Update README.md

README.md

@@ -7,8 +7,45 @@ metrics:
 pipeline_tag: image-text-to-text
 ---
 
+# Introduction
+
 We use the powerful [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) to create a super small image-text-to-text model with only 296M params.
 
 The goal is to make it possible to run LLaVA models on edge devices (with few gigabytes of memory).
 
-For LLM and vision tower, we choose [OpenELM-270M-Instruct](apple/OpenELM-270M-Instruct) and [facebook/dinov2-small](facebook/dinov2-small), respectively.
+For LLM and vision tower, we choose [OpenELM-270M-Instruct](apple/OpenELM-270M-Instruct) and [facebook/dinov2-small](facebook/dinov2-small), respectively.
+
+# Result
+
+For now we measured only [POPE](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#pope) with these results
+
+Category: adversarial, # samples: 3000
+TP      FP      TN      FN
+1264    575     925     236
+Accuracy: 0.7296666666666667
+Precision: 0.6873300706905927
+Recall: 0.8426666666666667
+F1 score: 0.7571129080563043
+Yes ratio: 0.613
+0.757, 0.730, 0.687, 0.843, 0.613
+====================================
+Category: popular, # samples: 3000
+TP      FP      TN      FN
+1264    301     1199    236
+Accuracy: 0.821
+Precision: 0.807667731629393
+Recall: 0.8426666666666667
+F1 score: 0.8247960848287113
+Yes ratio: 0.5216666666666666
+0.825, 0.821, 0.808, 0.843, 0.522
+====================================
+Category: random, # samples: 2910
+TP      FP      TN      FN
+1264    290     1120    236
+Accuracy: 0.8192439862542955
+Precision: 0.8133848133848134
+Recall: 0.8426666666666667
+F1 score: 0.8277668631303209
+Yes ratio: 0.534020618556701
+0.828, 0.819, 0.813, 0.843, 0.534
+====================================