fix: probe image column candidates when multiple exist

When multiple image columns are found, probes them (HEAD for URLs,
os.path.exists for paths) and picks the first that works.
Skips probing when top candidate is PIL/dict (score >= 75).

studio/backend/utils/datasets/format_detection.py

@@ -672,7 +672,7 @@ def detect_vlm_dataset_structure(dataset):
             return 75
 
         if isinstance(sample_value, str):
-            # URL strings are directly resolvable — prefer over bare filenames
+            # URL strings
             if sample_value.startswith(("http://", "https://")):
                 return 70 if not is_metadata_column(col) else 55
             # Bare file path
@@ -682,8 +682,31 @@ def detect_vlm_dataset_structure(dataset):
 
         return 0
 
+    def _probe_image_candidate(col, sample_value):
+        """Quick probe to check if an image candidate is actually reachable.
+        Returns True if likely valid, False if definitely broken."""
+        import os
+
+        # PIL / dict — already loaded, always valid
+        if not isinstance(sample_value, str):
+            return True
+
+        # Local file — check it exists
+        if not sample_value.startswith(("http://", "https://")):
+            return os.path.exists(sample_value)  # bare filenames return False here, that's OK
+
+        # URL — quick HEAD request with short timeout
+        try:
+            import urllib.request
+            req = urllib.request.Request(sample_value, method="HEAD")
+            resp = urllib.request.urlopen(req, timeout=3)
+            return resp.status < 400
+        except Exception:
+            return False
+
     def find_image_column():
-        """Find image column by keyword match + value-based fallback."""
+        """Find image column by keyword match + value-based fallback.
+        When multiple candidates exist, probes them to find one that works."""
         candidates = []
 
         # Pass 1: keyword-matched columns
@@ -706,12 +729,24 @@ def detect_vlm_dataset_structure(dataset):
                 # Slightly penalise non-keyword columns so keyword matches win on ties
                 candidates.append((col, max(score - 5, 1)))
 
-        # Return highest priority candidate
-        if candidates:
-            candidates.sort(key=lambda x: x[1], reverse=True)
+        if not candidates:
+            return None
+
+        candidates.sort(key=lambda x: x[1], reverse=True)
+
+        # Single candidate or top candidate is PIL/dict — no probing needed
+        if len(candidates) == 1 or candidates[0][1] >= 75:
             return candidates[0][0]
 
-        return None
+        # Multiple string-based candidates — probe to find one that actually works
+        for col, score in candidates:
+            sample_value = sample[col]
+            if _probe_image_candidate(col, sample_value):
+                return col
+
+        # Nothing probed successfully — return highest-scored anyway and let
+        # conversion handle the error (it may still resolve via hf_hub_download)
+        return candidates[0][0]
 
     def find_text_column():
         """Find text column by filtering out metadata and checking keywords."""