From 4fc55958b1360e893a5a4073c6f4b4589af9b2b6 Mon Sep 17 00:00:00 2001
From: shayaharon <shay.aharon@deci.ai>
Date: Mon, 13 Nov 2023 11:03:18 +0200
Subject: [PATCH] final touches

---
 notebooks/quickstart_segmentation.ipynb       |    1 -
 .../segmentation_connect_custom_dataset.ipynb | 1941 ++++++++---------
 ...nsfer_learning_semantic_segmentation.ipynb |    1 -
 3 files changed, 970 insertions(+), 973 deletions(-)

diff --git a/notebooks/quickstart_segmentation.ipynb b/notebooks/quickstart_segmentation.ipynb
index 63150b51c5..13798e9e3a 100644
--- a/notebooks/quickstart_segmentation.ipynb
+++ b/notebooks/quickstart_segmentation.ipynb
@@ -145,7 +145,6 @@
    ],
    "source": [
     "! pip install -qq super-gradients==3.4.1\n",
-    "\n",
     "! pip install -qq prettyformatter\n"
    ]
   },
diff --git a/notebooks/segmentation_connect_custom_dataset.ipynb b/notebooks/segmentation_connect_custom_dataset.ipynb
index 2e3b465b5b..1e2d55dad4 100644
--- a/notebooks/segmentation_connect_custom_dataset.ipynb
+++ b/notebooks/segmentation_connect_custom_dataset.ipynb
@@ -1,980 +1,979 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sh6t_y7KzqBH"
-      },
-      "source": [
-        "![SG - 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-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5aISf1B-AGDQ"
-      },
-      "source": [
-        "# SuperGradients Semantic Segmentation How to Connect Custom Dataset\n",
-        "\n",
-        "In this tutorial we will explore how you can connect your custom Semantic Segmentation dataset to SG.\n",
-        "\n",
-        "Since SG trainer is fully compatible with PyTorch data loaders, we will demonstrate how to build one and use it.\n",
-        "\n",
-        "The notebook is divided into 5 sections:\n",
-        "1. Experiment setup\n",
-        "2. Dataset definition: create a proxy dataset and create a dataloader\n",
-        "3. Architecture definition: pre-trained PPLiteSeg on Cityscapes  \n",
-        "4. Training setup\n",
-        "5. Training and Evaluation\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-1nPOPmc1lGp"
-      },
-      "source": [
-        "#Install SG"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VAssbjJw7Yt1"
-      },
-      "source": [
-        "The cell below will install **super_gradients** which will automatically get all its dependencies. Let's import all the installed libraries to make sure they installed succesfully."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "JKce1SM6voVH",
-        "outputId": "a6397510-a140-443f-f13c-eec1272cc1a8"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
-            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n",
-            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
-            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
-            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
-            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
-            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
-            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
-            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
-            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
-            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
-            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
-            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for pycocotools (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for termcolor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for treelib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for coverage (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for xhtml2pdf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for stringcase (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for svglib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "lida 0.0.10 requires fastapi, which is not installed.\n",
-            "lida 0.0.10 requires kaleido, which is not installed.\n",
-            "lida 0.0.10 requires python-multipart, which is not installed.\n",
-            "lida 0.0.10 requires uvicorn, which is not installed.\n",
-            "tensorflow 2.14.0 requires numpy>=1.23.5, but you have numpy 1.23.0 which is incompatible.\u001b[0m\u001b[31m\n",
-            "\u001b[0m"
-          ]
-        }
-      ],
-      "source": [
-        "! pip install torch torchvision torchaudio\n",
-        "! pip install -qq super-gradients==3.4.1\n",
-        "! pip install -qq prettyformatter"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "njthhNJR1pJm"
-      },
-      "source": [
-        "# 1. Experiment setup"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YPym4wvpOcOJ"
-      },
-      "source": [
-        "We will first initialize our **trainer** which will be in charge of everything, like training, evaluation, saving checkpoints, plotting etc.\n",
-        "\n",
-        "The **experiment name** argument is important as every checkpoints, logs and tensorboards to be saved in a directory with the same name. This directory will be created as a sub-directory of **ckpt_root_dir** as follow:\n",
-        "\n",
-        "```\n",
-        "ckpt_root_dir\n",
-        "|─── experiment_name_1\n",
-        "│       ckpt_best.pth                     # Model checkpoint on best epoch\n",
-        "│       ckpt_latest.pth                   # Model checkpoint on last epoch\n",
-        "│       average_model.pth                 # Model checkpoint averaged over epochs\n",
-        "│       events.out.tfevents.1659878383... # Tensorflow artifacts of a specific run\n",
-        "│       log_Aug07_11_52_48.txt            # Trainer logs of a specific run\n",
-        "└─── experiment_name_2\n",
-        "        ...\n",
-        "```\n",
-        "In this notebook multi-gpu training is set as `OFF`, for Distributed training multi_gpu can be set as\n",
-        " `MultiGPUMode.DISTRIBUTED_DATA_PARALLEL` or `MultiGPUMode.DATA_PARALLEL`.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A2PlnTWpimnH"
-      },
-      "source": [
-        "Let's define **ckpt_root_dir** inside the Colab, later we can use it to start TensorBoard and monitor the run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "_v1N3kXs3wo1"
-      },
-      "outputs": [],
-      "source": [
-        "from super_gradients.training import Trainer, MultiGPUMode\n",
-        "\n",
-        "\n",
-        "CHECKPOINT_DIR = '/home/notebook_ckpts/'\n",
-        "trainer = Trainer(experiment_name='transfer_learning_semantic_segementation_ppLite', ckpt_root_dir=CHECKPOINT_DIR)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "J9ZaMulSvwhr"
-      },
-      "source": [
-        "# 2. Dataset definition\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_1TXuJKkKzFJ"
-      },
-      "source": [
-        "## 2.A Generate Proxy Dataset"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Y7us7VHRig7M"
-      },
-      "source": [
-        "\n",
-        "A proxy dataset generation is available merely to demonstrate an end-to-end training pipeline in this notebook.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "wbdVYnIyjgv-"
-      },
-      "outputs": [],
-      "source": [
-        "from PIL import Image\n",
-        "import os\n",
-        "import numpy as np\n",
-        "\n",
-        "\n",
-        "# creation of proxy dataset to demonstrate usage\n",
-        "def generate_proxy_dataset(write_path: str, num_samples: int, num_classes: int, img_size: int = 256):\n",
-        "    # Create training files and text\n",
-        "    os.makedirs(os.path.join(write_path, 'images', 'train'), exist_ok=True)\n",
-        "    os.makedirs(os.path.join(write_path, 'images', 'val'), exist_ok=True)\n",
-        "    os.makedirs(os.path.join(write_path, 'labels', 'train'), exist_ok=True)\n",
-        "    os.makedirs(os.path.join(write_path, 'labels', 'val'), exist_ok=True)\n",
-        "\n",
-        "    train_fp = open(os.path.join(write_path, 'train.txt'), 'w')\n",
-        "    val_fp = open(os.path.join(write_path, 'val.txt'), 'w')\n",
-        "\n",
-        "    # Create random samples\n",
-        "    for n in range(num_samples):\n",
-        "        img = np.random.rand(img_size, img_size, 3) * 255\n",
-        "        img = Image.fromarray(img.astype('uint8')).convert('RGB')\n",
-        "\n",
-        "        lbl = np.random.randint(0, num_classes, size=(img_size, img_size))\n",
-        "        lbl = Image.fromarray(lbl.astype('uint8')).convert('L')\n",
-        "\n",
-        "        im_string = '%000d.jpg' % n\n",
-        "        lbl_string = '%000d.png' % n\n",
-        "\n",
-        "        img_train_fn = os.path.join(write_path, 'images', 'train', im_string)\n",
-        "        img_val_fn = img_train_fn.replace(\"train\", \"val\")\n",
-        "        img.save(img_train_fn)\n",
-        "        img.save(img_val_fn)\n",
-        "\n",
-        "        lbl_train_fn = os.path.join(write_path, 'labels', 'train', lbl_string)\n",
-        "        lbl_val_fn = lbl_train_fn.replace(\"train\", \"val\")\n",
-        "        lbl.save(lbl_train_fn)\n",
-        "        lbl.save(lbl_val_fn)\n",
-        "\n",
-        "        train_fp.write(f\"{img_train_fn} {lbl_train_fn}\\n\")\n",
-        "        val_fp.write(f\"{img_val_fn} {lbl_val_fn}\\n\")\n",
-        "\n",
-        "    train_fp.close()\n",
-        "    val_fp.close()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "DXu4yfuZoiv0",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "dccaf4ba-159f-4a47-d13d-ba4b60eaac80"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Train file `train.txt` content: \n",
-            "/content/example_data/images/train/0.jpg /content/example_data/labels/train/0.png\n",
-            "/content/example_data/images/train/1.jpg /content/example_data/labels/train/1.png\n",
-            "/content/example_data/images/train/2.jpg /content/example_data/labels/train/2.png\n",
-            "/content/example_data/images/train/3.jpg /content/example_data/labels/train/3.png\n",
-            "/content/example_data/images/train/4.jpg /content/example_data/labels/train/4.png\n",
-            "/content/example_data/images/train/5.jpg /content/example_data/labels/train/5.png\n",
-            "/content/example_data/images/train/6.jpg /content/example_data/labels/train/6.png\n",
-            "/content/example_data/images/train/7.jpg /content/example_data/labels/train/7.png\n",
-            "/content/example_data/images/train/8.jpg /content/example_data/labels/train/8.png\n",
-            "/content/example_data/images/train/9.jpg /content/example_data/labels/train/9.png\n"
-          ]
-        }
-      ],
-      "source": [
-        "num_classes = 10\n",
-        "generate_proxy_dataset('/content/example_data', num_samples=10, num_classes=num_classes)\n",
-        "\n",
-        "print(\"Train file `train.txt` content: \")\n",
-        "! cat /content/example_data/train.txt"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MDksFYrIqClt"
-      },
-      "source": [
-        "## 2.B Create Torch Dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "AGziBKSIqaUu"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "from torch.utils.data import Dataset\n",
-        "from torchvision import transforms, utils\n",
-        "\n",
-        "\n",
-        "class CustomDataset(Dataset):\n",
-        "    \"\"\"\n",
-        "    A PyTorch Dataset class to be used in a PyTorch DataLoader to create batches.\n",
-        "    \"\"\"\n",
-        "\n",
-        "    def __init__(self, data_folder, split):\n",
-        "        \"\"\"\n",
-        "        :param data_folder: folder where data files are stored\n",
-        "        :param split: split, one of 'TRAIN' or 'TEST'\n",
-        "        \"\"\"\n",
-        "        self.data_folder = data_folder\n",
-        "        self.split = split.lower()\n",
-        "        assert self.split in {'train', 'val'}\n",
-        "\n",
-        "        # Read data files\n",
-        "        with open(os.path.join(data_folder, self.split + '.txt'), 'r') as f:\n",
-        "            data_lines = f.readlines()\n",
-        "        self.samples_fn = [line.strip().split(\" \") for line in data_lines]\n",
-        "\n",
-        "        self.transforms = transforms.Compose([transforms.ToTensor()])\n",
-        "\n",
-        "    def __getitem__(self, i):\n",
-        "        # Read image and label\n",
-        "        image = Image.open(self.samples_fn[i][0]).convert('RGB')\n",
-        "        label = Image.open(self.samples_fn[i][1])\n",
-        "\n",
-        "        image_tensor = self.transforms(image)\n",
-        "        label_tensor = torch.from_numpy(np.array(label)).long()\n",
-        "\n",
-        "        return image_tensor, label_tensor\n",
-        "\n",
-        "\n",
-        "    def __len__(self):\n",
-        "        return len(self.samples_fn)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "id": "2B0hlas_1Rh-"
-      },
-      "outputs": [],
-      "source": [
-        "train_dataset = CustomDataset(\"/content/example_data\", split=\"train\")\n",
-        "val_dataset = CustomDataset(\"/content/example_data\", split=\"val\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eIG5tsiuor9E"
-      },
-      "source": [
-        "Let's have a look at the first sample:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "ZsHqcq1jpN0F",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e9c1182a-5359-45b6-c0f3-ad430a4fc67d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([3, 256, 256]) torch.Size([256, 256])\n",
-            "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n"
-          ]
-        }
-      ],
-      "source": [
-        "img, lbl = train_dataset[0]\n",
-        "print(img.shape, lbl.shape)\n",
-        "print(torch.unique(lbl))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aWfFrYLzo9j8"
-      },
-      "source": [
-        "## 2.C Create Torch Dataloader"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "D3ThxDIopDDB"
-      },
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "id": "XrWjWfjXnw_r"
-      },
-      "outputs": [],
-      "source": [
-        "from torch.utils.data import Dataset, DataLoader\n",
-        "\n",
-        "train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)\n",
-        "val_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=False, num_workers=2)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vB1sGPO8qwZJ"
-      },
-      "source": [
-        "Lets' have a look at the first batch:\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "O-KuZQ3XBduM",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "489fc05e-f972-464d-c150-360e4f2dbd7e"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "torch.Size([4, 3, 256, 256])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
-        }
-      ],
-      "source": [
-        "next(iter(train_dataloader))[0].shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fFfvyMHU32QF"
-      },
-      "source": [
-        "\n",
-        "# 3. Architecture definition"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EpqgjQjl4awr"
-      },
-      "source": [
-        "SG includes implementations of many different architectures for object detection tasks that can be found [here](https://github.com/Deci-AI/super-gradients#implemented-model-architectures)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GNM64JAa4sbF"
-      },
-      "source": [
-        "Create a PPLiteSeg nn.Module, with 1 class segmentation head classifier. For simplicity `use_aux_head` is set as `False`\n",
-        "and extra Auxiliary heads aren't used for training.\n",
-        "\n",
-        "Other segmentation modules can be used for this task such as, DDRNet, STDC and RegSeg.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "YDK4btf04Gbu",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "cc7f1ab1-3a01-49c1-c9a2-6ca434dcc192"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Downloading: \"https://sghub.deci.ai/models/pp_lite_t_seg75_cityscapes.pth\" to /root/.cache/torch/hub/checkpoints/pp_lite_t_seg75_cityscapes.pth\n",
-            "100%|██████████| 31.4M/31.4M [00:01<00:00, 32.4MB/s]\n",
-            "[2023-11-12 14:41:45] INFO - checkpoint_utils.py - Successfully loaded pretrained weights for architecture pp_lite_t_seg75\n"
-          ]
-        }
-      ],
-      "source": [
-        "from super_gradients.training import models\n",
-        "from super_gradients.common.object_names import Models\n",
-        "\n",
-        "model = models.get(model_name=Models.PP_LITE_T_SEG75,\n",
-        "                   arch_params={\"use_aux_heads\": False},\n",
-        "                   num_classes=num_classes,\n",
-        "                   pretrained_weights=\"cityscapes\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "40UcYJ3u5JyF"
-      },
-      "source": [
-        "That being said, SG allows you to use one of SG implemented architectures or your custom architecture, as long as it inherits torch.nn.Module."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LYPVR-XM4GsZ"
-      },
-      "source": [
-        "# 4. Training setup\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6K_56lDV8azX"
-      },
-      "source": [
-        "\n",
-        "Here we define the training recipe. The full parameters can be found here  [training parameters supported](https://deci-ai.github.io/super-gradients/user_guide.html#training-parameters).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "3eRe0hBz4G1n"
-      },
-      "outputs": [],
-      "source": [
-        "from super_gradients.training.metrics.segmentation_metrics import IoU\n",
-        "from super_gradients.training.utils.callbacks import BinarySegmentationVisualizationCallback, Phase\n",
-        "\n",
-        "\n",
-        "train_params = {\"max_epochs\": 10,\n",
-        "                \"lr_mode\": \"cosine\",\n",
-        "                \"initial_lr\": 0.005,\n",
-        "                \"optimizer\": \"SGD\",\n",
-        "                \"loss\": \"cross_entropy\",\n",
-        "                \"average_best_models\": False,\n",
-        "                \"metric_to_watch\": \"IoU\",\n",
-        "                \"greater_metric_to_watch_is_better\": True,\n",
-        "                \"train_metrics_list\": [IoU(num_classes=10)],\n",
-        "                \"valid_metrics_list\": [IoU(num_classes=10)],\n",
-        "                \"loss_logging_items_names\": [\"loss\"],\n",
-        "                \"phase_callbacks\": [BinarySegmentationVisualizationCallback(phase=Phase.VALIDATION_BATCH_END,\n",
-        "                                                                            freq=1,\n",
-        "                                                                            last_img_idx_in_batch=4)],\n",
-        "\n",
-        "                }"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "D3tVVUhy4OqP"
-      },
-      "source": [
-        "# 5. Training and evaluation\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8tKUuxbe9NlQ"
-      },
-      "source": [
-        "The logs and the checkpoint for the latest epoch will be kept in your experiment folder.\n",
-        "\n",
-        "To start training we'll call train(...) and provide it with the objects we construted above: the model, the training parameters and the data loaders."
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sh6t_y7KzqBH"
+   },
+   "source": [
+    "![SG - 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+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5aISf1B-AGDQ"
+   },
+   "source": [
+    "# SuperGradients Semantic Segmentation How to Connect Custom Dataset\n",
+    "\n",
+    "In this tutorial we will explore how you can connect your custom Semantic Segmentation dataset to SG.\n",
+    "\n",
+    "Since SG trainer is fully compatible with PyTorch data loaders, we will demonstrate how to build one and use it.\n",
+    "\n",
+    "The notebook is divided into 5 sections:\n",
+    "1. Experiment setup\n",
+    "2. Dataset definition: create a proxy dataset and create a dataloader\n",
+    "3. Architecture definition: pre-trained PPLiteSeg on Cityscapes  \n",
+    "4. Training setup\n",
+    "5. Training and Evaluation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-1nPOPmc1lGp"
+   },
+   "source": [
+    "#Install SG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VAssbjJw7Yt1"
+   },
+   "source": [
+    "The cell below will install **super_gradients** which will automatically get all its dependencies. Let's import all the installed libraries to make sure they installed succesfully."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JKce1SM6voVH",
+    "outputId": "a6397510-a140-443f-f13c-eec1272cc1a8"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
+      "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n",
+      "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
+      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
+      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
+      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
+      "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",
+      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Installing build dependencies ... \u001B[?25l\u001B[?25hdone\n",
+      "  Getting requirements to build wheel ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for pycocotools (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for termcolor (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for treelib (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for coverage (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for xhtml2pdf (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for stringcase (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "  Building wheel for svglib (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
+      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "lida 0.0.10 requires fastapi, which is not installed.\n",
+      "lida 0.0.10 requires kaleido, which is not installed.\n",
+      "lida 0.0.10 requires python-multipart, which is not installed.\n",
+      "lida 0.0.10 requires uvicorn, which is not installed.\n",
+      "tensorflow 2.14.0 requires numpy>=1.23.5, but you have numpy 1.23.0 which is incompatible.\u001B[0m\u001B[31m\n",
+      "\u001B[0m"
+     ]
+    }
+   ],
+   "source": [
+    "! pip install -qq super-gradients==3.4.1\n",
+    "! pip install -qq prettyformatter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "njthhNJR1pJm"
+   },
+   "source": [
+    "# 1. Experiment setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YPym4wvpOcOJ"
+   },
+   "source": [
+    "We will first initialize our **trainer** which will be in charge of everything, like training, evaluation, saving checkpoints, plotting etc.\n",
+    "\n",
+    "The **experiment name** argument is important as every checkpoints, logs and tensorboards to be saved in a directory with the same name. This directory will be created as a sub-directory of **ckpt_root_dir** as follow:\n",
+    "\n",
+    "```\n",
+    "ckpt_root_dir\n",
+    "|─── experiment_name_1\n",
+    "│       ckpt_best.pth                     # Model checkpoint on best epoch\n",
+    "│       ckpt_latest.pth                   # Model checkpoint on last epoch\n",
+    "│       average_model.pth                 # Model checkpoint averaged over epochs\n",
+    "│       events.out.tfevents.1659878383... # Tensorflow artifacts of a specific run\n",
+    "│       log_Aug07_11_52_48.txt            # Trainer logs of a specific run\n",
+    "└─── experiment_name_2\n",
+    "        ...\n",
+    "```\n",
+    "In this notebook multi-gpu training is set as `OFF`, for Distributed training multi_gpu can be set as\n",
+    " `MultiGPUMode.DISTRIBUTED_DATA_PARALLEL` or `MultiGPUMode.DATA_PARALLEL`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A2PlnTWpimnH"
+   },
+   "source": [
+    "Let's define **ckpt_root_dir** inside the Colab, later we can use it to start TensorBoard and monitor the run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "_v1N3kXs3wo1"
+   },
+   "outputs": [],
+   "source": [
+    "from super_gradients.training import Trainer, MultiGPUMode\n",
+    "\n",
+    "\n",
+    "CHECKPOINT_DIR = '/home/notebook_ckpts/'\n",
+    "trainer = Trainer(experiment_name='transfer_learning_semantic_segementation_ppLite', ckpt_root_dir=CHECKPOINT_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "J9ZaMulSvwhr"
+   },
+   "source": [
+    "# 2. Dataset definition\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_1TXuJKkKzFJ"
+   },
+   "source": [
+    "## 2.A Generate Proxy Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y7us7VHRig7M"
+   },
+   "source": [
+    "\n",
+    "A proxy dataset generation is available merely to demonstrate an end-to-end training pipeline in this notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "wbdVYnIyjgv-"
+   },
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import os\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "# creation of proxy dataset to demonstrate usage\n",
+    "def generate_proxy_dataset(write_path: str, num_samples: int, num_classes: int, img_size: int = 256):\n",
+    "    # Create training files and text\n",
+    "    os.makedirs(os.path.join(write_path, 'images', 'train'), exist_ok=True)\n",
+    "    os.makedirs(os.path.join(write_path, 'images', 'val'), exist_ok=True)\n",
+    "    os.makedirs(os.path.join(write_path, 'labels', 'train'), exist_ok=True)\n",
+    "    os.makedirs(os.path.join(write_path, 'labels', 'val'), exist_ok=True)\n",
+    "\n",
+    "    train_fp = open(os.path.join(write_path, 'train.txt'), 'w')\n",
+    "    val_fp = open(os.path.join(write_path, 'val.txt'), 'w')\n",
+    "\n",
+    "    # Create random samples\n",
+    "    for n in range(num_samples):\n",
+    "        img = np.random.rand(img_size, img_size, 3) * 255\n",
+    "        img = Image.fromarray(img.astype('uint8')).convert('RGB')\n",
+    "\n",
+    "        lbl = np.random.randint(0, num_classes, size=(img_size, img_size))\n",
+    "        lbl = Image.fromarray(lbl.astype('uint8')).convert('L')\n",
+    "\n",
+    "        im_string = '%000d.jpg' % n\n",
+    "        lbl_string = '%000d.png' % n\n",
+    "\n",
+    "        img_train_fn = os.path.join(write_path, 'images', 'train', im_string)\n",
+    "        img_val_fn = img_train_fn.replace(\"train\", \"val\")\n",
+    "        img.save(img_train_fn)\n",
+    "        img.save(img_val_fn)\n",
+    "\n",
+    "        lbl_train_fn = os.path.join(write_path, 'labels', 'train', lbl_string)\n",
+    "        lbl_val_fn = lbl_train_fn.replace(\"train\", \"val\")\n",
+    "        lbl.save(lbl_train_fn)\n",
+    "        lbl.save(lbl_val_fn)\n",
+    "\n",
+    "        train_fp.write(f\"{img_train_fn} {lbl_train_fn}\\n\")\n",
+    "        val_fp.write(f\"{img_val_fn} {lbl_val_fn}\\n\")\n",
+    "\n",
+    "    train_fp.close()\n",
+    "    val_fp.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "DXu4yfuZoiv0",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "dccaf4ba-159f-4a47-d13d-ba4b60eaac80"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Train file `train.txt` content: \n",
+      "/content/example_data/images/train/0.jpg /content/example_data/labels/train/0.png\n",
+      "/content/example_data/images/train/1.jpg /content/example_data/labels/train/1.png\n",
+      "/content/example_data/images/train/2.jpg /content/example_data/labels/train/2.png\n",
+      "/content/example_data/images/train/3.jpg /content/example_data/labels/train/3.png\n",
+      "/content/example_data/images/train/4.jpg /content/example_data/labels/train/4.png\n",
+      "/content/example_data/images/train/5.jpg /content/example_data/labels/train/5.png\n",
+      "/content/example_data/images/train/6.jpg /content/example_data/labels/train/6.png\n",
+      "/content/example_data/images/train/7.jpg /content/example_data/labels/train/7.png\n",
+      "/content/example_data/images/train/8.jpg /content/example_data/labels/train/8.png\n",
+      "/content/example_data/images/train/9.jpg /content/example_data/labels/train/9.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_classes = 10\n",
+    "generate_proxy_dataset('/content/example_data', num_samples=10, num_classes=num_classes)\n",
+    "\n",
+    "print(\"Train file `train.txt` content: \")\n",
+    "! cat /content/example_data/train.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MDksFYrIqClt"
+   },
+   "source": [
+    "## 2.B Create Torch Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "AGziBKSIqaUu"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.utils.data import Dataset\n",
+    "from torchvision import transforms, utils\n",
+    "\n",
+    "\n",
+    "class CustomDataset(Dataset):\n",
+    "    \"\"\"\n",
+    "    A PyTorch Dataset class to be used in a PyTorch DataLoader to create batches.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, data_folder, split):\n",
+    "        \"\"\"\n",
+    "        :param data_folder: folder where data files are stored\n",
+    "        :param split: split, one of 'TRAIN' or 'TEST'\n",
+    "        \"\"\"\n",
+    "        self.data_folder = data_folder\n",
+    "        self.split = split.lower()\n",
+    "        assert self.split in {'train', 'val'}\n",
+    "\n",
+    "        # Read data files\n",
+    "        with open(os.path.join(data_folder, self.split + '.txt'), 'r') as f:\n",
+    "            data_lines = f.readlines()\n",
+    "        self.samples_fn = [line.strip().split(\" \") for line in data_lines]\n",
+    "\n",
+    "        self.transforms = transforms.Compose([transforms.ToTensor()])\n",
+    "\n",
+    "    def __getitem__(self, i):\n",
+    "        # Read image and label\n",
+    "        image = Image.open(self.samples_fn[i][0]).convert('RGB')\n",
+    "        label = Image.open(self.samples_fn[i][1])\n",
+    "\n",
+    "        image_tensor = self.transforms(image)\n",
+    "        label_tensor = torch.from_numpy(np.array(label)).long()\n",
+    "\n",
+    "        return image_tensor, label_tensor\n",
+    "\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.samples_fn)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "2B0hlas_1Rh-"
+   },
+   "outputs": [],
+   "source": [
+    "train_dataset = CustomDataset(\"/content/example_data\", split=\"train\")\n",
+    "val_dataset = CustomDataset(\"/content/example_data\", split=\"val\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eIG5tsiuor9E"
+   },
+   "source": [
+    "Let's have a look at the first sample:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "ZsHqcq1jpN0F",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "e9c1182a-5359-45b6-c0f3-ad430a4fc67d"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([3, 256, 256]) torch.Size([256, 256])\n",
+      "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n"
+     ]
+    }
+   ],
+   "source": [
+    "img, lbl = train_dataset[0]\n",
+    "print(img.shape, lbl.shape)\n",
+    "print(torch.unique(lbl))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aWfFrYLzo9j8"
+   },
+   "source": [
+    "## 2.C Create Torch Dataloader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D3ThxDIopDDB"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "XrWjWfjXnw_r"
+   },
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)\n",
+    "val_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=False, num_workers=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vB1sGPO8qwZJ"
+   },
+   "source": [
+    "Lets' have a look at the first batch:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "O-KuZQ3XBduM",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "outputId": "489fc05e-f972-464d-c150-360e4f2dbd7e"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "-Ojnc1bk9L3s",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "b36b8b9b-b554-444e-d440-02bf623c3efa"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[2023-11-12 14:41:51] WARNING - sg_trainer.py - Train dataset size % batch_size != 0 and drop_last=False, this might result in smaller last batch.\n",
-            "[2023-11-12 14:41:58] INFO - sg_trainer.py - Starting a new run with `run_id=RUN_20231112_144158_892860`\n",
-            "[2023-11-12 14:41:58] INFO - sg_trainer.py - Checkpoints directory: /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860\n",
-            "/usr/local/lib/python3.10/dist-packages/super_gradients/common/registry/registry.py:72: DeprecationWarning: Object name `cross_entropy` is now deprecated. Please replace it with `CrossEntropyLoss`.\n",
-            "  warnings.warn(f\"Object name `{name}` is now deprecated. Please replace it with `{deprecated_names[name]}`.\", DeprecationWarning)\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "The console stream is now moved to /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/console_Nov12_14_41_58.txt\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[2023-11-12 14:41:59] INFO - sg_trainer_utils.py - TRAINING PARAMETERS:\n",
-            "    - Mode:                         Single GPU\n",
-            "    - Number of GPUs:               1          (1 available on the machine)\n",
-            "    - Full dataset size:            10         (len(train_set))\n",
-            "    - Batch size per GPU:           4          (batch_size)\n",
-            "    - Batch Accumulate:             1          (batch_accumulate)\n",
-            "    - Total batch size:             4          (num_gpus * batch_size)\n",
-            "    - Effective Batch size:         4          (num_gpus * batch_size * batch_accumulate)\n",
-            "    - Iterations per epoch:         3          (len(train_loader))\n",
-            "    - Gradient updates per epoch:   3          (len(train_loader) / batch_accumulate)\n",
-            "\n",
-            "[2023-11-12 14:41:59] INFO - sg_trainer.py - Started training for 10 epochs (0/9)\n",
-            "\n",
-            "Train epoch 0: 100%|██████████| 3/3 [00:08<00:00,  2.91s/it, CrossEntropyLoss=3.49, IoU=0.0319, gpu_mem=0.686]\n",
-            "Validating: 100%|██████████| 3/3 [00:00<00:00,  3.83it/s]\n",
-            "[2023-11-12 14:42:09] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:09] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.013753225095570087\n",
-            "Train epoch 1:   0%|          | 0/3 [00:00<?, ?it/s]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 0\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 3.486\n",
-            "│   └── Iou = 0.0319\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.986\n",
-            "    └── Iou = 0.0138\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 1: 100%|██████████| 3/3 [00:00<00:00,  8.46it/s, CrossEntropyLoss=2.92, IoU=0.0449, gpu_mem=0.686]\n",
-            "Validating epoch 1: 100%|██████████| 3/3 [00:00<00:00,  5.47it/s]\n",
-            "[2023-11-12 14:42:10] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:10] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.030803922563791275\n",
-            "Train epoch 2:  33%|███▎      | 1/3 [00:00<00:00,  5.28it/s, CrossEntropyLoss=2.66, IoU=0.0505, gpu_mem=0.686]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 1\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.9155\n",
-            "│   │   ├── Epoch N-1      = 3.486  (\u001b[32m↘ -0.5705\u001b[0m)\n",
-            "│   │   └── Best until now = 3.486  (\u001b[32m↘ -0.5705\u001b[0m)\n",
-            "│   └── Iou = 0.0449\n",
-            "│       ├── Epoch N-1      = 0.0319 (\u001b[32m↗ 0.013\u001b[0m)\n",
-            "│       └── Best until now = 0.0319 (\u001b[32m↗ 0.013\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.5612\n",
-            "    │   ├── Epoch N-1      = 2.986  (\u001b[32m↘ -0.4249\u001b[0m)\n",
-            "    │   └── Best until now = 2.986  (\u001b[32m↘ -0.4249\u001b[0m)\n",
-            "    └── Iou = 0.0308\n",
-            "        ├── Epoch N-1      = 0.0138 (\u001b[32m↗ 0.0171\u001b[0m)\n",
-            "        └── Best until now = 0.0138 (\u001b[32m↗ 0.0171\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 2: 100%|██████████| 3/3 [00:00<00:00,  8.56it/s, CrossEntropyLoss=2.62, IoU=0.05, gpu_mem=0.686]\n",
-            "Validating epoch 2: 100%|██████████| 3/3 [00:00<00:00,  5.56it/s]\n",
-            "[2023-11-12 14:42:11] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:11] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.03217342495918274\n",
-            "Train epoch 3:  33%|███▎      | 1/3 [00:00<00:00,  5.19it/s, CrossEntropyLoss=2.57, IoU=0.0457, gpu_mem=0.686]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 2\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.6206\n",
-            "│   │   ├── Epoch N-1      = 2.9155 (\u001b[32m↘ -0.2949\u001b[0m)\n",
-            "│   │   └── Best until now = 2.9155 (\u001b[32m↘ -0.2949\u001b[0m)\n",
-            "│   └── Iou = 0.05\n",
-            "│       ├── Epoch N-1      = 0.0449 (\u001b[32m↗ 0.0051\u001b[0m)\n",
-            "│       └── Best until now = 0.0449 (\u001b[32m↗ 0.0051\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.6231\n",
-            "    │   ├── Epoch N-1      = 2.5612 (\u001b[31m↗ 0.062\u001b[0m)\n",
-            "    │   └── Best until now = 2.5612 (\u001b[31m↗ 0.062\u001b[0m)\n",
-            "    └── Iou = 0.0322\n",
-            "        ├── Epoch N-1      = 0.0308 (\u001b[32m↗ 0.0014\u001b[0m)\n",
-            "        └── Best until now = 0.0308 (\u001b[32m↗ 0.0014\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 3: 100%|██████████| 3/3 [00:00<00:00,  8.50it/s, CrossEntropyLoss=2.57, IoU=0.0445, gpu_mem=0.686]\n",
-            "Validating epoch 3: 100%|██████████| 3/3 [00:00<00:00,  5.10it/s]\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 3\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.5662\n",
-            "│   │   ├── Epoch N-1      = 2.6206 (\u001b[32m↘ -0.0544\u001b[0m)\n",
-            "│   │   └── Best until now = 2.6206 (\u001b[32m↘ -0.0544\u001b[0m)\n",
-            "│   └── Iou = 0.0445\n",
-            "│       ├── Epoch N-1      = 0.05   (\u001b[31m↘ -0.0054\u001b[0m)\n",
-            "│       └── Best until now = 0.05   (\u001b[31m↘ -0.0054\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.6995\n",
-            "    │   ├── Epoch N-1      = 2.6231 (\u001b[31m↗ 0.0764\u001b[0m)\n",
-            "    │   └── Best until now = 2.5612 (\u001b[31m↗ 0.1384\u001b[0m)\n",
-            "    └── Iou = 0.0281\n",
-            "        ├── Epoch N-1      = 0.0322 (\u001b[31m↘ -0.0041\u001b[0m)\n",
-            "        └── Best until now = 0.0322 (\u001b[31m↘ -0.0041\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 4: 100%|██████████| 3/3 [00:00<00:00,  8.55it/s, CrossEntropyLoss=2.55, IoU=0.0412, gpu_mem=0.686]\n",
-            "Validating epoch 4: 100%|██████████| 3/3 [00:00<00:00,  5.64it/s]\n",
-            "[2023-11-12 14:42:16] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:16] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.03436211124062538\n",
-            "Train epoch 5:  33%|███▎      | 1/3 [00:00<00:00,  5.69it/s, CrossEntropyLoss=2.5, IoU=0.0416, gpu_mem=0.686]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 4\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.5469\n",
-            "│   │   ├── Epoch N-1      = 2.5662 (\u001b[32m↘ -0.0193\u001b[0m)\n",
-            "│   │   └── Best until now = 2.5662 (\u001b[32m↘ -0.0193\u001b[0m)\n",
-            "│   └── Iou = 0.0412\n",
-            "│       ├── Epoch N-1      = 0.0445 (\u001b[31m↘ -0.0033\u001b[0m)\n",
-            "│       └── Best until now = 0.05   (\u001b[31m↘ -0.0087\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.6421\n",
-            "    │   ├── Epoch N-1      = 2.6995 (\u001b[32m↘ -0.0574\u001b[0m)\n",
-            "    │   └── Best until now = 2.5612 (\u001b[31m↗ 0.0809\u001b[0m)\n",
-            "    └── Iou = 0.0344\n",
-            "        ├── Epoch N-1      = 0.0281 (\u001b[32m↗ 0.0063\u001b[0m)\n",
-            "        └── Best until now = 0.0322 (\u001b[32m↗ 0.0022\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 5: 100%|██████████| 3/3 [00:00<00:00,  8.81it/s, CrossEntropyLoss=2.48, IoU=0.0422, gpu_mem=0.686]\n",
-            "Validating epoch 5: 100%|██████████| 3/3 [00:00<00:00,  5.51it/s]\n",
-            "[2023-11-12 14:42:17] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:17] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.040745388716459274\n",
-            "Train epoch 6:   0%|          | 0/3 [00:00<?, ?it/s]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 5\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.4809\n",
-            "│   │   ├── Epoch N-1      = 2.5469 (\u001b[32m↘ -0.066\u001b[0m)\n",
-            "│   │   └── Best until now = 2.5469 (\u001b[32m↘ -0.066\u001b[0m)\n",
-            "│   └── Iou = 0.0422\n",
-            "│       ├── Epoch N-1      = 0.0412 (\u001b[32m↗ 0.001\u001b[0m)\n",
-            "│       └── Best until now = 0.05   (\u001b[31m↘ -0.0077\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.5262\n",
-            "    │   ├── Epoch N-1      = 2.6421 (\u001b[32m↘ -0.1159\u001b[0m)\n",
-            "    │   └── Best until now = 2.5612 (\u001b[32m↘ -0.035\u001b[0m)\n",
-            "    └── Iou = 0.0407\n",
-            "        ├── Epoch N-1      = 0.0344 (\u001b[32m↗ 0.0064\u001b[0m)\n",
-            "        └── Best until now = 0.0344 (\u001b[32m↗ 0.0064\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 6: 100%|██████████| 3/3 [00:00<00:00,  8.34it/s, CrossEntropyLoss=2.41, IoU=0.0463, gpu_mem=0.686]\n",
-            "Validating epoch 6: 100%|██████████| 3/3 [00:00<00:00,  5.36it/s]\n",
-            "[2023-11-12 14:42:18] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:18] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.045226261019706726\n",
-            "Train epoch 7:  33%|███▎      | 1/3 [00:00<00:00,  5.32it/s, CrossEntropyLoss=2.39, IoU=0.0475, gpu_mem=0.686]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 6\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.4148\n",
-            "│   │   ├── Epoch N-1      = 2.4809 (\u001b[32m↘ -0.0662\u001b[0m)\n",
-            "│   │   └── Best until now = 2.4809 (\u001b[32m↘ -0.0662\u001b[0m)\n",
-            "│   └── Iou = 0.0463\n",
-            "│       ├── Epoch N-1      = 0.0422 (\u001b[32m↗ 0.0041\u001b[0m)\n",
-            "│       └── Best until now = 0.05   (\u001b[31m↘ -0.0036\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.4487\n",
-            "    │   ├── Epoch N-1      = 2.5262 (\u001b[32m↘ -0.0775\u001b[0m)\n",
-            "    │   └── Best until now = 2.5262 (\u001b[32m↘ -0.0775\u001b[0m)\n",
-            "    └── Iou = 0.0452\n",
-            "        ├── Epoch N-1      = 0.0407 (\u001b[32m↗ 0.0045\u001b[0m)\n",
-            "        └── Best until now = 0.0407 (\u001b[32m↗ 0.0045\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 7: 100%|██████████| 3/3 [00:00<00:00,  8.62it/s, CrossEntropyLoss=2.38, IoU=0.0493, gpu_mem=0.686]\n",
-            "Validating epoch 7: 100%|██████████| 3/3 [00:00<00:00,  4.36it/s]\n",
-            "[2023-11-12 14:42:20] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:20] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.04912910237908363\n",
-            "Train epoch 8:   0%|          | 0/3 [00:00<?, ?it/s]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 7\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.3847\n",
-            "│   │   ├── Epoch N-1      = 2.4148 (\u001b[32m↘ -0.03\u001b[0m)\n",
-            "│   │   └── Best until now = 2.4148 (\u001b[32m↘ -0.03\u001b[0m)\n",
-            "│   └── Iou = 0.0493\n",
-            "│       ├── Epoch N-1      = 0.0463 (\u001b[32m↗ 0.003\u001b[0m)\n",
-            "│       └── Best until now = 0.05   (\u001b[31m↘ -0.0007\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.4111\n",
-            "    │   ├── Epoch N-1      = 2.4487 (\u001b[32m↘ -0.0375\u001b[0m)\n",
-            "    │   └── Best until now = 2.4487 (\u001b[32m↘ -0.0375\u001b[0m)\n",
-            "    └── Iou = 0.0491\n",
-            "        ├── Epoch N-1      = 0.0452 (\u001b[32m↗ 0.0039\u001b[0m)\n",
-            "        └── Best until now = 0.0452 (\u001b[32m↗ 0.0039\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 8: 100%|██████████| 3/3 [00:00<00:00,  5.78it/s, CrossEntropyLoss=2.37, IoU=0.0514, gpu_mem=0.686]\n",
-            "Validating epoch 8: 100%|██████████| 3/3 [00:00<00:00,  4.03it/s]\n",
-            "[2023-11-12 14:42:22] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
-            "[2023-11-12 14:42:22] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.05125776678323746\n",
-            "Train epoch 9:   0%|          | 0/3 [00:00<?, ?it/s]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "===========================================================\n",
-            "SUMMARY OF EPOCH 8\n",
-            "├── Train\n",
-            "│   ├── Crossentropyloss = 2.3716\n",
-            "│   │   ├── Epoch N-1      = 2.3847 (\u001b[32m↘ -0.0132\u001b[0m)\n",
-            "│   │   └── Best until now = 2.3847 (\u001b[32m↘ -0.0132\u001b[0m)\n",
-            "│   └── Iou = 0.0514\n",
-            "│       ├── Epoch N-1      = 0.0493 (\u001b[32m↗ 0.0021\u001b[0m)\n",
-            "│       └── Best until now = 0.05   (\u001b[32m↗ 0.0014\u001b[0m)\n",
-            "└── Validation\n",
-            "    ├── Crossentropyloss = 2.4007\n",
-            "    │   ├── Epoch N-1      = 2.4111 (\u001b[32m↘ -0.0104\u001b[0m)\n",
-            "    │   └── Best until now = 2.4111 (\u001b[32m↘ -0.0104\u001b[0m)\n",
-            "    └── Iou = 0.0513\n",
-            "        ├── Epoch N-1      = 0.0491 (\u001b[32m↗ 0.0021\u001b[0m)\n",
-            "        └── Best until now = 0.0491 (\u001b[32m↗ 0.0021\u001b[0m)\n",
-            "\n",
-            "===========================================================\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Train epoch 9: 100%|██████████| 3/3 [00:00<00:00,  6.09it/s, CrossEntropyLoss=2.36, IoU=0.0515, gpu_mem=0.686]\n",
-            "Validating epoch 9: 100%|██████████| 3/3 [00:00<00:00,  4.32it/s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "trainer.train(model=model, training_params=train_params, train_loader=train_dataloader, valid_loader=val_dataloader)"
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([4, 3, 256, 256])"
       ]
+     },
+     "metadata": {},
+     "execution_count": 10
     }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+   ],
+   "source": [
+    "next(iter(train_dataloader))[0].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fFfvyMHU32QF"
+   },
+   "source": [
+    "\n",
+    "# 3. Architecture definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EpqgjQjl4awr"
+   },
+   "source": [
+    "SG includes implementations of many different architectures for object detection tasks that can be found [here](https://github.com/Deci-AI/super-gradients#implemented-model-architectures)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GNM64JAa4sbF"
+   },
+   "source": [
+    "Create a PPLiteSeg nn.Module, with 1 class segmentation head classifier. For simplicity `use_aux_head` is set as `False`\n",
+    "and extra Auxiliary heads aren't used for training.\n",
+    "\n",
+    "Other segmentation modules can be used for this task such as, DDRNet, STDC and RegSeg.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "YDK4btf04Gbu",
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "cc7f1ab1-3a01-49c1-c9a2-6ca434dcc192"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Downloading: \"https://sghub.deci.ai/models/pp_lite_t_seg75_cityscapes.pth\" to /root/.cache/torch/hub/checkpoints/pp_lite_t_seg75_cityscapes.pth\n",
+      "100%|██████████| 31.4M/31.4M [00:01<00:00, 32.4MB/s]\n",
+      "[2023-11-12 14:41:45] INFO - checkpoint_utils.py - Successfully loaded pretrained weights for architecture pp_lite_t_seg75\n"
+     ]
+    }
+   ],
+   "source": [
+    "from super_gradients.training import models\n",
+    "from super_gradients.common.object_names import Models\n",
+    "\n",
+    "model = models.get(model_name=Models.PP_LITE_T_SEG75,\n",
+    "                   arch_params={\"use_aux_heads\": False},\n",
+    "                   num_classes=num_classes,\n",
+    "                   pretrained_weights=\"cityscapes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "40UcYJ3u5JyF"
+   },
+   "source": [
+    "That being said, SG allows you to use one of SG implemented architectures or your custom architecture, as long as it inherits torch.nn.Module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LYPVR-XM4GsZ"
+   },
+   "source": [
+    "# 4. Training setup\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6K_56lDV8azX"
+   },
+   "source": [
+    "\n",
+    "Here we define the training recipe. The full parameters can be found here  [training parameters supported](https://deci-ai.github.io/super-gradients/user_guide.html#training-parameters).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "3eRe0hBz4G1n"
+   },
+   "outputs": [],
+   "source": [
+    "from super_gradients.training.metrics.segmentation_metrics import IoU\n",
+    "from super_gradients.training.utils.callbacks import BinarySegmentationVisualizationCallback, Phase\n",
+    "\n",
+    "\n",
+    "train_params = {\"max_epochs\": 10,\n",
+    "                \"lr_mode\": \"cosine\",\n",
+    "                \"initial_lr\": 0.005,\n",
+    "                \"optimizer\": \"SGD\",\n",
+    "                \"loss\": \"cross_entropy\",\n",
+    "                \"average_best_models\": False,\n",
+    "                \"metric_to_watch\": \"IoU\",\n",
+    "                \"greater_metric_to_watch_is_better\": True,\n",
+    "                \"train_metrics_list\": [IoU(num_classes=10)],\n",
+    "                \"valid_metrics_list\": [IoU(num_classes=10)],\n",
+    "                \"loss_logging_items_names\": [\"loss\"],\n",
+    "                \"phase_callbacks\": [BinarySegmentationVisualizationCallback(phase=Phase.VALIDATION_BATCH_END,\n",
+    "                                                                            freq=1,\n",
+    "                                                                            last_img_idx_in_batch=4)],\n",
+    "\n",
+    "                }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D3tVVUhy4OqP"
+   },
+   "source": [
+    "# 5. Training and evaluation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8tKUuxbe9NlQ"
+   },
+   "source": [
+    "The logs and the checkpoint for the latest epoch will be kept in your experiment folder.\n",
+    "\n",
+    "To start training we'll call train(...) and provide it with the objects we construted above: the model, the training parameters and the data loaders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "-Ojnc1bk9L3s",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "b36b8b9b-b554-444e-d440-02bf623c3efa"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[2023-11-12 14:41:51] WARNING - sg_trainer.py - Train dataset size % batch_size != 0 and drop_last=False, this might result in smaller last batch.\n",
+      "[2023-11-12 14:41:58] INFO - sg_trainer.py - Starting a new run with `run_id=RUN_20231112_144158_892860`\n",
+      "[2023-11-12 14:41:58] INFO - sg_trainer.py - Checkpoints directory: /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860\n",
+      "/usr/local/lib/python3.10/dist-packages/super_gradients/common/registry/registry.py:72: DeprecationWarning: Object name `cross_entropy` is now deprecated. Please replace it with `CrossEntropyLoss`.\n",
+      "  warnings.warn(f\"Object name `{name}` is now deprecated. Please replace it with `{deprecated_names[name]}`.\", DeprecationWarning)\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The console stream is now moved to /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/console_Nov12_14_41_58.txt\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[2023-11-12 14:41:59] INFO - sg_trainer_utils.py - TRAINING PARAMETERS:\n",
+      "    - Mode:                         Single GPU\n",
+      "    - Number of GPUs:               1          (1 available on the machine)\n",
+      "    - Full dataset size:            10         (len(train_set))\n",
+      "    - Batch size per GPU:           4          (batch_size)\n",
+      "    - Batch Accumulate:             1          (batch_accumulate)\n",
+      "    - Total batch size:             4          (num_gpus * batch_size)\n",
+      "    - Effective Batch size:         4          (num_gpus * batch_size * batch_accumulate)\n",
+      "    - Iterations per epoch:         3          (len(train_loader))\n",
+      "    - Gradient updates per epoch:   3          (len(train_loader) / batch_accumulate)\n",
+      "\n",
+      "[2023-11-12 14:41:59] INFO - sg_trainer.py - Started training for 10 epochs (0/9)\n",
+      "\n",
+      "Train epoch 0: 100%|██████████| 3/3 [00:08<00:00,  2.91s/it, CrossEntropyLoss=3.49, IoU=0.0319, gpu_mem=0.686]\n",
+      "Validating: 100%|██████████| 3/3 [00:00<00:00,  3.83it/s]\n",
+      "[2023-11-12 14:42:09] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:09] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.013753225095570087\n",
+      "Train epoch 1:   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 0\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 3.486\n",
+      "│   └── Iou = 0.0319\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.986\n",
+      "    └── Iou = 0.0138\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 1: 100%|██████████| 3/3 [00:00<00:00,  8.46it/s, CrossEntropyLoss=2.92, IoU=0.0449, gpu_mem=0.686]\n",
+      "Validating epoch 1: 100%|██████████| 3/3 [00:00<00:00,  5.47it/s]\n",
+      "[2023-11-12 14:42:10] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:10] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.030803922563791275\n",
+      "Train epoch 2:  33%|███▎      | 1/3 [00:00<00:00,  5.28it/s, CrossEntropyLoss=2.66, IoU=0.0505, gpu_mem=0.686]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 1\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.9155\n",
+      "│   │   ├── Epoch N-1      = 3.486  (\u001B[32m↘ -0.5705\u001B[0m)\n",
+      "│   │   └── Best until now = 3.486  (\u001B[32m↘ -0.5705\u001B[0m)\n",
+      "│   └── Iou = 0.0449\n",
+      "│       ├── Epoch N-1      = 0.0319 (\u001B[32m↗ 0.013\u001B[0m)\n",
+      "│       └── Best until now = 0.0319 (\u001B[32m↗ 0.013\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.5612\n",
+      "    │   ├── Epoch N-1      = 2.986  (\u001B[32m↘ -0.4249\u001B[0m)\n",
+      "    │   └── Best until now = 2.986  (\u001B[32m↘ -0.4249\u001B[0m)\n",
+      "    └── Iou = 0.0308\n",
+      "        ├── Epoch N-1      = 0.0138 (\u001B[32m↗ 0.0171\u001B[0m)\n",
+      "        └── Best until now = 0.0138 (\u001B[32m↗ 0.0171\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 2: 100%|██████████| 3/3 [00:00<00:00,  8.56it/s, CrossEntropyLoss=2.62, IoU=0.05, gpu_mem=0.686]\n",
+      "Validating epoch 2: 100%|██████████| 3/3 [00:00<00:00,  5.56it/s]\n",
+      "[2023-11-12 14:42:11] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:11] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.03217342495918274\n",
+      "Train epoch 3:  33%|███▎      | 1/3 [00:00<00:00,  5.19it/s, CrossEntropyLoss=2.57, IoU=0.0457, gpu_mem=0.686]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 2\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.6206\n",
+      "│   │   ├── Epoch N-1      = 2.9155 (\u001B[32m↘ -0.2949\u001B[0m)\n",
+      "│   │   └── Best until now = 2.9155 (\u001B[32m↘ -0.2949\u001B[0m)\n",
+      "│   └── Iou = 0.05\n",
+      "│       ├── Epoch N-1      = 0.0449 (\u001B[32m↗ 0.0051\u001B[0m)\n",
+      "│       └── Best until now = 0.0449 (\u001B[32m↗ 0.0051\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.6231\n",
+      "    │   ├── Epoch N-1      = 2.5612 (\u001B[31m↗ 0.062\u001B[0m)\n",
+      "    │   └── Best until now = 2.5612 (\u001B[31m↗ 0.062\u001B[0m)\n",
+      "    └── Iou = 0.0322\n",
+      "        ├── Epoch N-1      = 0.0308 (\u001B[32m↗ 0.0014\u001B[0m)\n",
+      "        └── Best until now = 0.0308 (\u001B[32m↗ 0.0014\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 3: 100%|██████████| 3/3 [00:00<00:00,  8.50it/s, CrossEntropyLoss=2.57, IoU=0.0445, gpu_mem=0.686]\n",
+      "Validating epoch 3: 100%|██████████| 3/3 [00:00<00:00,  5.10it/s]\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 3\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.5662\n",
+      "│   │   ├── Epoch N-1      = 2.6206 (\u001B[32m↘ -0.0544\u001B[0m)\n",
+      "│   │   └── Best until now = 2.6206 (\u001B[32m↘ -0.0544\u001B[0m)\n",
+      "│   └── Iou = 0.0445\n",
+      "│       ├── Epoch N-1      = 0.05   (\u001B[31m↘ -0.0054\u001B[0m)\n",
+      "│       └── Best until now = 0.05   (\u001B[31m↘ -0.0054\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.6995\n",
+      "    │   ├── Epoch N-1      = 2.6231 (\u001B[31m↗ 0.0764\u001B[0m)\n",
+      "    │   └── Best until now = 2.5612 (\u001B[31m↗ 0.1384\u001B[0m)\n",
+      "    └── Iou = 0.0281\n",
+      "        ├── Epoch N-1      = 0.0322 (\u001B[31m↘ -0.0041\u001B[0m)\n",
+      "        └── Best until now = 0.0322 (\u001B[31m↘ -0.0041\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 4: 100%|██████████| 3/3 [00:00<00:00,  8.55it/s, CrossEntropyLoss=2.55, IoU=0.0412, gpu_mem=0.686]\n",
+      "Validating epoch 4: 100%|██████████| 3/3 [00:00<00:00,  5.64it/s]\n",
+      "[2023-11-12 14:42:16] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:16] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.03436211124062538\n",
+      "Train epoch 5:  33%|███▎      | 1/3 [00:00<00:00,  5.69it/s, CrossEntropyLoss=2.5, IoU=0.0416, gpu_mem=0.686]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 4\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.5469\n",
+      "│   │   ├── Epoch N-1      = 2.5662 (\u001B[32m↘ -0.0193\u001B[0m)\n",
+      "│   │   └── Best until now = 2.5662 (\u001B[32m↘ -0.0193\u001B[0m)\n",
+      "│   └── Iou = 0.0412\n",
+      "│       ├── Epoch N-1      = 0.0445 (\u001B[31m↘ -0.0033\u001B[0m)\n",
+      "│       └── Best until now = 0.05   (\u001B[31m↘ -0.0087\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.6421\n",
+      "    │   ├── Epoch N-1      = 2.6995 (\u001B[32m↘ -0.0574\u001B[0m)\n",
+      "    │   └── Best until now = 2.5612 (\u001B[31m↗ 0.0809\u001B[0m)\n",
+      "    └── Iou = 0.0344\n",
+      "        ├── Epoch N-1      = 0.0281 (\u001B[32m↗ 0.0063\u001B[0m)\n",
+      "        └── Best until now = 0.0322 (\u001B[32m↗ 0.0022\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 5: 100%|██████████| 3/3 [00:00<00:00,  8.81it/s, CrossEntropyLoss=2.48, IoU=0.0422, gpu_mem=0.686]\n",
+      "Validating epoch 5: 100%|██████████| 3/3 [00:00<00:00,  5.51it/s]\n",
+      "[2023-11-12 14:42:17] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:17] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.040745388716459274\n",
+      "Train epoch 6:   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 5\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.4809\n",
+      "│   │   ├── Epoch N-1      = 2.5469 (\u001B[32m↘ -0.066\u001B[0m)\n",
+      "│   │   └── Best until now = 2.5469 (\u001B[32m↘ -0.066\u001B[0m)\n",
+      "│   └── Iou = 0.0422\n",
+      "│       ├── Epoch N-1      = 0.0412 (\u001B[32m↗ 0.001\u001B[0m)\n",
+      "│       └── Best until now = 0.05   (\u001B[31m↘ -0.0077\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.5262\n",
+      "    │   ├── Epoch N-1      = 2.6421 (\u001B[32m↘ -0.1159\u001B[0m)\n",
+      "    │   └── Best until now = 2.5612 (\u001B[32m↘ -0.035\u001B[0m)\n",
+      "    └── Iou = 0.0407\n",
+      "        ├── Epoch N-1      = 0.0344 (\u001B[32m↗ 0.0064\u001B[0m)\n",
+      "        └── Best until now = 0.0344 (\u001B[32m↗ 0.0064\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 6: 100%|██████████| 3/3 [00:00<00:00,  8.34it/s, CrossEntropyLoss=2.41, IoU=0.0463, gpu_mem=0.686]\n",
+      "Validating epoch 6: 100%|██████████| 3/3 [00:00<00:00,  5.36it/s]\n",
+      "[2023-11-12 14:42:18] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:18] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.045226261019706726\n",
+      "Train epoch 7:  33%|███▎      | 1/3 [00:00<00:00,  5.32it/s, CrossEntropyLoss=2.39, IoU=0.0475, gpu_mem=0.686]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 6\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.4148\n",
+      "│   │   ├── Epoch N-1      = 2.4809 (\u001B[32m↘ -0.0662\u001B[0m)\n",
+      "│   │   └── Best until now = 2.4809 (\u001B[32m↘ -0.0662\u001B[0m)\n",
+      "│   └── Iou = 0.0463\n",
+      "│       ├── Epoch N-1      = 0.0422 (\u001B[32m↗ 0.0041\u001B[0m)\n",
+      "│       └── Best until now = 0.05   (\u001B[31m↘ -0.0036\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.4487\n",
+      "    │   ├── Epoch N-1      = 2.5262 (\u001B[32m↘ -0.0775\u001B[0m)\n",
+      "    │   └── Best until now = 2.5262 (\u001B[32m↘ -0.0775\u001B[0m)\n",
+      "    └── Iou = 0.0452\n",
+      "        ├── Epoch N-1      = 0.0407 (\u001B[32m↗ 0.0045\u001B[0m)\n",
+      "        └── Best until now = 0.0407 (\u001B[32m↗ 0.0045\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 7: 100%|██████████| 3/3 [00:00<00:00,  8.62it/s, CrossEntropyLoss=2.38, IoU=0.0493, gpu_mem=0.686]\n",
+      "Validating epoch 7: 100%|██████████| 3/3 [00:00<00:00,  4.36it/s]\n",
+      "[2023-11-12 14:42:20] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:20] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.04912910237908363\n",
+      "Train epoch 8:   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 7\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.3847\n",
+      "│   │   ├── Epoch N-1      = 2.4148 (\u001B[32m↘ -0.03\u001B[0m)\n",
+      "│   │   └── Best until now = 2.4148 (\u001B[32m↘ -0.03\u001B[0m)\n",
+      "│   └── Iou = 0.0493\n",
+      "│       ├── Epoch N-1      = 0.0463 (\u001B[32m↗ 0.003\u001B[0m)\n",
+      "│       └── Best until now = 0.05   (\u001B[31m↘ -0.0007\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.4111\n",
+      "    │   ├── Epoch N-1      = 2.4487 (\u001B[32m↘ -0.0375\u001B[0m)\n",
+      "    │   └── Best until now = 2.4487 (\u001B[32m↘ -0.0375\u001B[0m)\n",
+      "    └── Iou = 0.0491\n",
+      "        ├── Epoch N-1      = 0.0452 (\u001B[32m↗ 0.0039\u001B[0m)\n",
+      "        └── Best until now = 0.0452 (\u001B[32m↗ 0.0039\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 8: 100%|██████████| 3/3 [00:00<00:00,  5.78it/s, CrossEntropyLoss=2.37, IoU=0.0514, gpu_mem=0.686]\n",
+      "Validating epoch 8: 100%|██████████| 3/3 [00:00<00:00,  4.03it/s]\n",
+      "[2023-11-12 14:42:22] INFO - base_sg_logger.py - Checkpoint saved in /home/notebook_ckpts/transfer_learning_semantic_segementation_ppLite/RUN_20231112_144158_892860/ckpt_best.pth\n",
+      "[2023-11-12 14:42:22] INFO - sg_trainer.py - Best checkpoint overriden: validation IoU: 0.05125776678323746\n",
+      "Train epoch 9:   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "===========================================================\n",
+      "SUMMARY OF EPOCH 8\n",
+      "├── Train\n",
+      "│   ├── Crossentropyloss = 2.3716\n",
+      "│   │   ├── Epoch N-1      = 2.3847 (\u001B[32m↘ -0.0132\u001B[0m)\n",
+      "│   │   └── Best until now = 2.3847 (\u001B[32m↘ -0.0132\u001B[0m)\n",
+      "│   └── Iou = 0.0514\n",
+      "│       ├── Epoch N-1      = 0.0493 (\u001B[32m↗ 0.0021\u001B[0m)\n",
+      "│       └── Best until now = 0.05   (\u001B[32m↗ 0.0014\u001B[0m)\n",
+      "└── Validation\n",
+      "    ├── Crossentropyloss = 2.4007\n",
+      "    │   ├── Epoch N-1      = 2.4111 (\u001B[32m↘ -0.0104\u001B[0m)\n",
+      "    │   └── Best until now = 2.4111 (\u001B[32m↘ -0.0104\u001B[0m)\n",
+      "    └── Iou = 0.0513\n",
+      "        ├── Epoch N-1      = 0.0491 (\u001B[32m↗ 0.0021\u001B[0m)\n",
+      "        └── Best until now = 0.0491 (\u001B[32m↗ 0.0021\u001B[0m)\n",
+      "\n",
+      "===========================================================\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Train epoch 9: 100%|██████████| 3/3 [00:00<00:00,  6.09it/s, CrossEntropyLoss=2.36, IoU=0.0515, gpu_mem=0.686]\n",
+      "Validating epoch 9: 100%|██████████| 3/3 [00:00<00:00,  4.32it/s]\n"
+     ]
     }
+   ],
+   "source": [
+    "trainer.train(model=model, training_params=train_params, train_loader=train_dataloader, valid_loader=val_dataloader)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/notebooks/transfer_learning_semantic_segmentation.ipynb b/notebooks/transfer_learning_semantic_segmentation.ipynb
index 8e50b9036e..3995333ed8 100644
--- a/notebooks/transfer_learning_semantic_segmentation.ipynb
+++ b/notebooks/transfer_learning_semantic_segmentation.ipynb
@@ -171,7 +171,6 @@
     }
    ],
    "source": [
-    "! pip install torch torchvision torchaudio\n",
     "! pip install -qq super-gradients==3.4.1\n",
     "! pip install -qq prettyformatter"
    ]