网页策划方案,国外seo查询,梅州网站建设wlwl,上海城隍庙门票多少钱esp32-s3训练自己的数据进行目标检测、图像分类 一、下载项目二、环境三、训练和导出模型四、部署模型五、存在的问题 esp-idf的安装参考我前面的文章#xff1a;
esp32cam和esp32-s3烧录human_face_detect实现人脸识别 一、下载项目
训练、转换模型#xff1a;ModelAssist… esp32-s3训练自己的数据进行目标检测、图像分类 一、下载项目二、环境三、训练和导出模型四、部署模型五、存在的问题 esp-idf的安装参考我前面的文章
esp32cam和esp32-s3烧录human_face_detect实现人脸识别 一、下载项目
训练、转换模型ModelAssistant(main)部署模型sscma-example-esp32(1.0.0)说明文档sscma-model-zoo
二、环境
python3.8 CUDA11.7 esp-idf5.0
# 主要按照ModelAssistant/requirements_cuda.txt如果训练时有库不兼容的问题可参考下方
torch 2.0.0cu117
torchaudio 2.0.1cu117
torchvision 0.15.1cu117
yapf 0.40.2
typing_extensions 4.5.0
tensorboard 2.13.0
tensorboard-data-server 0.7.2
tensorflow 2.13.0
keras 2.13.1
tensorflow-estimator 2.13.0
tensorflow-intel 2.13.0
tensorflow-io-gcs-filesystem 0.31.0
sscma 2.0.0rc3
setuptools 60.2.0
rich 13.4.2
Pillow 9.4.0
mmcls 1.0.0rc6
mmcv 2.0.0
mmdet 3.0.0
mmengine 0.10.1
mmpose 1.2.0
mmyolo 0.5.0三、训练和导出模型
step 1: 将voc格式的标注文件转换为edgelab的训练格式并按82的比例划分为训练集和验证集
import os
import json
import pandas as pd
from xml.etree import ElementTree as ET
from PIL import Image
import shutil
import random
from tqdm import tqdm# Set paths
voc_path F:/datasets/VOCdevkit/VOC2007
train_path F:/edgelab/ModelAssistant/datasets/myself/train
valid_path F:/edgelab/ModelAssistant/datasets/meself/valid# 只读取有目标的且属于需要训练的类别
classes [face]# Create directories if not exist
if not os.path.exists(train_path):os.makedirs(train_path)
if not os.path.exists(valid_path):os.makedirs(valid_path)# Get list of image files
image_files os.listdir(os.path.join(voc_path, JPEGImages))
random.seed(0)
random.shuffle(image_files)# Split data into train and valid
train_files image_files[:int(len(image_files)*0.8)]
valid_files image_files[int(len(image_files)*0.8):]# Convert train data to COCO format
train_data {categories: [], images: [], annotations: []}
train_ann_id 0
train_cat_id 0
img_id 0
train_categories {}
for file in tqdm(train_files):# Add annotationsxml_file os.path.join(voc_path, Annotations, file[:-4] .xml)tree ET.parse(xml_file)root tree.getroot()for obj in root.findall(object):category obj.find(name).textif category not in classes:continueif category not in train_categories:train_categories[category] train_cat_idtrain_cat_id 1category_id train_categories[category]bbox obj.find(bndbox)x1 int(bbox.find(xmin).text)y1 int(bbox.find(ymin).text)x2 int(bbox.find(xmax).text)y2 int(bbox.find(ymax).text)width x2 - x1height y2 - y1ann_info {id: train_ann_id, image_id: img_id, category_id: category_id, bbox: [x1, y1, width, height],area: width*height, iscrowd: 0}train_data[annotations].append(ann_info)train_ann_id 1if len(root.findall(object)):# 只有有目标的图片才加进来image_id img_idimg_id 1image_file os.path.join(voc_path, JPEGImages, file)shutil.copy(image_file, os.path.join(train_path, file))img Image.open(image_file)image_info {id: image_id, file_name: file, width: img.size[0], height: img.size[1]}train_data[images].append(image_info)# Add categories
for category, category_id in train_categories.items():train_data[categories].append({id: category_id, name: category})# Save train data to file
with open(os.path.join(train_path, _annotations.coco.json), w) as f:json.dump(train_data, f, indent4)# Convert valid data to COCO format
valid_data {categories: [], images: [], annotations: []}
valid_ann_id 0
img_id 0
for file in tqdm(valid_files):# Add annotationsxml_file os.path.join(voc_path, Annotations, file[:-4] .xml)tree ET.parse(xml_file)root tree.getroot()for obj in root.findall(object):category obj.find(name).textif category not in classes:continuecategory_id train_categories[category]bbox obj.find(bndbox)x1 int(bbox.find(xmin).text)y1 int(bbox.find(ymin).text)x2 int(bbox.find(xmax).text)y2 int(bbox.find(ymax).text)width x2 - x1height y2 - y1ann_info {id: valid_ann_id, image_id: img_id, category_id: category_id, bbox: [x1, y1, width, height],area: width*height, iscrowd: 0}valid_data[annotations].append(ann_info)valid_ann_id 1if len(root.findall(object)):# Add imageimage_id img_idimg_id 1image_file os.path.join(voc_path, JPEGImages, file)shutil.copy(image_file, os.path.join(valid_path, file))img Image.open(image_file)image_info {id: image_id, file_name: file, width: img.size[0], height: img.size[1]}valid_data[images].append(image_info)# Add categories
valid_data[categories] train_data[categories]# Save valid data to file
with open(os.path.join(valid_path, _annotations.coco.json), w) as f:json.dump(valid_data, f, indent4)step 2: 参考Face Detection - Swift-YOLO下载模型权重文件和训练
python tools/train.py configs/yolov5/yolov5_tiny_1xb16_300e_coco.py \
--cfg-options \work_dirwork_dirs/face_96 \num_classes3 \epochs300 \height96 \width96 \batch128 \data_rootdatasets/face/ \load_fromdatasets/face/pretrain.pthstep 3: 训练过程可视化tensorboard
cd work_dirs/face_96/20231219_181418/vis_data
tensorboard --logdir./然后按照提示打开http://localhost:6006/
step 4: 导出模型
python tools/export.py configs/yolov5/yolov5_tiny_1xb16_300e_coco.py ./work_dirs/face_96/best_coco_bbox_mAP_epoch_300.pth --target tflite onnx
--cfg-options \work_dirwork_dirs/face_96 \num_classes3 \epochs300 \height96 \width96 \batch128 \data_rootdatasets/face/ \load_fromdatasets/face/pretrain.pth这样就会在./work_dirs/face_96路径下生成best_coco_bbox_mAP_epoch_300_int8.tflite文件了。
四、部署模型
step 1: 将best_coco_bbox_mAP_epoch_300_int8.tflite复制到F:\edgelab\sscma-example-esp32-1.0.0\model_zoo路径下step 2: 参照edgelab-example-esp32-训练和部署一个FOMO模型将模型转换为C语言文件并将其放入到F:\edgelab\sscma-example-esp32-1.0.0\components\modules\model路径下
python tools/tflite2c.py --input ./model_zoo/best_coco_bbox_mAP_epoch_300_int8.tflite --name yolo --output_dir ./components/modules/model --classes face这样会生成./components/modules/model/yolo_model_data.cpp和yolo_model_data.h两个文件。
step 3: 利用idf烧录程序
fb_gfx_printf(frame, yolo.x - yolo.w / 2, yolo.y - yolo.h/2 - 5, 0x1FE0, %s:%d, g_yolo_model_classes[yolo.target], yolo.confidence);打开esp-idf cmd
cd F:\edgelab\sscma-example-esp32-1.0.0\examples\yolo
idf.py set-target esp32s3
idf.py menuconfig勾选上方的这个选项不然报错
E:/Softwares/Espressif/frameworks/esp-idf-v5.0.4/components/driver/deprecated/driver/i2s.h:27:2: warning: #warning This set of I2S APIs has been deprecated, please include driver/i2s_std.h, driver/i2s_pdm.h or driver/i2s_tdm.h instead. if you want to keep using the old APIs and ignore this warning, you can enable Suppress leagcy driver deprecated warning option under I2S Configuration menu in Kconfig [-Wcpp]27 | #warning This set of I2S APIs has been deprecated, \| ^~~~~~~
ninja: build stopped: subcommand failed.
ninja failed with exit code 1, output of the command is in the F:\edgelab\sscma-example-esp32-1.0.0\examples\yolo\build\log\idf_py_stderr_output_27512 and F:\edgelab\sscma-example-esp32-1.0.0\examples\yolo\build\log\idf_py_stdout_output_27512idf.py flash monitor -p COM3lcd端也能实时显示识别结果输入大小为96x96时推理时间大概200ms192x192时时间大概660ms
五、存在的问题
该链路中量化是比较简单的在我的数据集上量化后精度大打折扣应该需要修改量化算法后续再说吧。
量化前 量化后
