# tensorflow-yolo **Repository Path**: xielinjiangs/tensorflow-yolo ## Basic Information - **Project Name**: tensorflow-yolo - **Description**: tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test) - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: python2.7 - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-11-05 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # tensorflow-yolo ### Require tensorflow-1.0 ### download pretrained model yolo_tiny: https://drive.google.com/file/d/0B-yiAeTLLamRekxqVE01Yi1RRlk/view?usp=sharing ``` mv yolo_tiny.ckpt models/pretrain/ ``` ### Train #### Train on pascal-voc2007 data ##### Download pascal-Voc2007 data 1. Download the training, validation and test data ``` wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar ``` 2. Extract all of these tars into one directory named `VOCdevkit` ``` tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar ``` 3. It should have this basic structure ``` $VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. # ... and several other directories ... ``` 4. Create symlinks for the PASCAL VOC dataset ``` cd $YOLO_ROOT/data ln -s $VOCdevkit VOCdevkit2007 ``` Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects. #### convert the Pascal-voc data to text_record file ``` python tools/preprocess_pascal_voc.py ``` #### train ``` python tools/train.py -c conf/train.cfg ``` #### Train your customer data 1. transform your training data to text_record file(the format reference to pascal_voc) 2. write your own train-configure file 3. train (python tools/train.py -c $your_configure_file) ### test demo ``` python demo.py ```