一、创建项目
(1)进入到https://aistudio.baidu.com/aistudio/projectoverview/public
(2)创建项目
点击添加数据集:找到这两个
然后创建即可。
会生成以下项目:
二、启动环境,选择GPU版本
然后会进入到以下界面
选择的两个压缩包在/home/aistudio/data/下,先进行解压:
!unzip /home/aistudio/data/data15067/fruit.zip !unzip /home/aistudio/data/data15072/PaddleDetec.zip
之后在左边文件夹就可以看到解压后的内容了:
三、查看fruit-detection中的内容:
其实是类似pascal voc目标检测数据集的格式
(1) Annotations
以第一个apple_65.xml为例:
folder:文件夹名称
filename:图片名称
path:文件地址
size:图片的大小
object:图片中的对象名称以及其的左下角和右上角的坐标。
<annotation> <folder>train</folder> <filename>apple_65.jpg</filename> <path>C:\tensorflow1\models\research\object_detection\images\train\apple_65.jpg</path> <source> <database>Unknown</database> </source> <size> <width>800</width> <height>600</height> <depth>3</depth> </size> <segmented>0</segmented> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>70</xmin> <ymin>25</ymin> <xmax>290</xmax> <ymax>226</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>35</xmin> <ymin>217</ymin> <xmax>253</xmax> <ymax>453</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>183</xmin> <ymin>177</ymin> <xmax>382</xmax> <ymax>411</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>605</xmin> <ymin>298</ymin> <xmax>787</xmax> <ymax>513</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>498</xmin> <ymin>370</ymin> <xmax>675</xmax> <ymax>567</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>333</xmin> <ymin>239</ymin> <xmax>574</xmax> <ymax>463</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>191</xmin> <ymin>350</ymin> <xmax>373</xmax> <ymax>543</ymax> </bndbox> </object> <object> <name>apple</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>443</xmin> <ymin>425</ymin> <xmax>655</xmax> <ymax>598</ymax> </bndbox> </object> </annotation>
(2)ImageSets
里面只有一个文件夹Main,Main里面有:
分别看下是什么:
val.txt:验证集图片的名称
orange_92
banana_79
apple_94
apple_93
banana_81
banana_94
orange_77
mixed_23
orange_78
banana_85
apple_92
apple_79
apple_84
orange_83
apple_85
mixed_21
orange_91
orange_89
banana_80
apple_78
banana_93
mixed_22
orange_94
apple_83
banana_90
apple_77
orange_79
apple_81
orange_86
orange_95
banana_88
orange_85
orange_80
apple_80
apple_82
mixed_25
apple_88
banana_83
banana_77
banana_84
banana_92
banana_86
apple_87
orange_84
banana_78
orange_93
orange_90
banana_89
orange_82
apple_90
apple_95
banana_82
banana_91
mixed_24
banana_87
apple_91
orange_81
apple_89
apple_86
orange_87
train.txt:训练集图片的名称,这里就不贴了,有点长,与验证集类似
label_list.txt:类别名称
apple
banana
orange
也就是说,水果分类检测目前只是识别三类。
(3) JPEGImages:存储的就是实际的图片了
找一下apple_65.jpg看看
就是这个样子的
(4) create_list.py、label_list.txt、train.txt、val.txt
import os import os.path as osp import re import random devkit_dir = ‘./‘ years = [‘2007‘, ‘2012‘] def get_dir(devkit_dir, type): return osp.join(devkit_dir, type) def walk_dir(devkit_dir): filelist_dir = get_dir(devkit_dir, ‘ImageSets/Main‘) annotation_dir = get_dir(devkit_dir, ‘Annotations‘) img_dir = get_dir(devkit_dir, ‘JPEGImages‘) trainval_list = [] test_list = [] added = set() for _, _, files in os.walk(filelist_dir): for fname in files: img_ann_list = [] if re.match(‘train\.txt‘, fname): img_ann_list = trainval_list elif re.match(‘val\.txt‘, fname): img_ann_list = test_list else: continue fpath = osp.join(filelist_dir, fname) for line in open(fpath): name_prefix = line.strip().split()[0] if name_prefix in added: continue added.add(name_prefix) ann_path = osp.join(annotation_dir, name_prefix + ‘.xml‘) img_path = osp.join(img_dir, name_prefix + ‘.jpg‘) assert os.path.isfile(ann_path), ‘file %s not found.‘ % ann_path assert os.path.isfile(img_path), ‘file %s not found.‘ % img_path img_ann_list.append((img_path, ann_path)) return trainval_list, test_list def prepare_filelist(devkit_dir, output_dir): trainval_list = [] test_list = [] trainval, test = walk_dir(devkit_dir) trainval_list.extend(trainval) test_list.extend(test) random.shuffle(trainval_list) with open(osp.join(output_dir, ‘train.txt‘), ‘w‘) as ftrainval: for item in trainval_list: ftrainval.write(item[0] + ‘ ‘ + item[1] + ‘\n‘) with open(osp.join(output_dir, ‘val.txt‘), ‘w‘) as ftest: for item in test_list: ftest.write(item[0] + ‘ ‘ + item[1] + ‘\n‘) if __name__ == ‘__main__‘: prepare_filelist(devkit_dir, ‘.‘)
将标注信息转换为列表进行存储。
label_list.txt:还是那三种类别
train.txt:./JPEGImages/mixed_20.jpg ./Annotations/mixed_20.xml等一系列路径
val.txt:./JPEGImages/orange_92.jpg ./Annotations/orange_92.xml等一系列路径
至此fruit-dections中的内容就是这么多了。
四、查看PaddleDetection中的内容
(1) configs
各种网络的配置文件
找到yolov3_mobilenet_v1_fruit.yml看看
architecture: YOLOv3 train_feed: YoloTrainFeed eval_feed: YoloEvalFeed test_feed: YoloTestFeed use_gpu: true max_iters: 20000 log_smooth_window: 20 save_dir: output snapshot_iter: 200 metric: VOC map_type: 11point pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar weights: output/yolov3_mobilenet_v1_fruit/best_model num_classes: 3 finetune_exclude_pretrained_params: [‘yolo_output‘] YOLOv3: backbone: MobileNet yolo_head: YOLOv3Head MobileNet: norm_type: sync_bn norm_decay: 0. conv_group_scale: 1 with_extra_blocks: false YOLOv3Head: anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] norm_decay: 0. ignore_thresh: 0.7 label_smooth: true nms: background_label: -1 keep_top_k: 100 nms_threshold: 0.45 nms_top_k: 1000 normalized: false score_threshold: 0.01 LearningRate: base_lr: 0.00001 schedulers: - !PiecewiseDecay gamma: 0.1 milestones: - 15000 - 18000 - !LinearWarmup start_factor: 0. steps: 100 OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 type: L2 YoloTrainFeed: batch_size: 1 dataset: dataset_dir: dataset/fruit annotation: fruit-detection/train.txt use_default_label: false num_workers: 16 bufsize: 128 use_process: true mixup_epoch: -1 sample_transforms: - !DecodeImage to_rgb: true with_mixup: false - !NormalizeBox {} - !ExpandImage max_ratio: 4.0 mean: [123.675, 116.28, 103.53] prob: 0.5 - !RandomInterpImage max_size: 0 target_size: 608 - !RandomFlipImage is_mask_flip: false is_normalized: true prob: 0.5 - !NormalizeImage is_channel_first: false is_scale: true mean: - 0.485 - 0.456 - 0.406 std: - 0.229 - 0.224 - 0.225 - !Permute channel_first: true to_bgr: false batch_transforms: - !RandomShape sizes: [608] with_background: false YoloEvalFeed: batch_size: 1 image_shape: [3, 608, 608] dataset: dataset_dir: dataset/fruit annotation: fruit-detection/val.txt use_default_label: false YoloTestFeed: batch_size: 1 image_shape: [3, 608, 608] dataset: dataset_dir: dataset/fruit annotation: fruit-detection/label_list.txt use_default_label: false
注意标红的地方即可。
(2)contrib
行人检测和车辆检测?暂时不用管
(3)dataset: 各文件夹下有py文件,用于下载数据集的
(4)demo:用于检测结果的示例图片。
(5)docs:
(6)inference: 用于推断的‘?
(7) ppdet:paddlepaddle检测相关文件
(8) requirements.txt:所需的一些依赖
tqdm docstring_parser @ http://github.com/willthefrog/docstring_parser/tarball/master typeguard ; python_version >= ‘3.4‘ tb-paddle tb-nightly
(9)slim:应该是用于压缩模型的
(10) tools:工具
五、进行训练
训练的代码在tools中的train.py
进入到PaddleDection目录下
在终端输入:python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml --use_tb=True -- eval
如果发现错误No module named ppdet,在train.py中加入
import sys
sys.path.append("/home/aistudio/PaddleDection")即可
最后卡在了这,不过应该是训练完了,在PaddleDection目录下可以看到output文件夹:
里面有一个迭代时产生的权重信息:
六、进行测试一张图片
python -u tools/infer.py -c configs/yolov3_mobilenet_v1_fruit.yml -o weights=/home/aisudio/PaddleDetection/output/yolov3_mobilenet_v1_fruit/model_final --infer_img=demo/orange_71.jpg
会报错没有相关包,输入以下命令安装:
pip install docstring_parser
pip install pycocotools
之后:
去output下看看orange_71.jpg:
检测出来的是orange,准确率:94%。
知道了检测训练的整个流程,那么去手动标注poscal voc格式的数据,那么就可以实现检测自己想要的东西了。 然后也可以去看下相关目标检测的论文,明白其中的原理,看看源码之类的。
原文地址:https://www.cnblogs.com/xiximayou/p/12419567.html