evaluate boxes doc

weecology · Jan 10, 2025 · 361ad97 · 361ad97
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diff --git a/docs/examples/baseline_boxes.ipynb b/docs/examples/baseline_boxes.ipynb
diff --git a/docs/examples/baseline_points.ipynb b/docs/examples/baseline_points.ipynb
@@ -279,11 +279,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "import warnings\n",
+    "from deepforest.utilities import read_file\n",
     "\n",
     "def format_deepforest_prediction(images, metadata, targets, m, batch_index, point_dataset):\n",
     "    # Suppress user warnings to make more readable\n",
@@ -293,7 +294,8 @@
     "    predictions = m.predict_step(images, batch_index)\n",
     "    for image_metadata, pred, image_targets, image in zip(metadata,predictions, targets, images):\n",
     "        basename = point_dataset._filename_id_to_code[int(image_metadata[0])]\n",
-    "        pred[\"geometry\"] = pred.centroid\n",
+    "        pred = read_file(pred)\n",
+    "        pred[\"geometry\"] = pred[\"geometry\"].centroid\n",
     "        pred[[\"x\",\"y\"]] = pred[\"geometry\"].apply(lambda x: pd.Series([x.x, x.y]))\n",
     "        if pred is None:\n",
     "            y_pred = {}\n",
@@ -327,14 +329,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 66 batches in the test loader\n"
+     "ename": "NameError",
+     "evalue": "name 'get_eval_loader' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Get the test loader\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m \u001b[43mget_eval_loader\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m\"\u001b[39m, point_test_data, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Print the length of the test loader\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere are \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m batches in the test loader\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(test_loader)))\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'get_eval_loader' is not defined"
      ]
     }
    ],

diff --git a/milliontrees/common/metrics/all_metrics.py b/milliontrees/common/metrics/all_metrics.py
@@ -444,7 +444,7 @@ def _accuracy(self, src_boxes, pred_boxes,  iou_threshold):
             )
             false_negative = total_gt - true_positive
             acc = true_positive / ( true_positive + false_positive + false_negative )
-            return true_positive / ( true_positive + false_positive + false_negative )
+            return acc
         elif total_gt == 0:
             if total_pred > 0:
                 return torch.tensor(0.)
@@ -510,8 +510,7 @@ def _accuracy(self, src_keypoints, pred_keypoints, distance_threshold):
             false_negative = total_gt - true_positive
             acc = true_positive / (true_positive + false_positive +
                                    false_negative)
-            return true_positive / (true_positive + false_positive +
-                                    false_negative)
+            return acc
         elif total_gt == 0:
             if total_pred > 0:
                 return torch.tensor(0.)