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| #user scripts | ||
| *.sh | ||
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| # model files | ||
| *.onnx | ||
| *.pt | ||
| *.engine | ||
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| # Please insure that your custom_dataset are put in same parent dir with YOLOv6_DIR | ||
| train: VOCdevkit/voc_07_12/images/train # train images | ||
| val: VOCdevkit/voc_07_12/images/val # val images | ||
| test: VOCdevkit/voc_07_12/images/val # test images (optional) | ||
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| # whether it is coco dataset, only coco dataset should be set to True. | ||
| is_coco: False | ||
| # Classes | ||
| nc: 20 # number of classes | ||
| names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', | ||
| 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Training YOLOv6 on VOC dataset" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Step 1: Prepare VOC dataset" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "| dataset | url | size | images |\n", | ||
| "| :----: | :----: |:----: | :----: |\n", | ||
| "| VOC2007 trainval | [download zip](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar) | 446MB | 5012 \n", | ||
| "| VOC2007 test | [download zip](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar) | 438MB | 4953\n", | ||
| "| VOC2012 trainval | [download zip](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) | 1.95GB | 17126" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Download VOC dataset and unzip them, the directory shows like:\n", | ||
| "```\n", | ||
| "VOCdevkit\n", | ||
| "├── VOC2007\n", | ||
| "│ ├── Annotations\n", | ||
| "│ ├── ImageSets\n", | ||
| "│ ├── JPEGImages\n", | ||
| "│ ├── SegmentationClass\n", | ||
| "│ └── SegmentationObject\n", | ||
| "└── VOC2012\n", | ||
| " ├── Annotations\n", | ||
| " ├── ImageSets\n", | ||
| " ├── JPEGImages\n", | ||
| " ├── SegmentationClass\n", | ||
| " └── SegmentationObject\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Step 2: Convert VOC dataset to YOLO-format." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The VOC dataset use xml format annotations as below. (refer to [VOC2007 guidelines](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/guidelines.html))\n", | ||
| "```\n", | ||
| "<annotation>\n", | ||
| "\t<folder>VOC2007</folder>\n", | ||
| "\t<filename>000007.jpg</filename>\n", | ||
| "\t<source>\n", | ||
| "\t\t<database>The VOC2007 Database</database>\n", | ||
| "\t\t<annotation>PASCAL VOC2007</annotation>\n", | ||
| "\t\t<image>flickr</image>\n", | ||
| "\t\t<flickrid>194179466</flickrid>\n", | ||
| "\t</source>\n", | ||
| "\t<owner>\n", | ||
| "\t\t<flickrid>monsieurrompu</flickrid>\n", | ||
| "\t\t<name>Thom Zemanek</name>\n", | ||
| "\t</owner>\n", | ||
| "\t<size>\n", | ||
| "\t\t<width>500</width>\n", | ||
| "\t\t<height>333</height>\n", | ||
| "\t\t<depth>3</depth>\n", | ||
| "\t</size>\n", | ||
| "\t<segmented>0</segmented>\n", | ||
| "\t<object>\n", | ||
| "\t\t<name>car</name>\n", | ||
| "\t\t<pose>Unspecified</pose>\n", | ||
| "\t\t<truncated>1</truncated>\n", | ||
| "\t\t<difficult>0</difficult>\n", | ||
| "\t\t<bndbox>\n", | ||
| "\t\t\t<xmin>141</xmin>\n", | ||
| "\t\t\t<ymin>50</ymin>\n", | ||
| "\t\t\t<xmax>500</xmax>\n", | ||
| "\t\t\t<ymax>330</ymax>\n", | ||
| "\t\t</bndbox>\n", | ||
| "\t</object>\n", | ||
| "</annotation>\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Run the following command to convert voc dataset to yolo format:\n", | ||
| "\n", | ||
| "  `python yolov6/data/voc2yolo.py --voc_path your_path/to/VOCdevkit`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We follow the `07+12` training setting, which means using VOC2007 and VOC2012's train+val(16551) as training set, VOC2007's test(4952) as validation set and testing set.\n", | ||
| "\n", | ||
| "Finally, the directory looks like:\n", | ||
| "```\n", | ||
| "VOCdevkit\n", | ||
| "├── images\n", | ||
| "├── labels\n", | ||
| "├── voc_07_12\n", | ||
| "│ ├── images\n", | ||
| "│ │ ├── train\n", | ||
| "│ │ └── val\n", | ||
| "│ └── labels\n", | ||
| "│ ├── train\n", | ||
| "│ └── val\n", | ||
| "├── VOC2007\n", | ||
| "└── VOC2012\n", | ||
| "```\n", | ||
| "Where `voc_07_12` is the converted yolo-format dataset." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Visualize yolo format dataset (Optional)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "To check if your dataset is correct, run the following command:\n", | ||
| "\n", | ||
| "  `python yolov6/data/vis_dataset.py --img_dir your_path/to/VOCdevkit/images/train --label_dir your_path/to/VOCdevkit/labels/train`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Step 3: Create dataset config file." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Create `data/voc.yaml` like:\n", | ||
| "\n", | ||
| "```\n", | ||
| "# Please insure that your custom_dataset are put in same parent dir with YOLOv6_DIR\n", | ||
| "train: your_path/to/VOCdevkit/voc_07_12/images/train # train images\n", | ||
| "val: your_path/to/VOCdevkit/voc_07_12/images/val # val images\n", | ||
| "test: your_path/to/VOCdevkit/voc_07_12/images/val # test images (optional)\n", | ||
| "\n", | ||
| "# whether it is coco dataset, only coco dataset should be set to True.\n", | ||
| "is_coco: False\n", | ||
| "# Classes\n", | ||
| "nc: 20 # number of classes\n", | ||
| "names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',\n", | ||
| " 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Step 4: Training.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Use the following command to start training:\n", | ||
| "- Multi GPUs (DDP mode recommended)\n", | ||
| "\n", | ||
| "  `python -m torch.distributed.launch --nproc_per_node 4 --master_port=23456 tools/train.py --batch 256 --conf configs/yolov6n_finetune.py --data data/voc.yaml --device 0,1,2,3`\n", | ||
| "\n", | ||
| "- Single GPU\n", | ||
| "\n", | ||
| "  `python tools/train.py --batch 256 --conf configs/yolov6_finetune.py --data data/data.yaml --device 0`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Tensorboard\n", | ||
| "We can use tensorboard to visualize the loss/mAP curve, run:\n", | ||
| "\n", | ||
| "  `tensorboard --logdir=your_path/to/log`\n", | ||
| "\n", | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Evaluation\n", | ||
| "When training finished, it automatically do evaulation on the testset, the output metrics are:\n", | ||
| "```\n", | ||
| "DONE (t=4.21s).\n", | ||
| " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.632\n", | ||
| " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.854\n", | ||
| " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.702\n", | ||
| " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.272\n", | ||
| " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473\n", | ||
| " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.689\n", | ||
| " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.518\n", | ||
| " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.737\n", | ||
| " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.751\n", | ||
| " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.554\n", | ||
| " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.656\n", | ||
| " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.791\n", | ||
| "Epoch: 399 | [email protected]: 0.8542516455615079 | [email protected]:0.95: 0.6315693468708705\n", | ||
| "\n", | ||
| "Training completed in 9.206 hours.\n", | ||
| "```\n", | ||
| "Or you can manually evaulation model on your dataset by:\n", | ||
| "\n", | ||
| "  `python tools/eval.py --data data/voc.yaml --weights your_path/to/weights/best_ckpt.pt --device 0`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### 5.Inference\n", | ||
| "\n", | ||
| "  `python tools/infer.py --weights your_path/to/weights/best_ckpt.pt --yaml data/voc.yaml --source data/images/image3.jpg --device 0`\n", | ||
| "\n", | ||
| "\n", | ||
| "### 6. Deployment\n", | ||
| "\n", | ||
| "  `python deploy/ONNX/export_onnx.py --weights your_path/to/weights/best_ckpt.pt --device 0`" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "interpreter": { | ||
| "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": "Python 3.8.2 64-bit", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.0" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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