+    )
+
+    candidate_inputs = model.data_process(
+        images=["./assets/cir_candi_1.png", "./assets/cir_candi_2.png"],
+        q_or_c="c",
+    )
+
+    query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]
+    candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]
+    
+    query_embs = torch.nn.functional.normalize(query_embs, dim=-1)
+    candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)
+
+    scores = torch.matmul(query_embs, candi_embs.T)
+print(scores)
+```
 
-### 2. MMRet-MLLM Models
-```Will be released soon.```
 
 ## Model Performance
 ### Zero-Shot Composed Image Retrieval