Creado con Colaboratory

tutorial.ipynb

@@ -661,7 +661,7 @@
         "id": "eyTZYGgRjnMc"
       },
       "source": [
-        "### 2.1 COCO val2017\n",
+        "## COCO val2017\n",
         "Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."
       ]
     },
@@ -786,7 +786,7 @@
         "id": "rc_KbFk0juX2"
       },
       "source": [
-        "### 2.2 COCO test-dev2017\n",
+        "## COCO test-dev2017\n",
         "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (20,000 images). Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
       ]
     },
@@ -996,15 +996,22 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "15glLzbQx5u0"
+      },
+      "source": [
+        "# 4. Visualize"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "DLI1JmHU7B0l"
       },
       "source": [
-        "# 4. Visualize\n",
-        "\n",
-        "## 4.1 Weights & Biases Logging (🚀 NEW)\n",
+        "## Weights & Biases Logging (🚀 NEW)\n",
         "\n",
         "[Weights & Biases](https://www.wandb.com/) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration among team members. To enable W&B logging install `wandb`, and then train normally (you will be guided setup on first use).\n",
         "```bash\n",
@@ -1022,7 +1029,7 @@
         "id": "-WPvRbS5Swl6"
       },
       "source": [
-        "## 4.2 Local Logging\n",
+        "## Local Logging\n",
         "\n",
         "All results are logged by default to the `runs/exp0` directory, with a new directory created for each new training as `runs/exp1`, `runs/exp2`, etc. View train and test jpgs to see mosaics, labels/predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
       ]
@@ -1093,7 +1100,7 @@
         "id": "Zelyeqbyt3GD"
       },
       "source": [
-        "## Environments\n",
+        "# Environments\n",
         "\n",
         "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
         "\n",