added softmax temperature for better uncertainty calibration

app/model.py

@@ -10,6 +10,7 @@ from transformers import AutoImageProcessor, AutoModelForImageClassification
 HF_NAME = "microsoft/swinv2-base-patch4-window16-256"
 WEIGHTS_PATH = Path(__file__).parent.parent / "model.safetensors"
 NUM_LABELS = 2
+SOFTMAX_TEMPERATURE = 1.5
 
 _device = torch.device("cpu")
 _processor = None
@@ -72,5 +73,5 @@ def predict_image(image: Image.Image) -> float:
         image = image.convert("RGB")
     inputs = processor(images=image, return_tensors="pt").to(_device)
     logits = model(**inputs).logits
-    probs = torch.softmax(logits, dim=-1)
+    probs = torch.softmax(logits / SOFTMAX_TEMPERATURE, dim=-1)
     return float(probs[0, 1].item())