Update README.md

README.md

@@ -68,12 +68,12 @@ inputs = processor.preprocess(image, return_tensors="pt")
 
 with torch.no_grad():
   outputs = model(**inputs.to(model.device, model.dtype))
-logits = torch.sigmoid(outputs.logits[0])
+logits = torch.sigmoid(outputs.logits[0]) # take the first logits
 
 # get probabilities
 results = {model.config.id2label[i]: logit.float() for i, logit in enumerate(logits)}
 results = {
-    k: v for k, v in sorted(results.items(), key=lambda item: item[1], reverse=True)
+    k: v for k, v in sorted(results.items(), key=lambda item: item[1], reverse=True) if v > 0.35 # 35% threshold
 }
 print(results)  # rating tags and character tags are also included
 #{'1girl': tensor(0.9974),