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src/sam_backend.py

@@ -16,7 +16,7 @@ from typing import Tuple
 # even when the weights are already cached locally.
 os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", "60")
 
-SAM2_MODEL_ID = "facebook/sam2.1-hiera-large"
+SAM2_MODEL_ID = "facebook/sam2.1-hiera-small"
 
 # SAM resizes internally to 1024 — feeding >1024 wastes CPU on image encoding.
 INFERENCE_MAX_SIDE = 1024

src/sam_card_detection.py

@@ -28,8 +28,8 @@ from .card_detection import (
 )
 from .sam_backend import INFERENCE_MAX_SIDE as PROMPT_INFERENCE_MAX_SIDE, get_sam2
 
-# HF Hub model id — small, fast SAM 2.1 variant that fits CPU / HF free Spaces
-SAM2_MODEL_ID = "facebook/sam2.1-hiera-large"
+# HF Hub model id — tiny, small, base-plus, large
+SAM2_MODEL_ID = "facebook/sam2.1-hiera-small"
 
 # Downscale large images before inference to keep CPU runtime tractable.
 # SAM 2.1 internally resizes to 1024, so feeding >1024 is pure overhead.