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chute_config.yml

@@ -2,8 +2,8 @@ Image:
   from_base: parachutes/python:3.12
   run_command:
     - pip install --upgrade setuptools wheel
-    - pip install 'numpy>=1.23' 'onnxruntime-gpu>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
-    - pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu[cuda,cudnn]>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
+    - pip install torch torchvision
 
 NodeSelector:
   gpu_count: 1

miner.py

@@ -37,7 +37,7 @@ class Miner:
         #   balaclava=0, hoodie=1, glove=2, bat=3, spray paint=4, graffiti=5
         self.class_names = ['balaclava', 'hoodie', 'glove', 'bat', 'spray paint', 'graffiti']
         # ONNX class index order from training export (Ultralytics names 0..5 in dataset.yaml).
-        model_class_order = ['balaclava', 'bat', 'glove', 'graffiti', 'hoodie', 'spray paint']
+        model_class_order = ['balaclava', 'hoodie', 'glove', 'bat', 'spray paint', 'graffiti']
         self._train_cls_to_canonical = np.array(
             [self.class_names.index(n) for n in model_class_order],
             dtype=np.int32
@@ -91,10 +91,10 @@ class Miner:
         # (glove, spray paint), long handhelds (bat) and large markings
         # (graffiti). Confidence peaks vary widely so we keep a moderate
         # floor and lean on TTA consensus for the soft tail.
-        self.conf_thres = 0.15
+        self.conf_thres = 0.25
 
         # Above this on orig view, accept directly. Below it, require TTA agreement.
-        self.conf_high = 0.32
+        self.conf_high = 0.35
 
         # Standard NMS IoU; balaclavas / hoodies on the same person can
         # overlap heavily but they're different classes so the per-class
@@ -536,6 +536,7 @@ class Miner:
         - any box with conf >= conf_high
         - low/medium-conf boxes only if confirmed across TTA views
         Then run final hard NMS.
+
         All thresholds default to the instance attributes when not supplied,
         so the non-sweep path can call this without args. The sweep path
         passes explicit values to avoid mutating shared state across

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:71009e2a8381b151383d0f6d1c87b2d0af7911664a267a30e3e7160143a83f94
-size 19157269
+oid sha256:97d6a4c1a6ec630ce8cf98d507b4838a3616592fa431e55533aaea19876c64b9
+size 19409709