update RF-DETR fine-tuning notebook

Author: SkalskiP

notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb

@@ -1088,10 +1088,10 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Run Inference with Fine-tuned RF-DETR Model"
+        "## Evaluate Fine-tuned RF-DETR Model"
       ],
       "metadata": {
-        "id": "xT1tPwZS_-6t"
+        "id": "X_9c113E39QP"
       }
     },
     {
@@ -1213,6 +1213,80 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "import supervision as sv\n",
+        "from tqdm import tqdm\n",
+        "from supervision.metrics import MeanAveragePrecision\n",
+        "\n",
+        "targets = []\n",
+        "predictions = []\n",
+        "\n",
+        "for path, image, annotations in tqdm(ds):\n",
+        "    image = Image.open(path)\n",
+        "    detections = model.predict(image, threshold=0)\n",
+        "\n",
+        "    targets.append(annotations)\n",
+        "    predictions.append(detections)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "szxs3PZsBVxa",
+        "outputId": "59c4660d-8e15-41a3-c842-a5b98bb022ea"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 169/169 [00:03<00:00, 44.91it/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "map_metric = MeanAveragePrecision()\n",
+        "map_result = map_metric.update(predictions, targets).compute()\n",
+        "print(map_result)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fxqvXOQcsRF2",
+        "outputId": "df467d54-e4ad-4b40-b65f-46bb6cfc5ca7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Average Precision (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.568\n",
+            "Average Precision (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.908\n",
+            "Average Precision (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.580\n",
+            "Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.321\n",
+            "Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ]                 = 0.758\n",
+            "Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ]                 = 0.809\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Run Inference with Fine-tuned RF-DETR Model"
+      ],
+      "metadata": {
+        "id": "xT1tPwZS_-6t"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1345,80 +1419,6 @@
         }
       ]
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Evaluate Fine-tuned RF-DETR Model"
-      ],
-      "metadata": {
-        "id": "X_9c113E39QP"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import supervision as sv\n",
-        "from tqdm import tqdm\n",
-        "from supervision.metrics import MeanAveragePrecision\n",
-        "\n",
-        "targets = []\n",
-        "predictions = []\n",
-        "\n",
-        "for path, image, annotations in tqdm(ds):\n",
-        "    image = Image.open(path)\n",
-        "    detections = model.predict(image, threshold=0)\n",
-        "\n",
-        "    targets.append(annotations)\n",
-        "    predictions.append(detections)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "szxs3PZsBVxa",
-        "outputId": "59c4660d-8e15-41a3-c842-a5b98bb022ea"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "100%|██████████| 169/169 [00:03<00:00, 44.91it/s]\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "map_metric = MeanAveragePrecision()\n",
-        "map_result = map_metric.update(predictions, targets).compute()\n",
-        "print(map_result)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fxqvXOQcsRF2",
-        "outputId": "df467d54-e4ad-4b40-b65f-46bb6cfc5ca7"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Average Precision (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.568\n",
-            "Average Precision (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.908\n",
-            "Average Precision (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.580\n",
-            "Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.321\n",
-            "Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ]                 = 0.758\n",
-            "Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ]                 = 0.809\n"
-          ]
-        }
-      ]
-    },
     {
       "cell_type": "markdown",
       "source": [