Update zero-shot comparison table

README.md

@@ -32,14 +32,16 @@ We evaluate CropVLM against CLIP-based baselines by encoding each crop class nam
 
 | Model | Overall Accuracy (%) | Per-Class Mean +/- Std (%) |
 |---|---:|---:|
-| SigLIP 2 | 3.43 | 3.43 +/- 16.91 |
-| AgriCLIP | 4.04 | 4.04 +/- 14.61 |
+| AgriCLIP | 3.64 | 3.64 +/- 12.69 |
 | RemoteCLIP | 42.52 | 42.52 +/- 27.57 |
 | BioCLIP | 48.33 | 48.34 +/- 34.95 |
 | BioTrove-CLIP | 51.07 | 51.07 +/- 36.20 |
 | BioCLIP 2 | 67.74 | 67.74 +/- 31.17 |
 | OpenAI CLIP ViT-B/32 | 70.24 | 70.24 +/- 28.83 |
 | **CropVLM** | **72.51** | **72.51 +/- 29.71** |
+| SigLIP 2 | 92.51 | 92.51 +/- 16.61 |
+
+SigLIP 2 is reported with fixed 64-token text padding, following the official SigLIP2 preprocessing requirement. AgriCLIP is reported using its official `Agri_Dino_aligner_DPT_CPT.pth` aligned checkpoint; because AgriCLIP is a DINO-to-CLIP alignment pipeline rather than a standard CLIP-style encoder, this result should be interpreted cautiously.
 
 ## Installation
 

scripts/evaluate_zero_shot.py

@@ -163,7 +163,13 @@ class Siglip2Adapter(Adapter):
         self.model = AutoModel.from_pretrained(self.name).to(device).eval()
 
     def encode_text(self, prompts: Sequence[str]) -> torch.Tensor:
-        inputs = self.processor(text=list(prompts), padding=True, return_tensors="pt").to(self.device)
+        inputs = self.processor(
+            text=[prompt.lower() for prompt in prompts],
+            padding="max_length",
+            max_length=64,
+            truncation=True,
+            return_tensors="pt",
+        ).to(self.device)
         with torch.no_grad():
             if hasattr(self.model, "get_text_features"):
                 features = self.model.get_text_features(**inputs)