{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Train YOLOX on COTS dataset (PART 1 - TRAINING)\n\nThis notebook shows how to train custom object detection model (COTS dataset) on Kaggle. It could be good starting point for build own custom model based on YOLOX detector. Full github repository you can find here - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)\n\n\n\n**Steps covered in this notebook:**\n* Install YOLOX \n* Prepare COTS dataset for YOLOX object detection training\n* Download Pre-Trained Weights for YOLOX\n* Prepare configuration files\n* YOLOX training\n* Run YOLOX inference on test images\n* Export YOLOX weights for Tensorflow inference (soon)\n\nNow I created notebook for learning and prototyping in YOLOX. Next step is too create better model (play with YOLOX experimentation parameters).","metadata":{"execution":{"iopub.status.busy":"2021-11-29T13:34:31.033138Z","iopub.execute_input":"2021-11-29T13:34:31.033449Z","iopub.status.idle":"2021-11-29T13:34:33.455468Z","shell.execute_reply.started":"2021-11-29T13:34:31.033368Z","shell.execute_reply":"2021-11-29T13:34:33.454141Z"}}},{"cell_type":"markdown","source":"\nI found that there is no reference custom model training YOLOX notebook on Kaggle (or I am bad in searching ... ). Since we have such an opportunity this is my contribution to this competition. Feel free to use it and enjoy!\n I really appreciate if you upvote this notebook. Thank you! \n
\n\n\n\nThis work consists of two parts: \n
\n \n
\n For YOLOX_s I use input size 960x960 but you can change it for your experiments. \n
","metadata":{}},{"cell_type":"code","source":"config_file_template = '''\n\n#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n def __init__(self):\n super(Exp, self).__init__()\n self.depth = 1.0 \n self.width = 1.0\n self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n \n # Define yourself dataset path\n self.data_dir = \"/kaggle/working/dataset/images\"\n self.train_ann = \"train.json\"\n self.val_ann = \"valid.json\"\n\n self.num_classes = 1 #目标类别数 为 1\n\n self.max_epoch = $max_epoch\n self.data_num_workers = 4 #几个线程\n self.eval_interval = 5 #训练每隔几个epoch 验证一次\n \n #self.mosaic_prob = 1.0 # mosaic增强的概率\n self.translate = 0.1 # 仿射变换\n #self.mixup_prob = 0.5 # mixup增强的概率\n #self.hsv_prob = 0.5 #色彩变换的概率\n self.flip_prob = 0.5 #翻转的概率\n self.no_aug_epochs = 10 #不做数据增强的轮次\n \n self.input_size = (960, 960)\n self.mosaic_scale = (0.5, 1.5)\n self.random_size = (10, 20)\n self.test_size = (960, 960)\n'''","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 3B. YOLOX-NANO CONFIG FILE\n\n For YOLOX_nano I use input size 460x460 but you can change it for your experiments. \n\n I trained model for 20 EPOCHS only .... This is for DEMO purposes only. \n
","metadata":{}},{"cell_type":"code","source":"PIPELINE_CONFIG_PATH='cots_config.py'\n\npipeline = Template(config_file_template).substitute(max_epoch = 20)\n\nwith open(PIPELINE_CONFIG_PATH, 'w') as f:\n f.write(pipeline)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# ./yolox/data/datasets/voc_classes.py\n\nvoc_cls = '''\nVOC_CLASSES = (\n \"starfish\",\n)\n'''\nwith open('./yolox/data/datasets/voc_classes.py', 'w') as f:\n f.write(voc_cls)\n\n# ./yolox/data/datasets/coco_classes.py\n\ncoco_cls = '''\nCOCO_CLASSES = (\n \"starfish\",\n)\n'''\nwith open('./yolox/data/datasets/coco_classes.py', 'w') as f:\n f.write(coco_cls)\n\n# check if everything is ok \n!more ./yolox/data/datasets/coco_classes.py","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. DOWNLOAD PRETRAINED WEIGHTS","metadata":{}},{"cell_type":"markdown","source":"List of pretrained models:\n* YOLOX-s\n* YOLOX-m\n* YOLOX-nano for inference speed (!)\n* etc.","metadata":{}},{"cell_type":"code","source":"sh = 'wget https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_l.pth'\nMODEL_FILE = 'yolox_l.pth'\n\nif NANO:\n sh = '''\n wget https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_nano.pth\n '''\n MODEL_FILE = 'yolox_nano.pth'\n\nwith open('script.sh', 'w') as file:\n file.write(sh)\n\n!bash script.sh","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5. TRAIN MODEL","metadata":{}},{"cell_type":"code","source":"!cp ./tools/train.py ./","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python train.py \\\n -f cots_config.py \\\n -d 1 \\\n -b 32 \\\n --fp16 \\\n -o \\\n -c {MODEL_FILE} # Remember to chenge this line if you take different model eg. yolo_nano.pth, yolox_s.pth or yolox_m.pth","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!cp -r YOLOX_outputs /kaggle/working","metadata":{},"execution_count":null,"outputs":[]}]} 
![](\"https://github.com/Megvii-BaseDetection/YOLOX/raw/main/assets/git_fig.png\"/)