Upload metadata.json with huggingface_hub

metadata.json

@@ -0,0 +1,40 @@
+{
+  "model": "DEIMv2-X",
+  "framework": "ONNX Runtime",
+  "description": "2-class pill + needle detector for Android, 100% exact match on 87 val images (fp32), 15/16 test images match (fp16)",
+  "classes": {"0": "pill", "1": "needle"},
+  "input": {
+    "name": "image",
+    "shape": [1, 3, 640, 640],
+    "dtype": "float16",
+    "layout": "NCHW",
+    "normalization": {
+      "mean": [0.485, 0.456, 0.406],
+      "std": [0.229, 0.224, 0.225],
+      "note": "ImageNet stats. Formula: (pixel / 255.0 - mean) / std"
+    }
+  },
+  "outputs": {
+    "labels": {"shape": [1, 300], "dtype": "int32", "description": "0=pill, 1=needle"},
+    "boxes": {"shape": [1, 300, 4], "dtype": "float16", "description": "xyxy pixel coords (0-640)"},
+    "scores": {"shape": [1, 300], "dtype": "float16", "description": "confidence 0-1"}
+  },
+  "postprocessing": {
+    "confidence_threshold": 0.5,
+    "nms": false,
+    "note": "DEIMv2 is end-to-end, no NMS needed. Always outputs 300 candidates, filter by score."
+  },
+  "files": {
+    "fp16": {"name": "deimv2_x_pill_fp16.onnx", "size_mb": 101},
+    "fp32": {"name": "deimv2_x_pill.onnx + .data", "size_mb": 199}
+  },
+  "performance": {
+    "val_images": 87,
+    "exact_match_fp32": "100%",
+    "exact_match_fp16": "99% (15/16 test images)",
+    "within_2": "100%",
+    "gpu_inference_ms": 24,
+    "parameters": "50.3M"
+  },
+  "gradle_dependency": "com.microsoft.onnxruntime:onnxruntime-android:1.21.0"
+}