本文主要是介绍使用 KITTI数据集训练YOLOX，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
1. 现在KITTI集后，首先将数据集转换为COCO数据集格式。
kitti_vis.py
import os
from pathlib import Path
import numpy as np
import cv2def anno_vis(img, anno_list):for anno in anno_list:points = np.array(anno[4:8], dtype=np.float32)cv2.rectangle(img, (int(points[0]),int(points[1])), (int(points[2]),int(points[3])), (0, 0, 255), 2)cv2.putText(img, anno[0], (int(points[0]),int(points[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)cv2.imshow('img', img)ret = cv2.waitKey(0)if ret == 27:exit(0)if __name__ == '__main__':img_root = Path(r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Image\training\image_2')label_root = Path(r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Label\training\label_2')img_list = os.listdir(img_root)for img_name in img_list[:5]:img_name = Path(img_name)label_name = img_name.with_suffix('.txt')img = cv2.imread(str(img_root/img_name))with open(label_root/label_name) as f:l = [x.split() for x in f.read().strip().splitlines()]anno_vis(img, l)kitti_split.py
'''
用于将KITTI数据集的7000多张训练集分为：前4000张为训练集，4000-6000张为验证集，剩余为测试集
运行命令：
python ./tools/kitti_split.py --source_img_path ./KITTI_origin/training/image_2 --source_label_path ./KITTI_origin/training/label_2/
--dst_img_path ./KITTI_YOLOX/img --dst_label_path ./KITTI_YOLOX/label# img_root = Path(r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Image\training\image_2')# label_root = Path(r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Label\training\label_2')'''import os
import argparse
from pathlib import Path
import shutil
from tqdm import tqdm
from loguru import loggerdef make_parser():parser = argparse.ArgumentParser("") parser.add_argument('--source_img_path', default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Image\training\image_2', help="Specify original kitti img path")parser.add_argument('--source_label_path', default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Label\training\label_2',help="Specify original kitti label path")parser.add_argument('--dst_img_path', default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img',help="Specify splited kitti img path")parser.add_argument('--dst_label_path', default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\label',help="Specify splited kitti label path")return parserdef check_dir(dir):if Path(dir).is_dir() == False:Path(dir).mkdir(parents=True, exist_ok=True)logger.info('Created %s' % dir)if __name__ == '__main__':args = make_parser().parse_args()img_root = Path(args.source_img_path)label_root = Path(args.source_label_path)# img_root = Path(r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Image\training\image_2')# label_root = Path(r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI-train_test-Label\training\label_2')img_list = os.listdir(img_root)dst_train_img_root = Path(args.dst_img_path)/'train'dst_val_img_root = Path(args.dst_img_path)/'val'dst_test_img_root = Path(args.dst_img_path)/'test'dst_train_label_root = Path(args.dst_label_path)/'train'dst_val_label_root = Path(args.dst_label_path)/'val'dst_test_label_root = Path(args.dst_label_path)/'test'check_dir(dst_train_img_root)check_dir(dst_val_img_root)check_dir(dst_test_img_root)check_dir(dst_train_label_root)check_dir(dst_val_label_root)check_dir(dst_test_label_root)for img_name in tqdm(img_list):if int(Path(img_name).stem) < 4000:shutil.copyfile(img_root/img_name, dst_train_img_root/img_name)shutil.copyfile(label_root/(Path(img_name).with_suffix('.txt')), dst_train_label_root/(Path(img_name).with_suffix('.txt')))elif int(Path(img_name).stem) < 6000:shutil.copyfile(img_root/img_name, dst_val_img_root/img_name)shutil.copyfile(label_root/(Path(img_name).with_suffix('.txt')), dst_val_label_root/(Path(img_name).with_suffix('.txt')))else:shutil.copyfile(img_root/img_name, dst_test_img_root/img_name)shutil.copyfile(label_root/(Path(img_name).with_suffix('.txt')), dst_test_label_root/(Path(img_name).with_suffix('.txt')))kitti2coco.py
'''
KITTI标注转COCO标注运行命令：（1）训练集：python tools/kitti2coco.py --img_path ./KITTI_YOLOX/img/train --label_path ./KITTI_YOLOX/label/train --dst_json ./train.json
（2）验证集：python tools/kitti2coco.py --img_path ./KITTI_YOLOX/img/val --label_path ./KITTI_YOLOX/label/val --dst_json ./val.json
（3）测试集：python tools/kitti2coco.py --img_path ./KITTI_YOLOX/img/test --label_path ./KITTI_YOLOX/label/test --dst_json ./test.json
'''
import os
import json
import argparse
from pathlib import Path
import cv2
from tqdm import tqdm# parser.add_argument('--dst_img_path', default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img',
# help="Specify splited kitti img path")
# parser.add_argument('--dst_label_path', default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\label',
# help="Specify splited kitti label path")def make_parser():# parser = argparse.ArgumentParser("Kitti to COCO format")# parser.add_argument('--img_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img\train',# help='Specify img path')# parser.add_argument('--label_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\label\train',# help='Specify label path')# parser.add_argument('--dst_json', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\train.json', help='Specify generated json file name')# parser = argparse.ArgumentParser("Kitti to COCO format")# parser.add_argument('--img_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img\test',# help='Specify img path')# parser.add_argument('--label_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\label\test',# help='Specify label path')# parser.add_argument('--dst_json', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\test.json', help='Specify generated json file name')#parser = argparse.ArgumentParser("Kitti to COCO format")parser.add_argument('--img_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img\val',help='Specify img path')parser.add_argument('--label_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\label\val',help='Specify label path')parser.add_argument('--dst_json', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\val.json', help='Specify generated json file name')return parserif __name__ == '__main__':args = make_parser().parse_args()img_root = Path(args.img_path)label_root = Path(args.label_path)category_dict = {1:'Car', 2:'Van', 3:'Pedestrian', 4:'Person_sitting', 5:'Truck', 6:'Cyclist', 7:'Tram'}category_name2id_dict = {v:k for k, v in category_dict.items()}img_list = os.listdir(img_root)img_id = 0anno_id = 0json_images_list = list()json_annotations_list = list()json_categories_list = list()for img_name in tqdm(img_list):img = cv2.imread(str(img_root/img_name))img_height, img_width, _ = img.shapeimg_dict = {'license': None,'file_name': img_name,'coco_url': None,'height': img_height, 'width': img_width, 'date_captured': None, 'flickr_url': None,'id': img_id}json_images_list.append(img_dict)label_name = Path(img_name).with_suffix('.txt')with open(label_root/label_name) as f:anno_list = [x.split() for x in f.read().strip().splitlines()]for anno in anno_list:if anno[0] in category_name2id_dict:bbox = [float(anno[4]), float(anno[5]), float(anno[6])-float(anno[4]), float(anno[7])-float(anno[5])] # anno[4:8]area = bbox[2]*bbox[3]anno_dict = {'segmentation': None,'area': area,'iscrowd': 0,'image_id': img_id,'bbox': bbox, 'category_id': category_name2id_dict[anno[0]],'id': anno_id}json_annotations_list.append(anno_dict)anno_id += 1img_id += 1for id in category_dict:json_categories_list.append({'supercategory': None,'id': id,'name': category_dict[id]})json_dict = {'images': json_images_list,'annotations': json_annotations_list,'categories': json_categories_list}with open(args.dst_json,"w") as f:json.dump(json_dict,f)COCO_vis.py
'''
验证转换后的json格式标注的准确性。
运行命令：python tools/COCO_vis.py --img_root ./KITTI_YOLOX/img/train --label_file ./KITTI_YOLOX/train.json
'''import argparse
from pathlib import Path
import numpy as np
import cv2
from pycocotools.coco import COCO# parser.add_argument('--img_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img\val',
# help='Specify img path')
# parser.add_argument('--label_path', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\label\val',
# help='Specify label path')
# parser.add_argument('--dst_json', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\val.json',
# help='Specify generated json file name')def make_parser():parser = argparse.ArgumentParser("") parser.add_argument('--img_root', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\KITTI_YOLOX\img\train', help='Specify img path')parser.add_argument('--label_file', type=str, default=r'D:\BaiduNetdiskDownload\CV\KITTI\train.json', help='Specify COCO format label file')return parserif __name__ == '__main__':args = make_parser().parse_args()img_root = args.img_rootanno_file = args.label_filecoco = COCO(anno_file)img_ids = coco.getImgIds()category_list = coco.loadCats(coco.getCatIds())label_id2name = dict([(item['id'], item['name']) for item in category_list])for img_id in img_ids:img_info = coco.loadImgs(img_id)[0]print('img name: ', str(Path(img_root)/img_info['file_name']))img = cv2.imread(str(Path(img_root)/img_info['file_name']))img_width = img_info["width"]img_height = img_info["height"]anno_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=False)result_anno_list = list()for anno_id in anno_ids:annotation = coco.loadAnns(anno_id)x1 = np.max((0, annotation[0]["bbox"][0]))y1 = np.max((0, annotation[0]["bbox"][1]))x2 = np.min((img_width, x1 + np.max((0, annotation[0]["bbox"][2]))))y2 = np.min((img_height, y1 + np.max((0, annotation[0]["bbox"][3]))))label = label_id2name[annotation[0]['category_id']]result_anno_list.append([label, x1, y1, x2, y2])cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0,0,255), 1)cv2.putText(img, label, (int(x1), int(y1)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (128,255,255))cv2.imshow('img', img)ret = cv2.waitKey(0) if ret == 27:exit(0)2.按照训练COCO数据集合的指令训练KITTI即可
python -m yolox.tools.train -n yolox-s -d 1 -b 32 --fp16
或者： python -m yolox.tools.train -f exps/default/yolox_s.py -d 1 -b 32 --fp
16
olox) xuefei@f123:/mnt/d/work/study/detect/7$
(yolox) xuefei@f123:/mnt/d/work/study/detect/7$ python -m yolox.tools.train -f exps/kitti_car_detection/yolox_s.py -d 1 -b 16 --fp16
2024-02-05 23:08:04 | INFO | yolox.core.trainer:130 - args: Namespace(batch_size=16, cache=False, ckpt=None, devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/kitti_car_detection/yolox_s.py', experiment_name='yolox_s', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=False, opts=[], resume=False, start_epoch=None)
2024-02-05 23:08:04 | INFO | yolox.core.trainer:131 - exp value:
╒═══════════════════╤═══════════════════════════════════════════════════════════════╕
│ keys │ values │
╞═══════════════════╪═══════════════════════════════════════════════════════════════╡
│ seed │ None │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ output_dir │ './YOLOX_outputs' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ print_interval │ 10 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ eval_interval │ 10 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ num_classes │ 7 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ depth │ 0.33 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ width │ 0.5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ act │ 'silu' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ data_num_workers │ 16 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ input_size │ (256, 832) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ multiscale_range │ 5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ data_dir │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/img/' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ train_ann │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/train.json' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ val_ann │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/val.json' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ test_ann │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/test.json' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mosaic_prob │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mixup_prob │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ hsv_prob │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ flip_prob │ 0.5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ degrees │ 10.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ translate │ 0.1 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mosaic_scale │ (0.1, 2) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ enable_mixup │ True │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mixup_scale │ (0.5, 1.5) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ shear │ 2.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ warmup_epochs │ 5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ max_epoch │ 300 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ warmup_lr │ 0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ min_lr_ratio │ 0.05 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ basic_lr_per_img │ 0.00015625 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ scheduler │ 'yoloxwarmcos' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ no_aug_epochs │ 80 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ ema │ True │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ weight_decay │ 0.0005 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ momentum │ 0.9 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ save_history_ckpt │ True │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ exp_name │ 'yolox_s' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ test_size │ (256, 832) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ test_conf │ 0.01 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ nmsthre │ 0.65 │
╘═══════════════════╧═══════════════════════════════════════════════════════════════╛
2024-02-05 23:08:05 | INFO | yolox.core.trainer:137 - Model Summary: Params: 8.94M, Gflops: 13.92
2024-02-05 23:08:07 | INFO | yolox.data.datasets.kitti:64 - loading annotations into memory...
2024-02-05 23:08:07 | INFO | yolox.data.datasets.kitti:64 - Done (t=0.05s)
2024-02-05 23:08:07 | INFO | pycocotools.coco:86 - creating index...
2024-02-05 23:08:07 | INFO | pycocotools.coco:86 - index created!
2024-02-05 23:08:08 | INFO | yolox.core.trainer:155 - init prefetcher, this might take one minute or less...
2024-02-05 23:08:17 | INFO | yolox.data.datasets.kitti:64 - loading annotations into memory...
2024-02-05 23:08:17 | INFO | yolox.data.datasets.kitti:64 - Done (t=0.05s)
2024-02-05 23:08:17 | INFO | pycocotools.coco:86 - creating index...
2024-02-05 23:08:17 | INFO | pycocotools.coco:86 - index created!
2024-02-05 23:08:17 | INFO | yolox.core.trainer:191 - Training start...
2024-02-05 23:08:17 | INFO | yolox.core.trainer:192 -
YOLOX((backbone): YOLOPAFPN((backbone): CSPDarknet((stem): Focus((conv): BaseConv((conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(dark2): Sequential((0): BaseConv((conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): CSPLayer((conv1): BaseConv((conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))))))(dark3): Sequential((0): BaseConv((conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): CSPLayer((conv1): BaseConv((conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(1): Bottleneck((conv1): BaseConv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(2): Bottleneck((conv1): BaseConv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))))))(dark4): Sequential((0): BaseConv((conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): CSPLayer((conv1): BaseConv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(1): Bottleneck((conv1): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(2): Bottleneck((conv1): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))))))(dark5): Sequential((0): BaseConv((conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): SPPBottleneck((conv1): BaseConv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): ModuleList((0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)(1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)(2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False))(conv2): BaseConv((conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(2): CSPLayer((conv1): BaseConv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))))))(upsample): Upsample(scale_factor=2.0, mode=nearest)(lateral_conv0): BaseConv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(C3_p4): CSPLayer((conv1): BaseConv((conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))))(reduce_conv1): BaseConv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(C3_p3): CSPLayer((conv1): BaseConv((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))))(bu_conv2): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(C3_n3): CSPLayer((conv1): BaseConv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))))(bu_conv1): BaseConv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(C3_n4): CSPLayer((conv1): BaseConv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv3): BaseConv((conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((conv1): BaseConv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(conv2): BaseConv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))))))(head): YOLOXHead((cls_convs): ModuleList((0): Sequential((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(1): Sequential((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(2): Sequential((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))))(reg_convs): ModuleList((0): Sequential((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(1): Sequential((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(2): Sequential((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))))(cls_preds): ModuleList((0): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1))(1): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1))(2): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1)))(reg_preds): ModuleList((0): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))(1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)))(obj_preds): ModuleList((0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))(1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)))(stems): ModuleList((0): BaseConv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(1): BaseConv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True))(2): BaseConv((conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)(act): SiLU(inplace=True)))(l1_loss): L1Loss()(bcewithlog_loss): BCEWithLogitsLoss()(iou_loss): IOUloss())
)
2024-02-05 23:08:17 | INFO | yolox.core.trainer:203 - ---> start train epoch1
2024-02-05 23:08:22 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 10/250, mem: 2730Mb, iter_time: 0.523s, data_time: 0.001s, total_loss: 17.5, iou_loss: 4.7, l1_loss: 3.0, conf_loss: 8.8, cls_loss: 1.0, lr: 1.600e-07, size: 256, ETA: 10:54:05
2024-02-05 23:08:27 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 20/250, mem: 2730Mb, iter_time: 0.478s, data_time: 0.223s, total_loss: 13.0, iou_loss: 4.7, l1_loss: 2.3, conf_loss: 5.1, cls_loss: 1.0, lr: 6.400e-07, size: 96, ETA: 10:25:36
2024-02-05 23:08:37 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 30/250, mem: 4257Mb, iter_time: 0.950s, data_time: 0.001s, total_loss: 22.2, iou_loss: 4.7, l1_loss: 3.0, conf_loss: 13.5, cls_loss: 1.0, lr: 1.440e-06, size: 416, ETA: 13:32:40
2024-02-05 23:08:43 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 40/250, mem: 4259Mb, iter_time: 0.676s, data_time: 0.001s, total_loss: 21.0, iou_loss: 4.7, l1_loss: 3.0, conf_loss: 12.3, cls_loss: 1.0, lr: 2.560e-06, size: 416, ETA: 13:40:40
2024-02-05 23:08:45 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 50/250, mem: 4259Mb, iter_time: 0.210s, data_time: 0.001s, total_loss: 13.2, iou_loss: 4.8, l1_loss: 2.6, conf_loss: 5.0, cls_loss: 0.8, lr: 4.000e-06, size: 96, ETA: 11:48:55
2024-02-05 23:08:52 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 60/250, mem: 4259Mb, iter_time: 0.646s, data_time: 0.001s, total_loss: 18.1, iou_loss: 4.7, l1_loss: 2.6, conf_loss: 9.8, cls_loss: 1.0, lr: 5.760e-06, size: 320, ETA: 12:05:09
2024-02-05 23:08:59 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 70/250, mem: 4279Mb, iter_time: 0.714s, data_time: 0.027s, total_loss: 20.1, iou_loss: 4.7, l1_loss: 2.7, conf_loss: 11.7, cls_loss: 1.0, lr: 7.840e-06, size: 416, ETA: 12:28:52
2024-02-05 23:09:04 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 80/250, mem: 4279Mb, iter_time: 0.478s, data_time: 0.047s, total_loss: 14.9, iou_loss: 4.7, l1_loss: 2.8, conf_loss: 6.4, cls_loss: 1.1, lr: 1.024e-05, size: 224, ETA: 12:09:45
2024-02-05 23:09:06 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 90/250, mem: 4279Mb, iter_time: 0.184s, data_time: 0.001s, total_loss: 12.9, iou_loss: 4.7, l1_loss: 2.2, conf_loss: 5.1, cls_loss: 0.9, lr: 1.296e-05, size: 96, ETA: 11:14:07
2024-02-05 23:09:15 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 100/250, mem: 4279Mb, iter_time: 0.949s, data_time: 0.259s, total_loss: 20.1, iou_loss: 4.7, l1_loss: 2.7, conf_loss: 11.7, cls_loss: 1.1, lr: 1.600e-05, size: 352, ETA: 12:05:03
2024-02-05 23:09:18 | INFO | yolox.core.trainer:261 - epoch: 1/300, iter: 110/250, mem: 4279Mb, iter_time: 0.248s, data_time: 0.001s, total_loss: 13.7, iou_loss: 4.7, l1_loss: 2.6, conf_loss: 5.4, cls_loss: 1.0, lr: 1.936e-05, size: 128, ETA: 11:27:11这篇关于使用 KITTI数据集训练YOLOX的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
