Copia_de_YOLOv5_Classification_ultimo_casi_funka_mio.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/danielsoy/ALOCC-CVPR2018/blob/master/Copia_de_YOLOv5_Classification_ultimo_casi_funka_mio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# YOLOv5 Classification Tutorial\n",
        "\n",
        "YOLOv5 supports classification tasks too. This is the official YOLOv5 classification notebook tutorial. YOLOv5 is maintained by [Ultralytics](https://github.com/ultralytics/yolov5).\n",
        "\n",
        "This notebook covers:\n",
        "\n",
        "*   Inference with out-of-the-box YOLOv5 classification on ImageNet\n",
        "*  [Training YOLOv5 classification](https://blog.roboflow.com//train-YOLOv5-classification-custom-data) on custom data\n",
        "\n",
        "*Looking for custom data? Explore over 66M community datasets on [Roboflow Universe](https://universe.roboflow.com).*\n",
        "\n",
        "This notebook was created with Google Colab. [Click here](https://colab.research.google.com/drive/1FiSNz9f_nT8aFtDEU3iDAQKlPT8SCVni?usp=sharing) to run it."
      ],
      "metadata": {
        "id": "5GYQX3of4QiW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup\n",
        "\n",
        "Pull in respective libraries to prepare the notebook environment."
      ],
      "metadata": {
        "id": "-PJ8vlYXbWtN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pIM7fOwm8A7l",
        "outputId": "ce313655-17ad-4e00-89db-4a0927a25a13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.6/78.2 GB disk)\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt  # install\n",
        "\n",
        "import torch\n",
        "import utils\n",
        "display = utils.notebook_init()  # checks"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Infer on ImageNet\n",
        "\n",
        "To demonstrate YOLOv5 classification, we'll leverage an already trained model. In this case, we'll download the ImageNet trained models pretrained on ImageNet using YOLOv5 Utils."
      ],
      "metadata": {
        "id": "i_DrUi2nmF40"
      }
    },
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        "from utils.downloads import attempt_download\n",
        "\n",
        "p5 = ['n', 's', 'm', 'l', 'x']  # P5 models\n",
        "cls = [f'{x}-cls' for x in p5]  # classification models\n",
        "\n",
        "for x in cls:\n",
        "    attempt_download(f'weights/yolov5{x}.pt')"
      ],
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        {
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            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt to weights/yolov5n-cls.pt...\n"
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              "model_id": "5f68e022ce824d51a11049c1423acd02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt to weights/yolov5l-cls.pt...\n"
          ]
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt to weights/yolov5x-cls.pt...\n"
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            }
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          "metadata": {}
        },
        {
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          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can infer on an example image from the ImageNet dataset."
      ],
      "metadata": {
        "id": "Fn2_a38DmZ2H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Download example image\n",
        "import requests\n",
        "image_url = \"https://i.imgur.com/OczPfaz.jpg\"\n",
        "img_data = requests.get(image_url).content\n",
        "with open('bananas.jpg', 'wb') as handler:\n",
        "    handler.write(img_data)"
      ],
      "metadata": {
        "id": "L9objhVHnS-h"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Infer using classify/predict.py\n",
        "!python classify/predict.py --weights ./weigths/yolov5s-cls.pt --source bananas.jpg\n",
        "\n"
      ],
      "metadata": {
        "id": "qqxF5pHCrLd3",
        "outputId": "3a7c81e0-364d-4922-deec-bb6a47b84fbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['./weigths/yolov5s-cls.pt'], source=bananas.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to weigths/yolov5s-cls.pt...\n",
            "100% 10.5M/10.5M [00:00<00:00, 254MB/s]\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
            "image 1/1 /content/yolov5/bananas.jpg: 224x224 banana 0.96, zucchini 0.00, acorn squash 0.00, spaghetti squash 0.00, green mamba 0.00, 4.1ms\n",
            "Speed: 0.4ms pre-process, 4.1ms inference, 2.3ms NMS per image at shape (1, 3, 224, 224)\n",
            "Results saved to \u001b[1mruns/predict-cls/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Infer using classify/predict.py\n",
        "!python classify/predict.py --weights ./weigths/yolov5s-cls.pt --source bananas.jpg\n",
        "\n"
      ],
      "metadata": {
        "outputId": "542dcc0c-96d9-45d3-be5f-4b67d5168c99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PyI5fQyzLZJ"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['./weigths/yolov5s-cls.pt'], source=bananas.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
            "image 1/1 /content/yolov5/bananas.jpg: 224x224 banana 0.96, zucchini 0.00, acorn squash 0.00, spaghetti squash 0.00, green mamba 0.00, 4.6ms\n",
            "Speed: 0.3ms pre-process, 4.6ms inference, 0.1ms NMS per image at shape (1, 3, 224, 224)\n",
            "Results saved to \u001b[1mruns/predict-cls/exp2\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Infer using classify/predict.py\n",
        "#!python classify/predict.py --weights ./weigths/yolov5s-cls.pt --source bananas.jpg\n",
        "\n"
      ],
      "metadata": {
        "id": "tXdlpkvDzQiY"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Infer using classify/predict.py\n",
        "# !python classify/predict.py --weights ./weigths/yolov5s-cls.pt --source bananas.jpg\n",
        "\n",
        "!python classify/predict.py --weights ./weigths/yolov5s-cls.pt --source /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/moño.jpg"
      ],
      "metadata": {
        "id": "XSnAoxajzXTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the output, we can see the ImageNet trained model correctly predicts the class `banana` with `0.95` confidence."
      ],
      "metadata": {
        "id": "yQmj7IXqo3kk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. (Optional) Validate\n",
        "\n",
        "Use the `classify/val.py` script to run validation for the model. This will show us the model's performance on each class.\n",
        "\n",
        "First, we need to download ImageNet."
      ],
      "metadata": {
        "id": "5EosQzyDCk3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # WARNING: takes ~20 minutes\n",
        "# !bash data/scripts/get_imagenet.sh --val"
      ],
      "metadata": {
        "id": "HwAYptjCq-C_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # run the validation script\n",
        "# !python classify/val.py --weights ./weigths/yolov5s-cls.pt --data ../datasets/imagenet"
      ],
      "metadata": {
        "id": "CoHdKXWc8hrD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The output shows accuracy metrics for the ImageNet validation dataset including per class accuracy."
      ],
      "metadata": {
        "id": "r2coOcIjuzCO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Train On Custom Data\n",
        "\n",
        "To train on custom data, we need to prepare a dataset with custom labels.\n",
        "\n",
        "To prepare custom data, we'll use [Roboflow](https://roboflow.com). Roboflow enables easy dataset prep with your team, including labeling, formatting into the right export format, deploying, and active learning with a `pip` package. \n",
        "\n",
        "If you need custom data, there are over 66M open source images from the community on [Roboflow Universe](https://universe.roboflow.com).\n",
        "\n",
        "(For more guidance, here's a detailed blog on [training YOLOv5 classification on custom data](https://blog.roboflow.com/train-YOLOv5-classification-custom-data).)\n",
        "\n",
        "\n",
        "Create a free Roboflow account, upload your data, and label. \n",
        "\n",
        "![](https://s4.gifyu.com/images/fruit-labeling.gif)"
      ],
      "metadata": {
        "id": "9bXHHYeVDCXg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Custom Dataset\n",
        "\n",
        "Next, we'll export our dataset into the right directory structure for training YOLOv5 classification to load into this notebook. Select the `Export` button at the top of the version page, `Folder Structure` type, and `show download code`.\n",
        "\n",
        "The ensures all our directories are in the right format:\n",
        "\n",
        "```\n",
        "dataset\n",
        "├── train\n",
        "│   ├── class-one\n",
        "│   │   ├── IMG_123.jpg\n",
        "│   └── class-two\n",
        "│       ├── IMG_456.jpg\n",
        "├── valid\n",
        "│   ├── class-one\n",
        "│   │   ├── IMG_789.jpg\n",
        "│   └── class-two\n",
        "│       ├── IMG_101.jpg\n",
        "├── test\n",
        "│   ├── class-one\n",
        "│   │   ├── IMG_121.jpg\n",
        "│   └── class-two\n",
        "│       ├── IMG_341.jpg\n",
        "```\n",
        "\n",
        "![](https://i.imgur.com/BF9BNR8.gif)\n",
        "\n",
        "\n",
        "Copy and paste that snippet into the cell below."
      ],
      "metadata": {
        "id": "Cu6-lrukD6Hc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure we're in the right directory to download our custom dataset\n",
        "import os\n",
        "os.makedirs(\"../datasets/\", exist_ok=True)\n",
        "%cd ../datasets/"
      ],
      "metadata": {
        "id": "6IIgJbP7G6Th",
        "outputId": "555a27ab-7ed2-4701-b59c-37a1c356a6a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/datasets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# REPLACE the below with your exported code snippet from above\n",
        "\n",
        "\n",
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"9ORhusQxYFimZzVYfjdC\")\n",
        "project = rf.workspace(\"new-workspace-lzcyx\").project(\"bow-pasta-yolo_v5\")\n",
        "dataset = project.version(1).download(\"folder\")\n"
      ],
      "metadata": {
        "id": "He6JwHIlG-W_",
        "outputId": "e995bab7-0b62-45af-debc-056ffdb2d127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: wget, urllib3, python-dotenv, pyparsing, cycler, requests-toolbelt, roboflow\n",
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            "      Successfully uninstalled pyparsing-3.0.9\n",
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            "    Uninstalling cycler-0.11.0:\n",
            "      Successfully uninstalled cycler-0.11.0\n",
            "Successfully installed cycler-0.10.0 pyparsing-2.4.7 python-dotenv-0.21.1 requests-toolbelt-0.10.1 roboflow-0.2.29 urllib3-1.26.14 wget-3.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cycler",
                  "pyparsing",
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in bow-pasta-yolo_v5-1 to folder: 100% [2708925 / 2708925] bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to bow-pasta-yolo_v5-1 in folder:: 100%|██████████| 335/335 [00:00<00:00, 1544.08it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Save the dataset name to the environment so we can use it in a system call later\n",
        "dataset_name = dataset.location.split(os.sep)[-1]\n",
        "os.environ[\"/content/datasets\"] = dataset_name"
      ],
      "metadata": {
        "id": "wLQbThFICpn4"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train On Custom Data 🎉\n",
        "Here, we use the DATASET_NAME environment variable to pass our dataset to the `--data` parameter.\n",
        "\n",
        "Note: we're training for 100 epochs here. We're also starting training from the pretrained weights. Larger datasets will likely benefit from longer training. "
      ],
      "metadata": {
        "id": "-5z7Yv42FGrK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ../yolov5\n",
        "!python classify/train.py --model yolov5s-cls.pt --data /content/datasets/bow-pasta-yolo_v5-1  --epochs 100 --img 128 --pretrained weights/yolov5s-cls.pt"
      ],
      "metadata": {
        "id": "MXWTTN2BEaqe",
        "outputId": "29736525-553c-42dc-aba8-a3bbdf2de5f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n",
            "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=/content/datasets/bow-pasta-yolo_v5-1, epochs=100, batch_size=64, imgsz=128, nosave=False, cache=None, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=weights/yolov5s-cls.pt, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=128, width=128, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n",
            "100% 10.5M/10.5M [00:00<00:00, 83.3MB/s]\n",
            "\n",
            "Model summary: 149 layers, 4175042 parameters, 4175042 gradients, 10.5 GFLOPs\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n",
            "Image sizes 128 train, 128 test\n",
            "Using 1 dataloader workers\n",
            "Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n",
            "Starting yolov5s-cls.pt training on /content/datasets/bow-pasta-yolo_v5-1 dataset with 2 classes for 100 epochs...\n",
            "\n",
            "     Epoch   GPU_mem  train_loss   test_loss    top1_acc    top5_acc\n",
            "     1/100    0.438G        1.08       0.697       0.469           1: 100% 4/4 [00:01<00:00,  2.78it/s]\n",
            "     2/100    0.438G       0.788       0.699       0.469           1: 100% 4/4 [00:00<00:00,  6.38it/s]\n",
            "     3/100    0.438G       0.687       0.701       0.469           1: 100% 4/4 [00:00<00:00,  7.22it/s]\n",
            "     4/100    0.438G        0.69       0.697       0.469           1: 100% 4/4 [00:00<00:00,  7.00it/s]\n",
            "     5/100    0.438G       0.684       0.694       0.469           1: 100% 4/4 [00:00<00:00,  6.38it/s]\n",
            "     6/100    0.438G       0.685       0.696       0.469           1: 100% 4/4 [00:00<00:00,  6.93it/s]\n",
            "     7/100    0.438G       0.675       0.697       0.469           1: 100% 4/4 [00:00<00:00,  7.10it/s]\n",
            "     8/100    0.438G        0.69       0.692       0.531           1: 100% 4/4 [00:00<00:00,  6.71it/s]\n",
            "     9/100    0.438G        0.67       0.691       0.531           1: 100% 4/4 [00:00<00:00,  6.30it/s]\n",
            "    10/100    0.438G       0.679       0.703       0.469           1: 100% 4/4 [00:00<00:00,  6.69it/s]\n",
            "    11/100    0.438G       0.671       0.741       0.469           1: 100% 4/4 [00:00<00:00,  5.74it/s]\n",
            "    12/100    0.438G       0.666       0.712       0.469           1: 100% 4/4 [00:00<00:00,  6.05it/s]\n",
            "    13/100    0.438G       0.611       0.689       0.531           1: 100% 4/4 [00:00<00:00,  5.73it/s]\n",
            "    14/100    0.438G       0.662       0.696       0.531           1: 100% 4/4 [00:00<00:00,  6.96it/s]\n",
            "    15/100    0.438G       0.666       0.731       0.531           1: 100% 4/4 [00:00<00:00,  6.58it/s]\n",
            "    16/100    0.438G        0.67       0.693       0.531           1: 100% 4/4 [00:00<00:00,  6.71it/s]\n",
            "    17/100    0.438G       0.642       0.696       0.469           1: 100% 4/4 [00:00<00:00,  7.53it/s]\n",
            "    18/100    0.438G       0.644       0.691       0.531           1: 100% 4/4 [00:00<00:00,  6.45it/s]\n",
            "    19/100    0.438G       0.651       0.848       0.531           1: 100% 4/4 [00:00<00:00,  6.23it/s]\n",
            "    20/100    0.438G       0.613       0.997       0.531           1: 100% 4/4 [00:00<00:00,  6.37it/s]\n",
            "    21/100    0.438G       0.627       0.866       0.531           1: 100% 4/4 [00:00<00:00,  6.52it/s]\n",
            "    22/100    0.438G       0.653       0.768       0.531           1: 100% 4/4 [00:00<00:00,  6.36it/s]\n",
            "    23/100    0.438G       0.639       0.721       0.531           1: 100% 4/4 [00:00<00:00,  6.54it/s]\n",
            "    24/100    0.438G       0.633       0.735       0.531           1: 100% 4/4 [00:00<00:00,  6.64it/s]\n",
            "    25/100    0.438G       0.624       0.793       0.531           1: 100% 4/4 [00:00<00:00,  6.84it/s]\n",
            "    26/100    0.438G       0.605       0.863       0.531           1: 100% 4/4 [00:00<00:00,  6.83it/s]\n",
            "    27/100    0.438G       0.615        1.02       0.531           1: 100% 4/4 [00:00<00:00,  6.38it/s]\n",
            "    28/100    0.438G       0.611       0.723       0.531           1: 100% 4/4 [00:00<00:00,  6.72it/s]\n",
            "    29/100    0.438G       0.629       0.526       0.875           1: 100% 4/4 [00:00<00:00,  6.84it/s]\n",
            "    30/100    0.438G       0.603       0.676         0.5           1: 100% 4/4 [00:00<00:00,  6.97it/s]\n",
            "    31/100    0.438G       0.611       0.535       0.844           1: 100% 4/4 [00:00<00:00,  6.28it/s]\n",
            "    32/100    0.438G       0.606       0.417       0.906           1: 100% 4/4 [00:00<00:00,  5.68it/s]\n",
            "    33/100    0.438G       0.619       0.437       0.875           1: 100% 4/4 [00:00<00:00,  6.47it/s]\n",
            "    34/100    0.438G         0.6       0.724       0.625           1: 100% 4/4 [00:00<00:00,  6.20it/s]\n",
            "    35/100    0.438G         0.6       0.676       0.531           1: 100% 4/4 [00:00<00:00,  6.91it/s]\n",
            "    36/100    0.438G       0.598       0.645       0.531           1: 100% 4/4 [00:00<00:00,  6.43it/s]\n",
            "    37/100    0.438G       0.604       0.657       0.531           1: 100% 4/4 [00:00<00:00,  6.47it/s]\n",
            "    38/100    0.438G        0.55       0.694       0.531           1: 100% 4/4 [00:00<00:00,  6.89it/s]\n",
            "    39/100    0.438G       0.603       0.733       0.531           1: 100% 4/4 [00:00<00:00,  5.86it/s]\n",
            "    40/100    0.438G        0.58       0.589       0.688           1: 100% 4/4 [00:00<00:00,  6.03it/s]\n",
            "    41/100    0.438G        0.58       0.614       0.625           1: 100% 4/4 [00:00<00:00,  6.71it/s]\n",
            "    42/100    0.438G       0.576       0.631       0.562           1: 100% 4/4 [00:00<00:00,  6.80it/s]\n",
            "    43/100    0.438G       0.594       0.554       0.688           1: 100% 4/4 [00:00<00:00,  6.27it/s]\n",
            "    44/100    0.438G       0.556                                    :  75% 3/4 [00:00<00:00,  4.75it/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"classify/train.py\", line 333, in <module>\n",
            "    main(opt)\n",
            "  File \"classify/train.py\", line 319, in main\n",
            "    train(opt, device)\n",
            "  File \"classify/train.py\", line 206, in train\n",
            "    top1, top5, vloss = validate.run(model=ema.ema,\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/content/yolov5/classify/val.py\", line 106, in run\n",
            "    for images, labels in bar:\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tqdm/std.py\", line 1195, in __iter__\n",
            "    for obj in iterable:\n",
            "  File \"/content/yolov5/utils/dataloaders.py\", line 172, in __iter__\n",
            "    yield next(self.iterator)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\", line 628, in __next__\n",
            "    data = self._next_data()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\", line 1333, in _next_data\n",
            "    return self._process_data(data)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\", line 1359, in _process_data\n",
            "    data.reraise()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/_utils.py\", line 543, in reraise\n",
            "    raise exception\n",
            "FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.\n",
            "Original Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/worker.py\", line 302, in _worker_loop\n",
            "    data = fetcher.fetch(index)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\", line 58, in fetch\n",
            "    data = [self.dataset[idx] for idx in possibly_batched_index]\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\", line 58, in <listcomp>\n",
            "    data = [self.dataset[idx] for idx in possibly_batched_index]\n",
            "  File \"/content/yolov5/utils/dataloaders.py\", line 1189, in __getitem__\n",
            "    im = cv2.imread(f)  # BGR\n",
            "  File \"/content/yolov5/utils/general.py\", line 1118, in imread\n",
            "    return cv2.imdecode(np.fromfile(path, np.uint8), flags)\n",
            "FileNotFoundError: [Errno 2] No such file or directory: '/content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-115-_PNG.rf.f424748c205fb520a2cc533575ab3c20.jpg'\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Validate Your Custom Model\n",
        "\n",
        "Repeat step 2 from above to test and validate your custom model."
      ],
      "metadata": {
        "id": "HHUFGeLbGd98"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data ../datasets/$DATASET_NAME"
      ],
      "metadata": {
        "id": "DIV7ydyKGZFL",
        "outputId": "4fef4d45-e2db-4341-d8f6-5df5fd0be7f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/, weights=['runs/train-cls/exp/weights/best.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 117 layers, 4169250 parameters, 0 gradients, 10.4 GFLOPs\n",
            "Traceback (most recent call last):\n",
            "  File \"classify/val.py\", line 170, in <module>\n",
            "    main(opt)\n",
            "  File \"classify/val.py\", line 165, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"classify/val.py\", line 92, in run\n",
            "    dataloader = create_classification_dataloader(path=test_dir,\n",
            "  File \"/content/yolov5/utils/dataloaders.py\", line 1207, in create_classification_dataloader\n",
            "    dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)\n",
            "  File \"/content/yolov5/utils/dataloaders.py\", line 1173, in __init__\n",
            "    super().__init__(root=root)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torchvision/datasets/folder.py\", line 309, in __init__\n",
            "    super().__init__(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torchvision/datasets/folder.py\", line 144, in __init__\n",
            "    classes, class_to_idx = self.find_classes(self.root)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torchvision/datasets/folder.py\", line 218, in find_classes\n",
            "    return find_classes(directory)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torchvision/datasets/folder.py\", line 40, in find_classes\n",
            "    classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())\n",
            "FileNotFoundError: [Errno 2] No such file or directory: '../datasets/val'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Infer With Your Custom Model"
      ],
      "metadata": {
        "id": "uH5tJNpEsi6g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the path of an image from the test or validation set\n",
        "if os.path.exists(os.path.join(dataset.location, \"test\")):\n",
        "  split_path = os.path.join(dataset.location, \"test\")\n",
        "else:\n",
        "  os.path.join(dataset.location, \"valid\")\n",
        "example_class = os.listdir(split_path)[0]\n",
        "example_image_name = os.listdir(os.path.join(split_path, example_class))[0]\n",
        "example_image_path = os.path.join(split_path, example_class, example_image_name)\n",
        "os.environ[\"TEST_IMAGE_PATH\"] = example_image_path\n",
        "# colocar direccion de la foto a inferir dentro de comillas\n",
        "\n",
        "print(f\"Inferring on an example of the class '{example_class}'\")\n",
        "\n",
        "#Infer\n",
        "!python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source $TEST_IMAGE_PATH"
      ],
      "metadata": {
        "id": "81lK1hU_sk54"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see the inference results show ~3ms inference and the respective classes predicted probabilities."
      ],
      "metadata": {
        "id": "DdGuG-1kNjWT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (OPTIONAL) Improve Our Model with Active Learning\n",
        "\n",
        "Now that we've trained our model once, we will want to continue to improve its performance. Improvement is largely dependent on improving our dataset.\n",
        "\n",
        "We can programmatically upload example failure images back to our custom dataset based on conditions (like seeing an underrpresented class or a low confidence score) using the same `pip` package."
      ],
      "metadata": {
        "id": "I38IM6NXKNN9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Upload example image\n",
        "# project.upload(image_path)\n"
      ],
      "metadata": {
        "id": "HycgSEnYKo0J"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Example upload code \n",
        "# min_conf = float(\"inf\")\n",
        "# for pred in results:\n",
        "#     if pred[\"score\"] < min_conf:\n",
        "#         min_conf = pred[\"score\"]\n",
        "# if min_conf < 0.4:\n",
        "#     project.upload(image_path)"
      ],
      "metadata": {
        "id": "VwXDoz_vLK3V"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (BONUS) YOLOv5 classify/predict.py Accepts Several Input Methods\n",
        "- Webcam: `python classify/predict.py --weights yolov5s-cls.pt --source 0`\n",
        "- Image `python classify/predict.py --weights yolov5s-cls.pt --source img.jpg`\n",
        "- Video: `python classify/predict.py --weights yolov5s-cls.pt --source vid.mp4`\n",
        "- Directory: `python classify/predict.py --weights yolov5s-cls.pt --source path/`\n",
        "- Glob: `python classify/predict.py --weights yolov5s-cls.pt --source 'path/*.jpg'`\n",
        "- YouTube: `python classify/predict.py --weights yolov5s-cls.pt --source 'https://youtu.be/Zgi9g1ksQHc'`\n",
        "- RTSP, RTMP, HTTP stream: `python classify/predict.py --weights yolov5s-cls.pt --source 'rtsp://example.com/media.mp4'`"
      ],
      "metadata": {
        "id": "aYlfaHDusN-j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Directory Example"
      ],
      "metadata": {
        "id": "iKSP-SNTvcLJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# la proxima celda falla, no hay modelo:FileNotFoundError: [Errno 2] No such file or directory: 'runs/train-cls/exp/weights/best.pt'"
      ],
      "metadata": {
        "id": "uwP-sHuItlFh"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9pDwLHfctuRS"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Directory infer\n",
        "os.environ[\"TEST_CLASS_PATH\"] = test_class_path = os.path.join(*os.environ[\"TEST_IMAGE_PATH\"].split(os.sep)[:-1])\n",
        "print(f\"Infering on all images from the directory {os.environ['TEST_CLASS_PATH']}\")\n",
        "!python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source /$TEST_CLASS_PATH/"
      ],
      "metadata": {
        "id": "lwSoHcHcvjeD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3678b6eb-ca88-4d28-fb94-17dc78fc3710"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Infering on all images from the directory content/datasets/bow-pasta-yolo_v5-1/test/red-tomato\n",
            "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['runs/train-cls/exp/weights/best.pt'], source=/content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 117 layers, 4169250 parameters, 0 gradients, 10.4 GFLOPs\n",
            "image 1/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/moño.jpg: 224x224 red-tomato 0.57, yellow-tomato 0.43, 3.9ms\n",
            "image 2/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-124-_PNG.rf.ceba7acc194ee642af923e653d01c699.jpg: 224x224 red-tomato 0.77, yellow-tomato 0.23, 3.9ms\n",
            "image 3/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-135-_PNG.rf.a4a364d444c2ee9331c7b7e1f66f06ef.jpg: 224x224 yellow-tomato 0.60, red-tomato 0.40, 4.2ms\n",
            "image 4/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-146-_PNG.rf.17534ac1503de75329405cf091bcb60c.jpg: 224x224 yellow-tomato 0.51, red-tomato 0.49, 4.3ms\n",
            "image 5/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-155-_PNG.rf.2cb861fbc6c47e3997fb52d801a2781d.jpg: 224x224 red-tomato 0.76, yellow-tomato 0.24, 4.3ms\n",
            "image 6/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-16-_PNG.rf.72557abf7f4ba1e28ecd8a3eeba0da75.jpg: 224x224 red-tomato 0.63, yellow-tomato 0.37, 3.9ms\n",
            "image 7/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-19-_PNG.rf.20225f1dd3a272b1357fc76c76ba60f6.jpg: 224x224 red-tomato 0.57, yellow-tomato 0.43, 3.9ms\n",
            "image 8/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-22-_PNG.rf.47c23f7661e26a6be031aa090c2981e0.jpg: 224x224 red-tomato 0.78, yellow-tomato 0.22, 4.9ms\n",
            "image 9/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-33-_PNG.rf.efe56e2f80d7ed33bd0ba158897ad7a1.jpg: 224x224 red-tomato 0.70, yellow-tomato 0.30, 4.0ms\n",
            "image 10/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-45-_PNG.rf.2e9867f519802fb50c1de22b98201f48.jpg: 224x224 yellow-tomato 0.53, red-tomato 0.47, 3.8ms\n",
            "image 11/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-50-_PNG.rf.1ce6c61e5b100f9bb1270c3d5d9e0fc9.jpg: 224x224 red-tomato 0.61, yellow-tomato 0.39, 3.9ms\n",
            "image 12/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-54-_PNG.rf.d5e5b3024b0e9f0075520803f68039a8.jpg: 224x224 red-tomato 0.69, yellow-tomato 0.31, 3.9ms\n",
            "image 13/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-70-_PNG.rf.3e57edb7094c7128738c605b031da885.jpg: 224x224 red-tomato 0.81, yellow-tomato 0.19, 3.9ms\n",
            "image 14/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-85-_PNG.rf.8eb51a7f102451b2bd08109754a38c1e.jpg: 224x224 red-tomato 0.62, yellow-tomato 0.38, 4.0ms\n",
            "image 15/15 /content/datasets/bow-pasta-yolo_v5-1/test/red-tomato/ok-98-_PNG.rf.e483bba4682559e148e429043356caa4.jpg: 224x224 red-tomato 0.64, yellow-tomato 0.36, 3.9ms\n",
            "Speed: 0.3ms pre-process, 4.0ms inference, 0.0ms NMS per image at shape (1, 3, 224, 224)\n",
            "Results saved to \u001b[1mruns/predict-cls/exp5\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###YouTube Example"
      ],
      "metadata": {
        "id": "kCCao9t8se8i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#YouTube infer\n",
        "!python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source 'https://www.youtube.com/watch?v=7AlYA4ItA74'"
      ],
      "metadata": {
        "id": "heebjpJBsakV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "222c91ac-8d43-49a1-ca5f-71391bda820b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mSe han truncado las últimas 5000 líneas del flujo de salida.\u001b[0m\n",
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}