tutorial.ipynb

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      "source": [
        "# 1. Setup\n",
        "\n",
        "Clone repo, install dependencies and check PyTorch and GPU.\n",
        "\n",
        "Alternatively, you can build the docker image or download our pre-built [image](https://hub.docker.com/repository/docker/sudochris/yolo6dpose/general) to reproduce the experiments\n",
        "\n",
        "The code has been tested on Python 3.9.1 on both windows 11 and Ubunutu 20.04"
      ]
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      "source": [
        "!git clone https://github.com/cviviers/YOLOv5-6D-Pose.git  # clone\n",
        "!cd YOLOv5-6D-Pose\n",
        "!pip install -r requirements.txt  # install\n",
        "\n",
        "# (Optional) install dependecies for calculating the 6D pose of symmetric objects\n",
        "# This requires python3.XX-dev (your version of python) in addition to a C++ compiler eg. sudo apt-get install python3.9-dev\n",
        "!cd ./utils/\n",
        "!python ./setup.py build_ext --inplace  # you will need to install cython # https://stackoverflow.com/questions/7508803/how-do-i-import-function-from-pyx-file-in-python\n",
        "\n",
        "import torch\n",
        "from IPython.display import clear_output  # to display images\n",
        "\n",
        "clear_output()\n",
        "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Example project layout\n",
        "\n",
        "```\n",
        "├── data\n",
        "│   ├── LINEMOD\n",
        "│   ├── VOCdevkit\n",
        "│   ├── Weights    \n",
        "├── yolov5_6d_pose\n",
        "│   ├── tutorial.ipynb\n",
        "│   ├── train.py\n",
        "│   ├── detect.py\n",
        "│   ├── etc...\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JnkELT0cIJg"
      },
      "source": [
        "# 2. Inference\n",
        "\n",
        "1. Download the pretrained weights from [google_drive](https://drive.google.com/drive/folders/11BW41xO3R1UBnc2Dx1xA3CPbYPGTrfHQ?usp=drive_link)\n",
        "2. Extract them to a preferred location\n",
        "3. `detect.py` runs inference on a variety of sources. In this example we included an image from the LINEMOD dataset"
      ]
    },
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          "text": [
            "'wget' is not recognized as an internal or external command,\n",
            "operable program or batch file.\n",
            "tar: Error opening archive: Failed to open 'weights.tar'\n"
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      ],
      "source": [
        "# Extract YOLOv5-6D pretrained weights, this includes the weights for the intialization and the fine-tuned models for each of the objects\n",
        "!tar xf linemod_weights.tar -C ../weights"
      ]
    },
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      "source": [
        "!python detect.py --weights path/to/cat/best.pt --img 640 --conf 0.25 --source results/images/example_cat.jpg --static-camera configs/linemod/linemod_camera.json --mesh-data path/to/cat.ply"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eq1SMWl6Sfn"
      },
      "source": [
        "# 3. Test\n",
        "Test a model on [LINEMOD](https://arxiv.org/abs/1808.08319)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "# Download LINEMOD the original dataset\n",
        "!wget -O LINEMOD.tar --no-check-certificate \"https://onedrive.live.com/download?cid=05750EBEE1537631&resid=5750EBEE1537631%21135&authkey=AJRHFmZbcjXxTmI\"\n",
        "!tar xf LINEMOD.tar -C ../data\n",
        "\n",
        "# Be sure to convert the data to our format focusing on the camera intrinsics\n",
        "!python data_curation/convert_linemod.py --path_to_linemod ../data/LINEMOD\n",
        "\n",
        "# Ulternatively, you can download the converted dataset from here: https://drive.google.com/drive/folders/13pkdF4KpqXWXbEIkiAcHoO-YUr_CWN-L?usp=drive_link\n",
        "!tar xf LINEMOD_updated.tar -C ../data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test LINEMOD cat \n",
        "!python test.py --batch 1 --weights ../data/weights/linemod/cat/best.pt --data configs/linemod/cat.yaml --rect --cache"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# 4. Train\n",
        "\n",
        "Download [LINEMOD](https://bop.felk.cvut.cz/) and [VOCdevkit](http://host.robots.ox.ac.uk/pascal/VOC/index.html), start tensorboard/wnadb and train YOLOv5-6D (note actual training is typically much longer, around **300-6000 epochs**, depending on your dataset)."
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      "source": [
        "# Download VOC, This can also be any other dataset you want to use for background images. We also use VOC for occlusion augmentation\n",
        "!wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar  # Alternatively - http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar or https://drive.google.com/drive/folders/13pkdF4KpqXWXbEIkiAcHoO-YUr_CWN-L?usp=drive_link\n",
        "!tar xf VOCtrainval_11-May-2012.tar -C ../data"
      ]
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        "id": "bOy5KI2ncnWd"
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      "source": [
        "# Tensorboard (optional)\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs/train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "# Weights & Biases (optional)\n",
        "%pip install -q wandb  \n",
        "\n",
        "!wandb login  # use 'wandb disabled' or 'wandb enabled' to disable or enable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Training configuration\n",
        "```bash\n",
        "python train.py --batch \"batch size\" --epochs \"number of epochs\" --cfg \"model configuration\" --hyp \"hyper parameter configuration\" --weights \"starting weights\" --data \"data configuration\" --rect --cache --optimizer Adam \n",
        "```"
      ]
    },
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      "source": [
        "# By default, the training script will download the COCO pretrained YOLOv5-v7 model. If this is no longer supported, you can download the weights from the YOLOv5-6D google drive (https://drive.google.com/drive/folders/11BW41xO3R1UBnc2Dx1xA3CPbYPGTrfHQ?usp=drive_link) and place them in the weights folder\n",
        "!python train.py --batch 32 --epochs 5000 --cfg models/yolov5x_6dpose_bifpn.yaml --hyp configs/hyp.single.yaml --weights yolov5x.pt --data configs/linemod/benchvise.yaml --rect --cache --optimizer Adam "
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# 5. Visualize\n",
        "## Weights & Biases Logging\n",
        "\n",
        "[Weights & Biases](https://www.wandb.com/) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
        "\n",
        "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WPvRbS5Swl6"
      },
      "source": [
        "## Local Logging\n",
        "\n",
        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test pngs to see labels, predictions and augmentation effects."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KN5ghjE6ZWh"
      },
      "source": [
        "Training losses and performance metrics are also logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and a custom `results.txt` logfile which is plotted as `results.png` (below) after training completes. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zelyeqbyt3GD"
      },
      "source": [
        "# Environments\n",
        "\n",
        "YOLOv5-6D may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
        "\n",
        "- **Google Colab and Kaggle** notebooks with free GPU: <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
        "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
      ]
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