Update README.md

README.md

@@ -19,33 +19,8 @@ For LLM and vision tower, we choose [OpenELM-270M-Instruct](apple/OpenELM-270M-I
 
 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
-====================================
+| Category    | # Samples | TP   | FP  | TN   | FN  | Accuracy | Precision | Recall | F1 Score | Yes Ratio |
+|-------------|------------|------|-----|------|-----|----------|-----------|--------|----------|-----------|
+| Adversarial | 3000       | 1264 | 575 | 925  | 236 | 0.7297   | 0.6873    | 0.8427 | 0.7571   | 0.613     |
+| Popular     | 3000       | 1264 | 301 | 1199 | 236 | 0.8210   | 0.8077    | 0.8427 | 0.8248   | 0.5217    |
+| Random      | 2910       | 1264 | 290 | 1120 | 236 | 0.8192   | 0.8134    | 0.8427 | 0.8278   | 0.5340    |