diff --git a/notebooks/700_metrics/701a_aupimo.ipynb b/notebooks/700_metrics/701a_aupimo.ipynb
index d780c5a964..da6bcefd47 100644
--- a/notebooks/700_metrics/701a_aupimo.ipynb
+++ b/notebooks/700_metrics/701a_aupimo.ipynb
@@ -225,7 +225,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "880e325e4e4842b2b679340ca8007849",
+       "model_id": "a6bf2640a4394d6a889eff93035ddfb3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -244,7 +244,7 @@
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
        "│<span style=\"color: #008080; text-decoration-color: #008080\">        image_AUROC        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9887908697128296     </span>│\n",
        "│<span style=\"color: #008080; text-decoration-color: #008080\">       image_F1Score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9726775884628296     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       pixel_AUPIMO        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7428419829089654     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">       pixel_AUPIMO        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7411147070039484     </span>│\n",
        "└───────────────────────────┴───────────────────────────┘\n",
        "</pre>\n"
       ],
@@ -254,7 +254,7 @@
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
        "│\u001b[36m \u001b[0m\u001b[36m       image_AUROC       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9887908697128296    \u001b[0m\u001b[35m \u001b[0m│\n",
        "│\u001b[36m \u001b[0m\u001b[36m      image_F1Score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9726775884628296    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      pixel_AUPIMO       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7428419829089654    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36m      pixel_AUPIMO       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7411147070039484    \u001b[0m\u001b[35m \u001b[0m│\n",
        "└───────────────────────────┴───────────────────────────┘\n"
       ]
      },
@@ -264,7 +264,7 @@
     {
      "data": {
       "text/plain": [
-       "[{'pixel_AUPIMO': 0.7428419829089654,\n",
+       "[{'pixel_AUPIMO': 0.7411147070039484,\n",
        "  'image_AUROC': 0.9887908697128296,\n",
        "  'image_F1Score': 0.9726775884628296}]"
       ]
@@ -314,7 +314,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8116b80da39406e966c2099ecb2fdb1",
+       "model_id": "11de350d36264bbd84a0a1de3f67e573",
        "version_major": 2,
        "version_minor": 0
       },
@@ -334,12 +334,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compute the AUPIMO scores."
+    "Compute the AUPIMO scores.\n",
+    "\n",
+    "This time, we'll compute AUPIMO in high resolution (1024x1024) and it will still be fast enough! (10s of seconds) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -348,6 +350,13 @@
      "text": [
       "Metric `AUPIMO` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n"
      ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "anomaly_maps.shape=torch.Size([28, 1, 1024, 1024]) masks.shape=torch.Size([28, 1, 1024, 1024])\n"
+     ]
     }
    ],
    "source": [
@@ -357,14 +366,26 @@
     ")\n",
     "\n",
     "for batch in predictions:\n",
-    "    anomaly_maps = batch[\"anomaly_maps\"].squeeze(dim=1)\n",
+    "    anomaly_maps = batch[\"anomaly_maps\"]\n",
     "    masks = batch[\"mask\"]\n",
-    "    aupimo.update(anomaly_maps=anomaly_maps, masks=masks)"
+    "    # upsample them to the original size\n",
+    "    anomaly_maps = torch.nn.functional.interpolate(\n",
+    "        anomaly_maps,\n",
+    "        size=(1024, 1024),\n",
+    "        mode=\"bilinear\",\n",
+    "        align_corners=False,\n",
+    "    )\n",
+    "    # we should use the actual mask instead of re-sampling up the mask\n",
+    "    # but let's keep it simple here\n",
+    "    masks = torch.nn.functional.interpolate(masks.unsqueeze(1).float(), size=(1024, 1024), mode=\"nearest\").bool()\n",
+    "    aupimo.update(anomaly_maps=anomaly_maps.squeeze(dim=1), masks=masks.squeeze(dim=1))\n",
+    "\n",
+    "print(f\"{anomaly_maps.shape=} {masks.shape=}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,27 +404,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([1.0000, 0.9144, 0.4944, 0.2837, 0.2784, 0.8687, 1.0000, 0.7463, 0.2899,\n",
-      "        0.8998, 1.0000, 0.9147, 0.6389, 0.9422, 0.9582, 0.9396, 0.9890, 0.5130,\n",
-      "        0.9698, 0.9237, 0.5732, 0.4620, 0.9995, 0.9078, 0.5873, 1.0000, 1.0000,\n",
-      "        1.0000, 0.3785, 0.6764, 0.4217, 0.9299, 0.7756, 0.4339, 0.8334, 0.9297,\n",
-      "        0.9992, 0.5584, 0.9937, 0.7811, 0.4986, 0.7630, 0.5361, 0.7157, 0.1689,\n",
-      "        0.3086, 0.3604, 0.2423, 0.2880, 0.6404, 0.5570, 0.3274, 0.7749, 0.6740,\n",
-      "        0.5516, 1.0000, 0.2399, 0.9721, 0.5346, 0.4709, 1.0000, 0.9732, 0.8470,\n",
-      "        0.8863, 0.0596, 0.0000, 0.5244, 0.0000, 1.0000, 1.0000, 1.0000, 0.0088,\n",
-      "        0.9706, 1.0000,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+      "tensor([1.0000, 0.9158, 0.4951, 0.2864, 0.2811, 0.8688, 1.0000, 0.7496, 0.2933,\n",
+      "        0.9000, 1.0000, 0.9158, 0.6413, 0.9426, 0.9583, 0.9401, 0.9892, 0.5150,\n",
+      "        0.9700, 0.9242, 0.5736, 0.4619, 0.9998, 0.9083, 0.5895, 1.0000, 1.0000,\n",
+      "        1.0000, 0.3825, 0.6814, 0.4216, 0.9302, 0.7765, 0.4362, 0.8334, 0.9303,\n",
+      "        0.9995, 0.5596, 0.9943, 0.7826, 0.5009, 0.7653, 0.5379, 0.7182, 0.1707,\n",
+      "        0.3103, 0.3635, 0.2446, 0.2901, 0.6445, 0.5604, 0.3292, 0.7774, 0.6764,\n",
+      "        0.5537, 1.0000, 0.2422, 0.9735, 0.5396, 0.4698, 1.0000, 0.9742, 0.8480,\n",
+      "        0.8874, 0.0605, 0.0000, 0.5263, 0.0000, 1.0000, 1.0000, 1.0000, 0.0094,\n",
+      "        0.9714, 1.0000,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-      "           nan,    nan,    nan,    nan,    nan,    nan,    nan, 0.9895, 0.8531,\n",
-      "        0.9985, 0.9470, 1.0000, 1.0000, 0.9918, 0.9792, 1.0000, 1.0000, 0.8824,\n",
-      "        1.0000, 0.9996, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
+      "           nan,    nan,    nan,    nan,    nan,    nan,    nan, 0.9903, 0.8545,\n",
+      "        0.9977, 0.9478, 1.0000, 1.0000, 0.9924, 0.9801, 1.0000, 1.0000, 0.8829,\n",
+      "        1.0000, 0.9998, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
       "       dtype=torch.float64)\n"
      ]
     }
@@ -425,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -433,9 +454,9 @@
      "output_type": "stream",
      "text": [
       "MEAN\n",
-      "aupimo_result.aupimos[~isnan].mean().item()=0.7428419829089654\n",
+      "aupimo_result.aupimos[~isnan].mean().item()=0.7439020364956669\n",
       "OTHER STATISTICS\n",
-      "DescribeResult(nobs=92, minmax=(0.0, 1.0), mean=0.7428419829089654, variance=0.08757789538421837, skewness=-0.9285672286850366, kurtosis=-0.3299234749959594)\n"
+      "DescribeResult(nobs=92, minmax=(0.0, 1.0), mean=0.7439020364956669, variance=0.0872207646626084, skewness=-0.9345660147682576, kurtosis=-0.3133529142238407)\n"
      ]
     }
    ],
@@ -458,17 +479,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -525,7 +546,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   },
   "orig_nbformat": 4
  },
diff --git a/notebooks/700_metrics/701b_aupimo_advanced_i.ipynb b/notebooks/700_metrics/701b_aupimo_advanced_i.ipynb
index ea322102f8..83c50471b1 100644
--- a/notebooks/700_metrics/701b_aupimo_advanced_i.ipynb
+++ b/notebooks/700_metrics/701b_aupimo_advanced_i.ipynb
@@ -254,9 +254,9 @@
      "output_type": "stream",
      "text": [
       "MEAN\n",
-      "aupimo_result.aupimos[labels == 1].mean().item()=0.742841961578308\n",
+      "aupimo_result.aupimos[labels == 1].mean().item()=0.7428374946357311\n",
       "OTHER STATISTICS\n",
-      "DescribeResult(nobs=92, minmax=(0.0, 1.0), mean=0.742841961578308, variance=0.08757792704451817, skewness=-0.9285678601866055, kurtosis=-0.3299211772047075)\n"
+      "DescribeResult(nobs=92, minmax=(0.0, 1.0), mean=0.7428374946357313, variance=0.08757776807097678, skewness=-0.9284572154639179, kurtosis=-0.3300816832805764)\n"
      ]
     },
     {
@@ -396,7 +396,7 @@
       "  statistic  value  image_index\n",
       "0    whislo   0.00           65\n",
       "1        q1   0.53           58\n",
-      "2       med   0.89           63\n",
+      "2       med   0.89            9\n",
       "3        q3   1.00           22\n",
       "4    whishi   1.00            0\n"
      ]
@@ -660,7 +660,7 @@
       "Lower bound: 0.00001\n",
       "Upper bound: 0.00010\n",
       "Thresholds corresponding to the FPR bounds\n",
-      "Lower threshold: 0.504\n",
+      "Lower threshold: 0.505\n",
       "Upper threshold: 0.553\n"
      ]
     }
@@ -1002,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1013,7 +1013,7 @@
       "0    whislo   0.00     0.00     65      1\n",
       "1        q1   0.53     0.53     58      1\n",
       "2      mean   0.74     0.75      7      1\n",
-      "3       med   0.89     0.89     63      1\n",
+      "3       med   0.89     0.90      9      1\n",
       "4        q3   1.00     1.00     22      1\n",
       "5    whishi   1.00     1.00      0      1\n"
      ]
@@ -1035,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1070,7 +1070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1101,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1111,7 +1111,7 @@
        "<Figure size 700x200 with 1 Axes>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1201,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1249,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1318,7 +1318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1328,7 +1328,7 @@
       "  statistic  value  nearest  index  label\n",
       "0    whislo   0.42     0.42     90      0\n",
       "1        q1   0.43     0.43     80      0\n",
-      "2       med   0.45     0.45    105      0\n",
+      "2       med   0.45     0.46     79      0\n",
       "3      mean   0.46     0.46     89      0\n",
       "4        q3   0.48     0.48     75      0\n",
       "5    whishi   0.52     0.52     95      0\n"
@@ -1344,7 +1344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1354,7 +1354,7 @@
       "  statistic  value  nearest  index  label\n",
       "0    whislo   0.42     0.42     90      0\n",
       "1        q1   0.52     0.52     95      0\n",
-      "2       med   0.65     0.65     17      1\n",
+      "2       med   0.65     0.65     62      1\n",
       "3      mean   0.66     0.66     45      1\n",
       "4        q3   0.77     0.77    108      1\n",
       "5    whishi   1.00     1.00     22      1\n"
@@ -1406,7 +1406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   },
   "orig_nbformat": 4
  },
diff --git a/notebooks/700_metrics/701c_aupimo_advanced_ii.ipynb b/notebooks/700_metrics/701c_aupimo_advanced_ii.ipynb
index 6911b9c546..a76c2f0e71 100644
--- a/notebooks/700_metrics/701c_aupimo_advanced_ii.ipynb
+++ b/notebooks/700_metrics/701c_aupimo_advanced_ii.ipynb
@@ -118,7 +118,7 @@
     "from anomalib.data import MVTec\n",
     "from anomalib.data.utils import read_image\n",
     "from anomalib.engine import Engine\n",
-    "from anomalib.metrics import AUPIMO\n",
+    "from anomalib.metrics import AUPIMO, PIMO\n",
     "from anomalib.models import Padim"
    ]
   },
@@ -248,9 +248,9 @@
      "output_type": "stream",
      "text": [
       "MEAN\n",
-      "aupimo_result.aupimos[labels == 1].mean().item()=0.742841961578308\n",
+      "aupimo_result.aupimos[labels == 1].mean().item()=0.7428374946357311\n",
       "OTHER STATISTICS\n",
-      "DescribeResult(nobs=92, minmax=(0.0, 1.0), mean=0.742841961578308, variance=0.08757792704451818, skewness=-0.9285678601866053, kurtosis=-0.3299211772047079)\n"
+      "DescribeResult(nobs=92, minmax=(0.0, 1.0), mean=0.7428374946357313, variance=0.08757776807097678, skewness=-0.9284572154639179, kurtosis=-0.3300816832805764)\n"
      ]
     },
     {
@@ -359,7 +359,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 6 Axes>"
       ]
@@ -594,6 +594,11 @@
     "\n",
     "fig, axes = plt.subplots(1, 3, figsize=(14, 5.2), layout=\"constrained\")\n",
     "\n",
+    "# recompute the PIMO curves with larger fpr bounds and more thresholds\n",
+    "pimo = PIMO(fpr_bounds=(1e-4, 1e-2), num_thresholds=3000)\n",
+    "pimo.update(anomaly_maps=anomaly_maps, masks=masks)\n",
+    "pimo_result = pimo.compute()\n",
+    "\n",
     "# function `threshold_from_fpr()` is replaced by an equivalent function\n",
     "# for FPRn is already implemented in `pimo_result.thresh_at`\n",
     "thresholds = [pimo_result.thresh_at(fpr_level)[1] for fpr_level in FRP_levels]\n",
@@ -713,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -722,8 +727,8 @@
      "text": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mAUPIMO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mnum_thresholds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m300000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpr_bounds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1e-05\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mnum_thresholds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mreturn_average\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mforce\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -750,8 +755,8 @@
       "    masks: binary (bool or int) ground truth masks of shape (N, H, W)\n",
       "\n",
       "Args:\n",
-      "    num_thresholds: number of thresholds to compute (K)\n",
       "    fpr_bounds: lower and upper bounds of the FPR integration range\n",
+      "    num_thresholds: number of thresholds used to compute the PIMO curve and AUPIMO scores (K)\n",
       "    force: whether to force the computation despite bad conditions\n",
       "\n",
       "Returns:\n",
@@ -760,8 +765,8 @@
       "Area Under the Per-Image Overlap (PIMO) curve.\n",
       "\n",
       "Args:\n",
-      "    num_thresholds: [passed to parent `PIMO`] number of thresholds used to compute the PIMO curve\n",
       "    fpr_bounds: lower and upper bounds of the FPR integration range\n",
+      "    num_thresholds: number of thresholds used to compute the PIMO curve and AUPIMO scores (K)\n",
       "    return_average: if True, return the average AUPIMO score; if False, return all the individual AUPIMO scores\n",
       "    force: if True, force the computation of the AUPIMO scores even in bad conditions (e.g. few points)\n",
       "\u001b[0;31mFile:\u001b[0m           ~/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages/anomalib/metrics/pimo/pimo.py\n",
@@ -785,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -812,21 +817,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 6 Axes>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axes = plt.subplots(2, 3, figsize=(10, 5), layout=\"tight\")\n",
     "\n",
@@ -918,7 +911,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   },
   "orig_nbformat": 4
  },
diff --git a/notebooks/700_metrics/701d_aupimo_advanced_iii.ipynb b/notebooks/700_metrics/701d_aupimo_advanced_iii.ipynb
index 7cbd29823b..092791ef95 100644
--- a/notebooks/700_metrics/701d_aupimo_advanced_iii.ipynb
+++ b/notebooks/700_metrics/701d_aupimo_advanced_iii.ipynb
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   },
   "orig_nbformat": 4
  },
diff --git a/src/anomalib/metrics/pimo/binary_classification_curve.py b/src/anomalib/metrics/pimo/binary_classification_curve.py
index 1a80944041..e576b7902f 100644
--- a/src/anomalib/metrics/pimo/binary_classification_curve.py
+++ b/src/anomalib/metrics/pimo/binary_classification_curve.py
@@ -158,7 +158,7 @@ def binary_classification_curve(
     return torch.from_numpy(result).to(scores_batch.device)
 
 
-def _get_linspaced_thresholds(anomaly_maps: torch.Tensor, num_thresholds: int) -> torch.Tensor:
+def _get_minmax_linspaced_thresholds(anomaly_maps: torch.Tensor, num_thresholds: int) -> torch.Tensor:
     """Get thresholds linearly spaced between the min and max of the anomaly maps."""
     _validate.is_num_thresholds_gte2(num_thresholds)
     # this operation can be a bit expensive
@@ -241,7 +241,7 @@ def threshold_and_binary_classification_curve(
                 f"but it is ignored because `thresholds_choice` is '{threshold_choice.value}'.",
             )
         # `num_thresholds` is validated in the function below
-        thresholds = _get_linspaced_thresholds(anomaly_maps, num_thresholds)
+        thresholds = _get_minmax_linspaced_thresholds(anomaly_maps, num_thresholds)
 
     elif threshold_choice == ThresholdMethod.MEAN_FPR_OPTIMIZED:
         raise NotImplementedError(f"TODO implement {threshold_choice.value}")  # noqa: EM102
diff --git a/src/anomalib/metrics/pimo/dataclasses.py b/src/anomalib/metrics/pimo/dataclasses.py
index 3eaa04cd12..759ecf1b6b 100644
--- a/src/anomalib/metrics/pimo/dataclasses.py
+++ b/src/anomalib/metrics/pimo/dataclasses.py
@@ -31,6 +31,10 @@ class PIMOResult:
             thresholds
     """
 
+    # metadata
+    fpr_lower_bound: float
+    fpr_upper_bound: float
+
     # data
     thresholds: torch.Tensor = field(repr=False)  # shape => (K,)
     shared_fpr: torch.Tensor = field(repr=False)  # shape => (K,)
@@ -80,6 +84,25 @@ def __post_init__(self) -> None:
             )
             raise TypeError(msg)
 
+        first_shared_fpr = self.shared_fpr[0]
+        last_shared_fpr = self.shared_fpr[-1]
+
+        if not torch.isclose(first_shared_fpr, torch.tensor(self.fpr_upper_bound, dtype=torch.float64), rtol=1e-2):
+            msg = (
+                f"Invalid {self.__class__.__name__} object. "
+                "The first shared FPR value is not equal to the upper bound: "
+                f"{first_shared_fpr=} != {self.fpr_upper_bound=}."
+            )
+            raise ValueError(msg)
+
+        if not torch.isclose(last_shared_fpr, torch.tensor(self.fpr_lower_bound, dtype=torch.float64), rtol=1e-2):
+            msg = (
+                f"Invalid {self.__class__.__name__} object. "
+                "The last shared FPR value is not equal to the lower bound: "
+                f"{last_shared_fpr=} != {self.fpr_lower_bound=}."
+            )
+            raise ValueError(msg)
+
     def thresh_at(self, fpr_level: float) -> tuple[int, float, float]:
         """Return the threshold at the given shared FPR.
 
@@ -183,7 +206,6 @@ def __post_init__(self) -> None:
     def from_pimo_result(
         cls: type["AUPIMOResult"],
         pimo_result: PIMOResult,
-        fpr_bounds: tuple[float, float],
         num_thresholds_auc: int,
         aupimos: torch.Tensor,
     ) -> "AUPIMOResult":
@@ -211,16 +233,12 @@ def from_pimo_result(
             msg = "Expected all anomalous images to have valid AUPIMOs (not nan), but some have NaN values."
             raise TypeError(msg)
 
-        fpr_lower_bound, fpr_upper_bound = fpr_bounds
-        # recall: fpr upper/lower bounds are the same as the thresh lower/upper bounds
-        _, thresh_lower_bound, __ = pimo_result.thresh_at(fpr_upper_bound)
-        _, thresh_upper_bound, __ = pimo_result.thresh_at(fpr_lower_bound)
-        # `_` is the threshold's index, `__` is the actual fpr value
         return cls(
-            fpr_lower_bound=fpr_lower_bound,
-            fpr_upper_bound=fpr_upper_bound,
+            fpr_lower_bound=pimo_result.fpr_lower_bound,
+            fpr_upper_bound=pimo_result.fpr_upper_bound,
             num_thresholds=num_thresholds_auc,
-            thresh_lower_bound=float(thresh_lower_bound),
-            thresh_upper_bound=float(thresh_upper_bound),
+            # recall: fpr upper/lower bounds are the same as the thresh lower/upper bounds
+            thresh_lower_bound=float(pimo_result.thresholds[0].item()),
+            thresh_upper_bound=float(pimo_result.thresholds[-1].item()),
             aupimos=aupimos,
         )
diff --git a/src/anomalib/metrics/pimo/functional.py b/src/anomalib/metrics/pimo/functional.py
index 7eac07b1bd..15b2ff53a0 100644
--- a/src/anomalib/metrics/pimo/functional.py
+++ b/src/anomalib/metrics/pimo/functional.py
@@ -18,7 +18,6 @@
 from . import _validate
 from .binary_classification_curve import (
     ThresholdMethod,
-    _get_linspaced_thresholds,
     per_image_fpr,
     per_image_tpr,
     threshold_and_binary_classification_curve,
@@ -31,7 +30,8 @@
 def pimo_curves(
     anomaly_maps: torch.Tensor,
     masks: torch.Tensor,
-    num_thresholds: int,
+    fpr_bounds: tuple[float, float] = (1e-5, 1e-4),
+    num_thresholds: int = 300,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """Compute the Per-IMage Overlap (PIMO, pronounced pee-mo) curves.
 
@@ -48,9 +48,10 @@ def pimo_curves(
         K: number of thresholds
 
     Args:
-        anomaly_maps: floating point anomaly score maps of shape (N, H, W)
-        masks: binary (bool or int) ground truth masks of shape (N, H, W)
-        num_thresholds: number of thresholds to compute (K)
+        anomaly_maps: floating point anomaly score maps of shape (N, H, W).
+        masks: binary (bool or int) ground truth masks of shape (N, H, W).
+        fpr_bounds: lower and upper bounds of the FPR integration range. Default is (1e-5, 1e-4).
+        num_thresholds: number of thresholds to compute (K). Default is 300.
 
     Returns:
         tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -59,7 +60,7 @@ def pimo_curves(
             [2] per-image TPR curves of shape (N, K), axis 1 in descending order (indices correspond to the thresholds)
             [3] image classes of shape (N,) with values 0 (normal) or 1 (anomalous)
     """
-    # validate the strings are valid
+    _validate.is_rate_range(fpr_bounds)
     _validate.is_num_thresholds_gte2(num_thresholds)
     _validate.is_anomaly_maps(anomaly_maps)
     _validate.is_masks(masks)
@@ -68,15 +69,18 @@ def pimo_curves(
     _validate.has_at_least_one_normal_image(masks)
 
     image_classes = images_classes_from_masks(masks)
+    anomaly_maps_normal_images = anomaly_maps[image_classes == 0]
 
-    # the thresholds are computed here so that they can be restrained to the normal images
-    # therefore getting a better resolution in terms of FPR quantization
-    # otherwise the function `binclf_curve_numpy.per_image_binclf_curve` would have the range of thresholds
-    # computed from all the images (normal + anomalous)
-    thresholds = _get_linspaced_thresholds(
-        anomaly_maps[image_classes == 0],
-        num_thresholds,
-    )
+    fpr_lower_bound, fpr_upper_bound = fpr_bounds
+
+    # find the thresholds at the given FPR bounds
+    threshold_at_fpr_lower_bound = _binary_search_threshold_at_fpr_target(anomaly_maps_normal_images, fpr_lower_bound)
+    threshold_at_fpr_upper_bound = _binary_search_threshold_at_fpr_target(anomaly_maps_normal_images, fpr_upper_bound)
+
+    # reminder: fpr lower/upper bound is threshold upper/lower bound (reversed)
+    threshold_lower_bound = threshold_at_fpr_upper_bound
+    threshold_upper_bound = threshold_at_fpr_lower_bound
+    thresholds = torch.linspace(threshold_lower_bound, threshold_upper_bound, num_thresholds, dtype=anomaly_maps.dtype)
 
     # N: number of images, K: number of thresholds
     # shapes are (K,) and (N, K, 2, 2)
@@ -115,8 +119,8 @@ def pimo_curves(
 def aupimo_scores(
     anomaly_maps: torch.Tensor,
     masks: torch.Tensor,
-    num_thresholds: int = 300_000,
     fpr_bounds: tuple[float, float] = (1e-5, 1e-4),
+    num_thresholds: int = 300,
     force: bool = False,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int]:
     """Compute the PIMO curves and their Area Under the Curve (i.e. AUPIMO) scores.
@@ -135,8 +139,8 @@ def aupimo_scores(
     Args:
         anomaly_maps: floating point anomaly score maps of shape (N, H, W)
         masks: binary (bool or int) ground truth masks of shape (N, H, W)
-        num_thresholds: number of thresholds to compute (K)
-        fpr_bounds: lower and upper bounds of the FPR integration range
+        fpr_bounds: lower and upper bounds of the FPR integration range. Default is (1e-5, 1e-4).
+        num_thresholds: number of thresholds to compute (K). Default is 300.
         force: whether to force the computation despite bad conditions
 
     Returns:
@@ -148,14 +152,14 @@ def aupimo_scores(
             [4] AUPIMO scores of shape (N,) in [0, 1]
             [5] number of points used in the AUC integration
     """
-    _validate.is_rate_range(fpr_bounds)
-
-    # other validations are done in the `pimo` function
+    # validations are done in the function `pimo_curves`
     thresholds, shared_fpr, per_image_tprs, image_classes = pimo_curves(
         anomaly_maps=anomaly_maps,
         masks=masks,
         num_thresholds=num_thresholds,
+        fpr_bounds=fpr_bounds,
     )
+
     try:
         _validate.is_valid_threshold(thresholds)
         _validate.is_rate_curve(shared_fpr, nan_allowed=False, decreasing=True)
@@ -166,19 +170,18 @@ def aupimo_scores(
         msg = f"Cannot compute AUPIMO because the PIMO curves are invalid. Cause: {ex}"
         raise RuntimeError(msg) from ex
 
+    if num_thresholds < 300:
+        logger.warning(
+            "The AUPIMO may be inaccurate because the integration range doesn't have enough points. "
+            f"Try increasing the values of {num_thresholds=}.",
+        )
+
     fpr_lower_bound, fpr_upper_bound = fpr_bounds
 
-    # get the threshold indices where the fpr bounds are achieved
-    fpr_lower_bound_thresh_idx, _, fpr_lower_bound_defacto = thresh_at_shared_fpr_level(
-        thresholds,
-        shared_fpr,
-        fpr_lower_bound,
-    )
-    fpr_upper_bound_thresh_idx, _, fpr_upper_bound_defacto = thresh_at_shared_fpr_level(
-        thresholds,
-        shared_fpr,
-        fpr_upper_bound,
-    )
+    # get the fpr actual values at the lower/upper bounds of the integration range
+    # reminder: fpr lower/upper bound is threshold upper/lower bound (reversed)
+    fpr_lower_bound_defacto = shared_fpr[-1]
+    fpr_upper_bound_defacto = shared_fpr[0]
 
     if not torch.isclose(
         fpr_lower_bound_defacto,
@@ -200,32 +203,27 @@ def aupimo_scores(
             f"Expected {fpr_upper_bound} but got {fpr_upper_bound_defacto}, which is not within {rtol=}.",
         )
 
+    # at which threshold the fpr bounds are achieved
     # reminder: fpr lower/upper bound is threshold upper/lower bound (reversed)
-    thresh_lower_bound_idx = fpr_upper_bound_thresh_idx
-    thresh_upper_bound_idx = fpr_lower_bound_thresh_idx
+    threshold_high_bound = thresholds[-1]  # at fpr lower bound
+    threshold_low_bound = thresholds[0]  # at fpr upper bound
 
     # deal with edge cases
-    if thresh_lower_bound_idx >= thresh_upper_bound_idx:
+    if threshold_low_bound >= threshold_high_bound:
         msg = (
             "The thresholds corresponding to the given `fpr_bounds` are not valid because "
             "they matched the same threshold or the are in the wrong order. "
-            f"FPR upper/lower = threshold lower/upper = {thresh_lower_bound_idx} and {thresh_upper_bound_idx}."
+            f"FPR upper/lower --> threshold lower|upper = {threshold_low_bound}|{threshold_high_bound}."
         )
         raise RuntimeError(msg)
 
-    # limit the curves to the integration range [lbound, ubound]
-    shared_fpr_bounded: torch.Tensor = shared_fpr[thresh_lower_bound_idx : (thresh_upper_bound_idx + 1)]
-    per_image_tprs_bounded: torch.Tensor = per_image_tprs[:, thresh_lower_bound_idx : (thresh_upper_bound_idx + 1)]
-
     # `shared_fpr` and `tprs` are in descending order; `flip()` reverts to ascending order
-    shared_fpr_bounded = torch.flip(shared_fpr_bounded, dims=[0])
-    per_image_tprs_bounded = torch.flip(per_image_tprs_bounded, dims=[1])
-
     # the log's base does not matter because it's a constant factor canceled by normalization factor
-    shared_fpr_bounded_log = torch.log(shared_fpr_bounded)
+    auc_shared_fpr = torch.log(torch.flip(shared_fpr, dims=[0]))
+    auc_per_image_tprs = torch.flip(per_image_tprs, dims=[1])
 
     # deal with edge cases
-    invalid_shared_fpr = ~torch.isfinite(shared_fpr_bounded_log)
+    invalid_shared_fpr = ~torch.isfinite(auc_shared_fpr)
 
     if invalid_shared_fpr.all():
         msg = (
@@ -241,15 +239,16 @@ def aupimo_scores(
         )
 
         # get rid of nan values by removing them from the integration range
-        shared_fpr_bounded_log = shared_fpr_bounded_log[~invalid_shared_fpr]
-        per_image_tprs_bounded = per_image_tprs_bounded[:, ~invalid_shared_fpr]
+        auc_shared_fpr = auc_shared_fpr[~invalid_shared_fpr]
+        auc_per_image_tprs = auc_per_image_tprs[:, ~invalid_shared_fpr]
 
-    num_points_integral = int(shared_fpr_bounded_log.shape[0])
+    # the code above may remove too many points, so we check if there are enough points to integrate
+    num_points_integral = int(auc_shared_fpr.shape[0])
 
     if num_points_integral <= 30:
         msg = (
             "Cannot compute AUPIMO because the shared fpr integration range doesn't have enough points. "
-            f"Found {num_points_integral} points in the integration range. "
+            f"Found {num_points_integral=} points in the integration range. "
             "Try increasing `num_thresholds`."
         )
         if not force:
@@ -260,11 +259,11 @@ def aupimo_scores(
     if num_points_integral < 300:
         logger.warning(
             "The AUPIMO may be inaccurate because the shared fpr integration range doesn't have enough points. "
-            f"Found {num_points_integral} points in the integration range. "
+            f"Found {num_points_integral=} points in the integration range. "
             "Try increasing `num_thresholds`.",
         )
 
-    aucs: torch.Tensor = torch.trapezoid(per_image_tprs_bounded, x=shared_fpr_bounded_log, axis=1)
+    aucs: torch.Tensor = torch.trapezoid(auc_per_image_tprs, x=auc_shared_fpr, axis=1)
 
     # normalize, then clip(0, 1) makes sure that the values are in [0, 1] in case of numerical errors
     normalization_factor = aupimo_normalizing_factor(fpr_bounds)
@@ -276,6 +275,73 @@ def aupimo_scores(
 # =========================================== AUX ===========================================
 
 
+def _binary_search_threshold_at_fpr_target(
+    anomaly_maps_normals: torch.Tensor,
+    fpr_target: float | torch.Tensor,
+    maximum_iterations: int = 300,
+) -> float:
+    """Binary search of threshold that achieves the given shared FPR level.
+
+    Args:
+        anomaly_maps_normals: anomaly score maps of normal images.
+        fpr_target: shared FPR level at which to get the threshold.
+        maximum_iterations: maximum number of iterations for the binary search. Default is 300.
+
+    Returns:
+        float: the threshold that achieves the given shared FPR level.
+    """
+    # binary search bounds
+    lower = anomaly_maps_normals.min()
+    upper = anomaly_maps_normals.max()
+    fpr_target = torch.tensor(fpr_target, dtype=torch.float64)
+
+    # edge case
+    if lower == upper:
+        return lower.item()
+
+    def get_middle(lower: torch.Tensor, upper: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        middle = (lower + upper) / 2
+        fpr_at_middle = (anomaly_maps_normals >= middle).double().mean()
+        return middle, fpr_at_middle
+
+    for iteration in range(maximum_iterations):  # noqa: B007
+        middle, fpr_at_middle = get_middle(lower, upper)
+
+        bounds_are_close = torch.isclose(lower, upper, rtol=1e-6)
+        target_is_close = torch.isclose(fpr_at_middle, fpr_target, rtol=1e-2)
+
+        if bounds_are_close and target_is_close:
+            break
+
+        # when they are too close, the sign of the difference is not reliable
+        # so we make a "half" replacement of the upper/lower bound
+        make_big_step = not target_is_close
+
+        if fpr_at_middle < fpr_target:
+            upper = middle if make_big_step else (middle + upper) / 2
+        else:
+            lower = middle if make_big_step else (lower + middle) / 2
+
+    if iteration == maximum_iterations - 1:
+        logger.warning(
+            f"Binary search reached the maximum number of iterations ({iteration + 1}). "
+            "The result may not be accurate. "
+            f"Target FPR: {fpr_target:.8g}, achieved FPR: {fpr_at_middle:.8g}. "
+            f"Thresholds: {lower=:.8g}, {middle=:.8g}, {upper=:.8g}. "
+            f"{bounds_are_close=} {target_is_close=}. "
+            f"Try increasing the resolution of the anomaly score maps.",
+        )
+    else:
+        logger.debug(
+            f"Binary search stoped with {iteration + 1} iterations. "
+            f"Target FPR: {fpr_target:.8g}, achieved FPR: {fpr_at_middle:.8g}. "
+            f"Thresholds: {lower=:.8g}, {middle=:.8g}, {upper=:.8g} "
+            f"{bounds_are_close=} {target_is_close=}.",
+        )
+
+    return middle.item()
+
+
 def thresh_at_shared_fpr_level(
     thresholds: torch.Tensor,
     shared_fpr: torch.Tensor,
@@ -306,14 +372,14 @@ def thresh_at_shared_fpr_level(
 
     shared_fpr_min, shared_fpr_max = shared_fpr.min(), shared_fpr.max()
 
-    if fpr_level < shared_fpr_min:
+    if fpr_level < shared_fpr_min and not torch.isclose(shared_fpr_min, torch.tensor(fpr_level).double(), rtol=1e-1):
         msg = (
             "Invalid `fpr_level` because it's out of the range of `shared_fpr` = "
             f"[{shared_fpr_min}, {shared_fpr_max}], and got {fpr_level}."
         )
         raise ValueError(msg)
 
-    if fpr_level > shared_fpr_max:
+    if fpr_level > shared_fpr_max and not torch.isclose(shared_fpr_min, torch.tensor(fpr_level).double(), rtol=1e-1):
         msg = (
             "Invalid `fpr_level` because it's out of the range of `shared_fpr` = "
             f"[{shared_fpr_min}, {shared_fpr_max}], and got {fpr_level}."
diff --git a/src/anomalib/metrics/pimo/pimo.py b/src/anomalib/metrics/pimo/pimo.py
index 9703b60b59..49c57c088b 100644
--- a/src/anomalib/metrics/pimo/pimo.py
+++ b/src/anomalib/metrics/pimo/pimo.py
@@ -74,8 +74,8 @@ class PIMO(Metric):
         masks: binary (bool or int) ground truth masks of shape (N, H, W)
 
     Args:
-        num_thresholds: number of thresholds to compute (K)
-        binclf_algorithm: algorithm to compute the binary classifier curve (see `binclf_curve_numpy.Algorithm`)
+        fpr_bounds: lower and upper bounds of the FPR integration range
+        num_thresholds: number of thresholds used to compute the PIMO curve and AUPIMO scores (K)
 
     Returns:
         PIMOResult: PIMO curves dataclass object. See `PIMOResult` for details.
@@ -85,8 +85,8 @@ class PIMO(Metric):
     higher_is_better: bool | None = None
     full_state_update: bool = False
 
+    fpr_bounds: tuple[float, float]
     num_thresholds: int
-    binclf_algorithm: str
 
     anomaly_maps: list[torch.Tensor]
     masks: list[torch.Tensor]
@@ -106,11 +106,12 @@ def image_classes(self) -> torch.Tensor:
         """Image classes (0: normal, 1: anomalous)."""
         return functional.images_classes_from_masks(self.masks)
 
-    def __init__(self, num_thresholds: int) -> None:
+    def __init__(self, fpr_bounds: tuple[float, float] = (1e-5, 1e-4), num_thresholds: int = 300) -> None:
         """Per-Image Overlap (PIMO) curve.
 
         Args:
-            num_thresholds: number of thresholds used to compute the PIMO curve (K)
+            fpr_bounds: lower and upper bounds of the FPR integration range
+            num_thresholds: number of thresholds used to compute the PIMO curve and AUPIMO scores (K)
         """
         super().__init__()
 
@@ -122,6 +123,9 @@ def __init__(self, num_thresholds: int) -> None:
         # the options below are, redundantly, validated here to avoid reaching
         # an error later in the execution
 
+        _validate.is_rate_range(fpr_bounds)
+        self.fpr_bounds = fpr_bounds
+
         _validate.is_num_thresholds_gte2(num_thresholds)
         self.num_thresholds = num_thresholds
 
@@ -158,9 +162,12 @@ def compute(self) -> PIMOResult:
         thresholds, shared_fpr, per_image_tprs, _ = functional.pimo_curves(
             anomaly_maps,
             masks,
-            self.num_thresholds,
+            fpr_bounds=self.fpr_bounds,
+            num_thresholds=self.num_thresholds,
         )
         return PIMOResult(
+            fpr_lower_bound=self.fpr_bounds[0],
+            fpr_upper_bound=self.fpr_bounds[1],
             thresholds=thresholds,
             shared_fpr=shared_fpr,
             per_image_tprs=per_image_tprs,
@@ -190,8 +197,8 @@ class AUPIMO(PIMO):
         masks: binary (bool or int) ground truth masks of shape (N, H, W)
 
     Args:
-        num_thresholds: number of thresholds to compute (K)
         fpr_bounds: lower and upper bounds of the FPR integration range
+        num_thresholds: number of thresholds used to compute the PIMO curve and AUPIMO scores (K)
         force: whether to force the computation despite bad conditions
 
     Returns:
@@ -224,25 +231,21 @@ def __repr__(self) -> str:
 
     def __init__(
         self,
-        num_thresholds: int = 300_000,
         fpr_bounds: tuple[float, float] = (1e-5, 1e-4),
+        num_thresholds: int = 300,
         return_average: bool = True,
         force: bool = False,
     ) -> None:
         """Area Under the Per-Image Overlap (PIMO) curve.
 
         Args:
-            num_thresholds: [passed to parent `PIMO`] number of thresholds used to compute the PIMO curve
             fpr_bounds: lower and upper bounds of the FPR integration range
+            num_thresholds: number of thresholds used to compute the PIMO curve and AUPIMO scores (K)
             return_average: if True, return the average AUPIMO score; if False, return all the individual AUPIMO scores
             force: if True, force the computation of the AUPIMO scores even in bad conditions (e.g. few points)
         """
-        super().__init__(num_thresholds=num_thresholds)
-
-        # other validations are done in PIMO.__init__()
-
-        _validate.is_rate_range(fpr_bounds)
-        self.fpr_bounds = fpr_bounds
+        # validations are done in PIMO.__init__()
+        super().__init__(fpr_bounds=fpr_bounds, num_thresholds=num_thresholds)
         self.return_average = return_average
         self.force = force
 
@@ -270,19 +273,20 @@ def compute(self, force: bool | None = None) -> tuple[PIMOResult, AUPIMOResult]:
         thresholds, shared_fpr, per_image_tprs, _, aupimos, num_thresholds_auc = functional.aupimo_scores(
             anomaly_maps,
             masks,
-            self.num_thresholds,
             fpr_bounds=self.fpr_bounds,
+            num_thresholds=self.num_thresholds,
             force=force,
         )
 
         pimo_result = PIMOResult(
+            fpr_lower_bound=self.fpr_bounds[0],
+            fpr_upper_bound=self.fpr_bounds[1],
             thresholds=thresholds,
             shared_fpr=shared_fpr,
             per_image_tprs=per_image_tprs,
         )
         aupimo_result = AUPIMOResult.from_pimo_result(
             pimo_result,
-            fpr_bounds=self.fpr_bounds,
             # not `num_thresholds`!
             # `num_thresholds` is the number of thresholds used to compute the PIMO curve
             # this is the number of thresholds used to compute the AUPIMO integral
diff --git a/tests/unit/metrics/pimo/test_pimo.py b/tests/unit/metrics/pimo/test_pimo.py
index 81bafe4c8e..e0e9f92083 100644
--- a/tests/unit/metrics/pimo/test_pimo.py
+++ b/tests/unit/metrics/pimo/test_pimo.py
@@ -22,7 +22,6 @@ def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
     All functions are parametrized with the same setting: 1 normal and 2 anomalous images.
     The anomaly maps are the same for all functions, but the masks are different.
     """
-    expected_thresholds = torch.arange(1, 7 + 1, dtype=torch.float32)
     shape = (1000, 1000)  # (H, W), 1 million pixels
 
     # --- normal ---
@@ -30,6 +29,8 @@ def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
     # value:   7   6    5    4    3    2     1
     # count:   1   9   90  900   9k   90k  900k
     # cumsum:  1  10  100   1k  10k  100k    1M
+    # proportion (1e{})
+    #         -6  -5   -4   -3   -2    -1     0
     pred_norm = torch.ones(1_000_000, dtype=torch.float32)
     pred_norm[:100_000] += 1
     pred_norm[:10_000] += 1
@@ -40,59 +41,22 @@ def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
     pred_norm = pred_norm.reshape(shape)
     mask_norm = torch.zeros_like(pred_norm, dtype=torch.int32)
 
-    expected_fpr_norm = torch.tensor([1.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6], dtype=torch.float64)
-    expected_tpr_norm = torch.full((7,), torch.nan, dtype=torch.float64)
-
     # --- anomalous ---
     pred_anom1 = pred_norm.clone()
     mask_anom1 = torch.ones_like(pred_anom1, dtype=torch.int32)
-    expected_tpr_anom1 = expected_fpr_norm.clone()
 
     # only the first 100_000 pixels are anomalous
     # which corresponds to the first 100_000 highest scores (2 to 7)
     pred_anom2 = pred_norm.clone()
     mask_anom2 = torch.concatenate([torch.ones(100_000), torch.zeros(900_000)]).reshape(shape).to(torch.int32)
-    expected_tpr_anom2 = (10 * expected_fpr_norm).clip(0, 1)
 
     anomaly_maps = torch.stack([pred_norm, pred_anom1, pred_anom2], axis=0)
     masks = torch.stack([mask_norm, mask_anom1, mask_anom2], axis=0)
 
-    expected_shared_fpr = expected_fpr_norm
-    expected_per_image_tprs = torch.stack([expected_tpr_norm, expected_tpr_anom1, expected_tpr_anom2], axis=0)
-    expected_image_classes = torch.tensor([0, 1, 1], dtype=torch.int32)
-
-    if metafunc.function is test_pimo or metafunc.function is test_aupimo_values:
-        argvalues_tensors = [
-            (
-                anomaly_maps,
-                masks,
-                expected_thresholds,
-                expected_shared_fpr,
-                expected_per_image_tprs,
-                expected_image_classes,
-            ),
-            (
-                10 * anomaly_maps,
-                masks,
-                10 * expected_thresholds,
-                expected_shared_fpr,
-                expected_per_image_tprs,
-                expected_image_classes,
-            ),
-        ]
-        metafunc.parametrize(
-            argnames=(
-                "anomaly_maps",
-                "masks",
-                "expected_thresholds",
-                "expected_shared_fpr",
-                "expected_per_image_tprs",
-                "expected_image_classes",
-            ),
-            argvalues=argvalues_tensors,
-        )
+    if metafunc.function is test_pimo or metafunc.function is test_aupimo or metafunc.function is test_aupimo_edge:
+        metafunc.parametrize(argnames=("anomaly_maps", "masks"), argvalues=[(anomaly_maps, masks)])
 
-    if metafunc.function is test_aupimo_values:
+    if metafunc.function is test_aupimo:
         argvalues_tensors = [
             (
                 (1e-1, 1.0),
@@ -138,173 +102,177 @@ def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
             argvalues=argvalues_tensors,
         )
 
-    if metafunc.function is test_aupimo_edge:
-        metafunc.parametrize(
-            argnames=(
-                "anomaly_maps",
-                "masks",
-            ),
-            argvalues=[
-                (
-                    anomaly_maps,
-                    masks,
-                ),
-                (
-                    10 * anomaly_maps,
-                    masks,
-                ),
-            ],
-        )
-        metafunc.parametrize(
-            argnames=("fpr_bounds",),
-            argvalues=[
-                ((1e-1, 1.0),),
-                ((1e-3, 1e-2),),
-                ((1e-5, 1e-4),),
-                (None,),
-            ],
-        )
+    # === random values ===
+    generator = torch.Generator().manual_seed(42)
+    masks_normals = torch.zeros((6, 1024, 1024), dtype=torch.int32)
+    anomaly_maps_normals = torch.normal(0, 1, (6, 1024, 1024), generator=generator)
+    masks_anomalous = torch.zeros_like(masks_normals)
+    # make some pixels anomalous
+    masks_anomalous[0, 512:, 512:] = 1
+    masks_anomalous[1, :512, :512] = 1
+    masks_anomalous[2, :512, 512:] = 1
+    masks_anomalous[3, 512:, :512] = 1
+    masks_anomalous[4, 256:768, 256:768] = 1
+    masks_anomalous[5, 256:768, 256:768] = 1
+    anomaly_maps_anomalous = torch.where(
+        masks_anomalous.bool(),
+        torch.normal(1, 1, (6, 1024, 1024), generator=generator),
+        torch.normal(0, 1, (6, 1024, 1024), generator=generator),
+    )
+    anomaly_maps = torch.concatenate([anomaly_maps_normals, anomaly_maps_anomalous], axis=0)
+    masks = torch.concatenate([masks_normals, masks_anomalous], axis=0)
 
+    if metafunc.function is test_pimo_random_values or metafunc.function is test_aupimo_random_values:
+        metafunc.parametrize(argnames=("anomaly_maps", "masks"), argvalues=[(anomaly_maps, masks)])
 
-def _do_test_pimo_outputs(
-    thresholds: Tensor,
-    shared_fpr: Tensor,
-    per_image_tprs: Tensor,
-    image_classes: Tensor,
-    expected_thresholds: Tensor,
-    expected_shared_fpr: Tensor,
-    expected_per_image_tprs: Tensor,
-    expected_image_classes: Tensor,
-) -> None:
-    """Test if the outputs of any of the PIMO interfaces are correct."""
-    assert isinstance(shared_fpr, Tensor)
-    assert isinstance(per_image_tprs, Tensor)
-    assert isinstance(image_classes, Tensor)
-    assert isinstance(expected_thresholds, Tensor)
-    assert isinstance(expected_shared_fpr, Tensor)
-    assert isinstance(expected_per_image_tprs, Tensor)
-    assert isinstance(expected_image_classes, Tensor)
-    allclose = torch.allclose
-
-    assert thresholds.ndim == 1
-    assert shared_fpr.ndim == 1
-    assert per_image_tprs.ndim == 2
-    assert tuple(image_classes.shape) == (3,)
-
-    assert allclose(thresholds, expected_thresholds)
-    assert allclose(shared_fpr, expected_shared_fpr)
-    assert allclose(per_image_tprs, expected_per_image_tprs, equal_nan=True)
-    assert (image_classes == expected_image_classes).all()
 
+def test_pimo_random_values(anomaly_maps: Tensor, masks: Tensor) -> None:
+    """Make sure the function runs without errors, types and shapes are correct."""
+    # metric interface
+    metric = pimo.PIMO(fpr_bounds=(1e-5, 1e-3), num_thresholds=300)
+    metric.update(anomaly_maps, masks)
+    pimo_result: PIMOResult = metric.compute()
 
-def test_pimo(
-    anomaly_maps: Tensor,
-    masks: Tensor,
-    expected_thresholds: Tensor,
-    expected_shared_fpr: Tensor,
-    expected_per_image_tprs: Tensor,
-    expected_image_classes: Tensor,
-) -> None:
-    """Test if `pimo()` returns the expected values."""
+    assert isinstance(pimo_result.thresholds, Tensor)
+    assert pimo_result.thresholds.ndim == 1
+    assert pimo_result.thresholds.shape == (300,)
 
-    def do_assertions(pimo_result: PIMOResult) -> None:
-        thresholds = pimo_result.thresholds
-        shared_fpr = pimo_result.shared_fpr
-        per_image_tprs = pimo_result.per_image_tprs
-        image_classes = pimo_result.image_classes
-        _do_test_pimo_outputs(
-            thresholds,
-            shared_fpr,
-            per_image_tprs,
-            image_classes,
-            expected_thresholds,
-            expected_shared_fpr,
-            expected_per_image_tprs,
-            expected_image_classes,
-        )
+    assert isinstance(pimo_result.shared_fpr, Tensor)
+    assert pimo_result.shared_fpr.ndim == 1
+    assert pimo_result.shared_fpr.shape == (300,)
+
+    assert isinstance(pimo_result.per_image_tprs, Tensor)
+    assert pimo_result.per_image_tprs.ndim == 2
+    assert pimo_result.per_image_tprs.shape == (12, 300)
 
+    assert isinstance(pimo_result.image_classes, Tensor)
+    assert pimo_result.image_classes.shape == (12,)
+
+    fpr_upper_bound_defacto = pimo_result.shared_fpr[0]
+    assert torch.isclose(fpr_upper_bound_defacto, torch.tensor(1e-3, dtype=torch.float64), rtol=1e-3)
+
+    fpr_lower_bound_defacto = pimo_result.shared_fpr[-1]
+    assert torch.isclose(fpr_lower_bound_defacto, torch.tensor(1e-5, dtype=torch.float64), rtol=1e-3)
+
+
+def test_aupimo_random_values(anomaly_maps: Tensor, masks: Tensor) -> None:
+    """Make sure the function runs without errors, types and shapes are correct."""
     # metric interface
-    metric = pimo.PIMO(
-        num_thresholds=7,
+    metric = pimo.AUPIMO(
+        fpr_bounds=(1e-5, 1e-3),
+        num_thresholds=300,
+        return_average=False,
+        force=False,
     )
     metric.update(anomaly_maps, masks)
-    pimo_result = metric.compute()
-    do_assertions(pimo_result)
+    aupimo_result: AUPIMOResult
+    _, aupimo_result = metric.compute()
+
+    assert aupimo_result.fpr_bounds == (1e-5, 1e-3)
+
+    assert aupimo_result.thresh_lower_bound < aupimo_result.thresh_upper_bound
+    assert anomaly_maps.min() < aupimo_result.thresh_lower_bound < aupimo_result.thresh_upper_bound < anomaly_maps.max()
 
+    assert isinstance(aupimo_result.aupimos, Tensor)
+    assert aupimo_result.aupimos.ndim == 1
+    assert aupimo_result.aupimos.shape == (12,)
 
-def _do_test_aupimo_outputs(
+
+def _assert_pimo_result_close_to_expected(
     thresholds: Tensor,
     shared_fpr: Tensor,
     per_image_tprs: Tensor,
     image_classes: Tensor,
-    aupimos: Tensor,
     expected_thresholds: Tensor,
     expected_shared_fpr: Tensor,
     expected_per_image_tprs: Tensor,
     expected_image_classes: Tensor,
-    expected_aupimos: Tensor,
 ) -> None:
-    _do_test_pimo_outputs(
-        thresholds,
-        shared_fpr,
-        per_image_tprs,
-        image_classes,
-        expected_thresholds,
-        expected_shared_fpr,
-        expected_per_image_tprs,
-        expected_image_classes,
+    """Test if the outputs of any of the PIMO interfaces are correct."""
+    assert torch.allclose(thresholds, expected_thresholds, atol=1e-2)
+    assert torch.allclose(shared_fpr, expected_shared_fpr)
+    assert torch.allclose(per_image_tprs, expected_per_image_tprs, equal_nan=True)
+    assert (image_classes == expected_image_classes).all()
+
+
+def test_pimo(anomaly_maps: Tensor, masks: Tensor) -> None:
+    """Test if `pimo()` returns the expected values."""
+    # metric interface
+    metric = pimo.PIMO(fpr_bounds=(1e-5, 1e-3), num_thresholds=3)
+    metric.update(anomaly_maps, masks)
+    pimo_result: PIMOResult = metric.compute()
+    _assert_pimo_result_close_to_expected(
+        thresholds=pimo_result.thresholds,
+        shared_fpr=pimo_result.shared_fpr,
+        per_image_tprs=pimo_result.per_image_tprs,
+        image_classes=pimo_result.image_classes,
+        expected_thresholds=torch.tensor([4, 5, 6], dtype=torch.float32),
+        expected_shared_fpr=torch.tensor([1e-3, 1e-4, 1e-5], dtype=torch.float64),
+        expected_per_image_tprs=torch.stack(
+            [
+                torch.full((3,), torch.nan, dtype=torch.float64),
+                torch.tensor([1e-3, 1e-4, 1e-5], dtype=torch.float64),
+                torch.tensor([1e-2, 1e-3, 1e-4], dtype=torch.float64),
+            ],
+            axis=0,
+        ),
+        expected_image_classes=torch.tensor([0, 1, 1], dtype=torch.int32),
+    )
+
+    # multiplying all scores by a factor should not change the results, only the thresholds
+    metric = pimo.PIMO(fpr_bounds=(1e-5, 1e-3), num_thresholds=3)
+    metric.update(10 * anomaly_maps, masks)  # x10 anomaly maps
+    pimo_result_x10: PIMOResult = metric.compute()
+    _assert_pimo_result_close_to_expected(
+        thresholds=pimo_result_x10.thresholds,
+        shared_fpr=pimo_result_x10.shared_fpr,
+        per_image_tprs=pimo_result_x10.per_image_tprs,
+        image_classes=pimo_result_x10.image_classes,
+        # x10 as well
+        expected_thresholds=torch.tensor([40, 50, 60], dtype=torch.float32),
+        # all other values are the same
+        expected_shared_fpr=torch.tensor([1e-3, 1e-4, 1e-5], dtype=torch.float64),
+        expected_per_image_tprs=torch.stack(
+            [
+                torch.full((3,), torch.nan, dtype=torch.float64),
+                torch.tensor([1e-3, 1e-4, 1e-5], dtype=torch.float64),
+                torch.tensor([1e-2, 1e-3, 1e-4], dtype=torch.float64),
+            ],
+            axis=0,
+        ),
+        expected_image_classes=torch.tensor([0, 1, 1], dtype=torch.int32),
+    )
+
+    # different bounds with more thresholds
+    metric = pimo.PIMO(fpr_bounds=(1e-5, 1e-2), num_thresholds=7)
+    metric.update(anomaly_maps, masks)
+    pimo_result_diff_bounds: PIMOResult = metric.compute()
+    _assert_pimo_result_close_to_expected(
+        thresholds=pimo_result_diff_bounds.thresholds,
+        shared_fpr=pimo_result_diff_bounds.shared_fpr,
+        per_image_tprs=pimo_result_diff_bounds.per_image_tprs,
+        image_classes=pimo_result_diff_bounds.image_classes,
+        expected_thresholds=torch.tensor([3, 3.5, 4, 4.5, 5, 5.5, 6], dtype=torch.float32),
+        expected_shared_fpr=torch.tensor([1e-2, 1e-3, 1e-3, 1e-4, 1e-4, 1e-5, 1e-5], dtype=torch.float64),
+        expected_per_image_tprs=torch.stack(
+            [
+                torch.full((7,), torch.nan, dtype=torch.float64),
+                torch.tensor([1e-2, 1e-3, 1e-3, 1e-4, 1e-4, 1e-5, 1e-5], dtype=torch.float64),
+                torch.tensor([1e-1, 1e-2, 1e-2, 1e-3, 1e-3, 1e-4, 1e-4], dtype=torch.float64),
+            ],
+            axis=0,
+        ),
+        expected_image_classes=torch.tensor([0, 1, 1], dtype=torch.int32),
     )
-    assert isinstance(aupimos, Tensor)
-    assert isinstance(expected_aupimos, Tensor)
-    allclose = torch.allclose
-    assert tuple(aupimos.shape) == (3,)
-    assert allclose(aupimos, expected_aupimos, equal_nan=True)
 
 
-def test_aupimo_values(
+def test_aupimo(
     anomaly_maps: torch.Tensor,
     masks: torch.Tensor,
     fpr_bounds: tuple[float, float],
-    expected_thresholds: torch.Tensor,
-    expected_shared_fpr: torch.Tensor,
-    expected_per_image_tprs: torch.Tensor,
-    expected_image_classes: torch.Tensor,
     expected_aupimos: torch.Tensor,
 ) -> None:
     """Test if `aupimo()` returns the expected values."""
-
-    def do_assertions(pimo_result: PIMOResult, aupimo_result: AUPIMOResult) -> None:
-        # test metadata
-        assert aupimo_result.fpr_bounds == fpr_bounds
-        # recall: this one is not the same as the number of thresholds in the curve
-        # this is the number of thresholds used to compute the integral in `aupimo()`
-        # always less because of the integration bounds
-        assert aupimo_result.num_thresholds < 7
-
-        # test data
-        # from pimo result
-        thresholds = pimo_result.thresholds
-        shared_fpr = pimo_result.shared_fpr
-        per_image_tprs = pimo_result.per_image_tprs
-        image_classes = pimo_result.image_classes
-        # from aupimo result
-        aupimos = aupimo_result.aupimos
-        _do_test_aupimo_outputs(
-            thresholds,
-            shared_fpr,
-            per_image_tprs,
-            image_classes,
-            aupimos,
-            expected_thresholds,
-            expected_shared_fpr,
-            expected_per_image_tprs,
-            expected_image_classes,
-            expected_aupimos,
-        )
-        thresh_lower_bound = aupimo_result.thresh_lower_bound
-        thresh_upper_bound = aupimo_result.thresh_upper_bound
-        assert anomaly_maps.min() <= thresh_lower_bound < thresh_upper_bound <= anomaly_maps.max()
-
     # metric interface
     metric = pimo.AUPIMO(
         num_thresholds=7,
@@ -313,8 +281,9 @@ def do_assertions(pimo_result: PIMOResult, aupimo_result: AUPIMOResult) -> None:
         force=True,
     )
     metric.update(anomaly_maps, masks)
-    pimo_result_from_metric, aupimo_result_from_metric = metric.compute()
-    do_assertions(pimo_result_from_metric, aupimo_result_from_metric)
+    aupimo_result: AUPIMOResult
+    _, aupimo_result = metric.compute()
+    torch.allclose(aupimo_result.aupimos, expected_aupimos, equal_nan=True)
 
     # metric interface
     metric = pimo.AUPIMO(
@@ -324,45 +293,31 @@ def do_assertions(pimo_result: PIMOResult, aupimo_result: AUPIMOResult) -> None:
         force=True,
     )
     metric.update(anomaly_maps, masks)
-    metric.compute()
+    average_aupimo = metric.compute()
+    assert torch.allclose(average_aupimo, expected_aupimos[~torch.isnan(expected_aupimos)].mean(), equal_nan=True)
 
 
 def test_aupimo_edge(
     anomaly_maps: torch.Tensor,
     masks: torch.Tensor,
-    fpr_bounds: tuple[float, float],
     caplog: pytest.LogCaptureFixture,
 ) -> None:
     """Test some edge cases."""
-    # None is the case of testing the default bounds
-    fpr_bounds = {"fpr_bounds": fpr_bounds} if fpr_bounds is not None else {}
-
     # not enough points on the curve
-    # 10 thresholds / 6 decades = 1.6 thresholds per decade < 3
-    with pytest.raises(RuntimeError):  # force=False --> raise error
+    # force=False --> raise error
+    with pytest.raises(RuntimeError):
         functional.aupimo_scores(
             anomaly_maps,
             masks,
             num_thresholds=10,
             force=False,
-            **fpr_bounds,
         )
-
-    with caplog.at_level(logging.WARNING):  # force=True --> warn
+    # force=True --> warn and compute anyway
+    with caplog.at_level(logging.WARNING):
         functional.aupimo_scores(
             anomaly_maps,
             masks,
             num_thresholds=10,
             force=True,
-            **fpr_bounds,
         )
     assert "Computation was forced!" in caplog.text
-
-    # default number of points on the curve (300k thresholds) should be enough
-    torch.manual_seed(42)
-    functional.aupimo_scores(
-        anomaly_maps * torch.FloatTensor(anomaly_maps.shape).uniform_(1.0, 1.1),
-        masks,
-        force=False,
-        **fpr_bounds,
-    )