新手学习yolov8目标检测小记2--对比实验中经典模型库MMDetection使用方法（使用自己的数据集训练，并转换为yolo格式评价指标）

一、按照步骤环境配置

pip install timm==1.0.7 thop efficientnet_pytorch==0.7.1 einops grad-cam==1.4.8 dill==0.3.6 albumentations==1.4.11 pytorch_wavelets==1.3.0 tidecv PyWavelets -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -U openmim -i https://pypi.tuna.tsinghua.edu.cn/simple
mim install mmengine -i https://pypi.tuna.tsinghua.edu.cn/simple
mim install "mmcv==2.1.0" -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install YOLO
pip install ultralytics
pip install -v -e.

二、自定义数据集放置

        我这里已经将数据集按照训练集、验证集、测试集=8:1:1划分好，具体的存放目录结构如下图所示。其中test2017、train2017、val2017存放图片，testlabels、trainlabels、vallabels存放标注文件txt，数据集格式转换后，在annotations文件中。

        YOLO格式转coco格式代码如下

import os
import cv2
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import argparse# visdrone2019
classes = ['beibie1','beibie2','beibie3']parser = argparse.ArgumentParser()
parser.add_argument('--image_path', default=r'E:\mmde\mmdetection-3.0.0\mmdetection-3.0.0\data\coco\val2017', type=str, help="path of images")
parser.add_argument('--label_path', default=r'E:\mmde\mmdetection-3.0.0\mmdetection-3.0.0\data\coco\vallabels', type=str, help="path of labels .txt")
parser.add_argument('--save_path', default='val.json', type=str,help="if not split the dataset, give a path to a json file")
arg = parser.parse_args()def yolo2coco(arg):print("Loading data from ", arg.image_path, arg.label_path)assert os.path.exists(arg.image_path)assert os.path.exists(arg.label_path)originImagesDir = arg.image_pathoriginLabelsDir = arg.label_path# images dir nameindexes = os.listdir(originImagesDir)dataset = {'categories': [], 'annotations': [], 'images': []}for i, cls in enumerate(classes, 0):dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})# 标注的idann_id_cnt = 0for k, index in enumerate(tqdm(indexes)):# 支持 png jpg 格式的图片.txtFile = f'{index[:index.rfind(".")]}.txt'stem = index[:index.rfind(".")]# 读取图像的宽和高try:im = cv2.imread(os.path.join(originImagesDir, index))height, width, _ = im.shapeexcept Exception as e:print(f'{os.path.join(originImagesDir, index)} read error.\nerror:{e}')# 添加图像的信息if not os.path.exists(os.path.join(originLabelsDir, txtFile)):# 如没标签，跳过，只保留图片信息.continuedataset['images'].append({'file_name': index,'id': stem,'width': width,'height': height})with open(os.path.join(originLabelsDir, txtFile), 'r') as fr:labelList = fr.readlines()for label in labelList:label = label.strip().split()x = float(label[1])y = float(label[2])w = float(label[3])h = float(label[4])# convert x,y,w,h to x1,y1,x2,y2H, W, _ = im.shapex1 = (x - w / 2) * Wy1 = (y - h / 2) * Hx2 = (x + w / 2) * Wy2 = (y + h / 2) * H# 标签序号从0开始计算, coco2017数据集标号混乱，不管它了。cls_id = int(label[0])width = max(0, x2 - x1)height = max(0, y2 - y1)dataset['annotations'].append({'area': width * height,'bbox': [x1, y1, width, height],'category_id': cls_id,'id': ann_id_cnt,'image_id': stem,'iscrowd': 0,# mask, 矩形是从左上角点按顺时针的四个顶点'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]]})ann_id_cnt += 1# 保存结果with open(arg.save_path, 'w') as f:json.dump(dataset, f)print('Save annotation to {}'.format(arg.save_path))if __name__ == "__main__":yolo2coco(arg)

三、参数修改

        以faster-rcnn为例，查看文件configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py内容如下：

_base_ = ['../_base_/models/faster-rcnn_r50_fpn.py','../_base_/datasets/coco_detection.py','../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

        根据显示内容，修改具体配置。（‘../_base_/default_runtime.py’无需修改）

        （1）到‘../_base_/models/faster-rcnn_r50_fpn.py’修改

num_classes为自己的实际数据集类别数。

        （2）到‘../_base_/datasets/coco_detection.py’，修改

dataset_type = 'CocoDataset'
data_root = 'data/coco/'

        由于我使用的是coco数据集格式，只需要改data_root为自己数据集的位置即可。并修改

scale为自己的图像尺寸大小，我的是scale=(640, 640)。接下来根据数据集修改ann_file和data_prefix，根据如上数据集的位置放置，我的修改后该文件完整内容如下：

# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)# data_root = 's3://openmmlab/datasets/detection/coco/'# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
backend_args = Nonetrain_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='LoadAnnotations', with_bbox=True),dict(type='Resize', scale=(640, 640), keep_ratio=True),dict(type='RandomFlip', prob=0.5),dict(type='PackDetInputs')
]
test_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='Resize', scale=(640, 640), keep_ratio=True),# If you don't have a gt annotation, delete the pipelinedict(type='LoadAnnotations', with_bbox=True),dict(type='PackDetInputs',meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape','scale_factor'))
]
train_dataloader = dict(batch_size=2,num_workers=2,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=True),batch_sampler=dict(type='AspectRatioBatchSampler'),dataset=dict(type=dataset_type,data_root=data_root,ann_file='annotations/instances_train2017.json',data_prefix=dict(img='train2017/'),filter_cfg=dict(filter_empty_gt=True, min_size=32),pipeline=train_pipeline,backend_args=backend_args))
val_dataloader = dict(batch_size=1,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,ann_file='annotations/instances_val2017.json',data_prefix=dict(img='val2017/'),test_mode=True,pipeline=test_pipeline,backend_args=backend_args))
# test_dataloader = val_dataloaderval_evaluator = dict(type='CocoMetric',ann_file=data_root + 'annotations/instances_val2017.json',metric='bbox',format_only=False,backend_args=backend_args)
# test_evaluator = val_evaluator# inference on test dataset and
# format the output results for submission.
test_dataloader = dict(batch_size=2,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,ann_file=data_root + 'annotations/instances_test2017.json',data_prefix=dict(img='test2017/'),test_mode=True,pipeline=test_pipeline))
test_evaluator = dict(type='CocoMetric',metric='bbox',format_only=True,ann_file=data_root + 'annotations/instances_test2017.json',outfile_prefix='./work_dirs/coco_detection/test')

        （3）到‘../_base_/schedules/schedule_1x.py’修改max_epochs为自己设置的最大训练轮次，其他的val_interval、lr、momentum、weight_decay，如果没有特别的要求可不修改。哦，根据自己的电脑情况，别忘了改base_batch_size。该文件整体内容如下：

# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100, val_interval=10)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')# learning rate
param_scheduler = [dict(type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),dict(type='MultiStepLR',begin=0,end=100,by_epoch=True,milestones=[67, 92],gamma=0.1)
]# optimizer
optim_wrapper = dict(type='OptimWrapper',optimizer=dict(type='SGD', lr=0.01, momentum=0.937, weight_decay=0.0001))# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)

四、命令训练

        使用如下命令训练：

python  tools/train.py configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py

       如果训练中断，使用resume继续（注意‘--work-dir work_dirs/faster-rcnn_r50_fpn_1x_coco’是训练结果输出的位置，epoch_21.pth是上次训练中断后，输出的最后一个pth，根据自己的实际情况修改）：

python tools/train.py configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py  --work-dir work_dirs/faster-rcnn_r50_fpn_1x_coco --resume work_dirs/faster-rcnn_r50_fpn_1x_coco/epoch_21.pth

五、转为YOLO格式的评价指标

        （1）找出最佳epoch

import os
import subprocess
import pickle
import numpy as np
import json
from prettytable import PrettyTable
from tqdm import tqdm# 设置工作目录和模型文件路径
work_dir = "work_dirs/faster-rcnn_r50_fpn_1x_coco"
config_file = "configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py"  # 你的config文件路径# 存放模型权重文件（epoch_1.pth 到 epoch_100.pth）
checkpoint_dir = os.path.join(work_dir, "")  # 假设检查点文件在 'checkpoints' 子文件夹下# 遍历模型权重文件（epoch_1.pth, epoch_2.pth, ..., epoch_100.pth）
checkpoint_files = [f for f in os.listdir(checkpoint_dir) if f.endswith('.pth')]
checkpoint_files.sort(key=lambda x: int(x.split('_')[1].split('.')[0]))  # 按照 epoch 数字排序# 用于存储评估结果
results = []# 循环遍历每个模型文件
for checkpoint_file in tqdm(checkpoint_files, desc="Evaluating"):checkpoint_path = os.path.join(checkpoint_dir, checkpoint_file)# 设置输出 pkl 文件的路径output_pkl = f"res_{checkpoint_file.split('.')[0]}.pkl"# 运行 test.py 脚本进行模型评估command = ["python", "tools/test.py", config_file, checkpoint_path,"--out", output_pkl  # 输出 pkl 文件]# 使用 subprocess 运行命令并捕获输出result = subprocess.run(command, capture_output=True, text=True)# 调试输出print(f"Evaluating {checkpoint_file}...")print(result.stdout)# 假设输出的评估结果包含了 mAP# 解析 mAP，假设它包含在 stdout 中，例如 "bbox_mAP: 0.45"for line in result.stdout.splitlines():if "bbox_mAP" in line:try:# 提取 mAP 分数bbox_mAP = float(line.split(":")[-1].strip())epoch = int(checkpoint_file.split("_")[1].split(".")[0])  # 获取 epoch 数字results.append((epoch, bbox_mAP, output_pkl))  # 存储结果 (epoch, mAP, pkl文件)breakexcept ValueError:print(f"Error parsing bbox_mAP for {checkpoint_file}: {line}")continue# 计算并输出最佳 epoch 和 mAP
if results:# 根据 mAP 找到最佳的 epochbest_epoch, best_mAP, best_pkl = max(results, key=lambda x: x[1])print(f"Best Epoch: {best_epoch}, Best bbox_mAP: {best_mAP:.4f}, Best Output pkl: {best_pkl}")
else:print("No valid results found. Please check the log outputs.")# 计算所有 epoch 的 mAP 值
table = PrettyTable()
table.title = f"Evaluation Metrics"
table.field_names = ["Epoch", "bbox_mAP", "Output pkl"]# 添加每个 epoch 的评估结果
for epoch, mAP, pkl_file in results:table.add_row([epoch, f"{mAP:.4f}", pkl_file])print(table)

        （2）根据最佳epoch生成pkl文件 

python tools/test.py work_dirs/faster-rcnn_r50_fpn_1x_coco/faster-rcnn_r50_fpn_1x_coco.py work_dirs/faster-rcnn_r50_fpn_1x_coco/best_coco_bbox_mAP_epoch_90.pth --out res90.pkl

        （3）根据pkl文件输出对比参数，修改内容在‘def parse_opt():’，将内容改为实际的地址名称。

        完整代码如下：

import os, torch, cv2, math, tqdm, time, shutil, argparse, json, pickle
import numpy as np
from prettytable import PrettyTabledef clip_boxes(boxes, shape):# Clip boxes (xyxy) to image shape (height, width)if isinstance(boxes, torch.Tensor):  # faster individuallyboxes[..., 0].clamp_(0, shape[1])  # x1boxes[..., 1].clamp_(0, shape[0])  # y1boxes[..., 2].clamp_(0, shape[1])  # x2boxes[..., 3].clamp_(0, shape[0])  # y2else:  # np.array (faster grouped)boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):# Rescale boxes (xyxy) from img1_shape to img0_shapeif ratio_pad is None:  # calculate from img0_shapegain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / newpad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh paddingelse:gain = ratio_pad[0][0]pad = ratio_pad[1]boxes[..., [0, 2]] -= pad[0]  # x paddingboxes[..., [1, 3]] -= pad[1]  # y paddingboxes[..., :4] /= gainclip_boxes(boxes, img0_shape)return boxesdef box_iou(box1, box2, eps=1e-7):"""Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format.Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.pyArgs:box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes.box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes.eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.Returns:(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2."""# NOTE: Need .float() to get accurate iou values# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)(a1, a2), (b1, b2) = box1.float().unsqueeze(1).chunk(2, 2), box2.float().unsqueeze(0).chunk(2, 2)inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)# IoU = inter / (area1 + area2 - inter)return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)def process_batch(detections, labels, iouv):"""Return correct prediction matrixArguments:detections (array[N, 6]), x1, y1, x2, y2, conf, classlabels (array[M, 5]), class, x1, y1, x2, y2Returns:correct (array[N, 10]), for 10 IoU levels"""correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)iou = box_iou(labels[:, 1:], detections[:, :4])correct_class = labels[:, 0:1] == detections[:, 5]for i in range(len(iouv)):x = torch.where((iou >= iouv[i]) & correct_class)  # IoU > threshold and classes matchif x[0].shape[0]:matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detect, iou]if x[0].shape[0] > 1:matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 1], return_index=True)[1]]# matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 0], return_index=True)[1]]correct[matches[:, 1].astype(int), i] = Truereturn torch.tensor(correct, dtype=torch.bool, device=iouv.device)def smooth(y, f=0.05):# Box filter of fraction fnf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)p = np.ones(nf // 2)  # ones paddingyp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y paddedreturn np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smootheddef ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):""" Compute the average precision, given the recall and precision curves.Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.# Argumentstp:  True positives (nparray, nx1 or nx10).conf:  Objectness value from 0-1 (nparray).pred_cls:  Predicted object classes (nparray).target_cls:  True object classes (nparray).plot:  Plot precision-recall curve at mAP@0.5save_dir:  Plot save directory# ReturnsThe average precision as computed in py-faster-rcnn."""# Sort by objectnessi = np.argsort(-conf)tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]# Find unique classesunique_classes, nt = np.unique(target_cls, return_counts=True)nc = unique_classes.shape[0]  # number of classes, number of detections# Create Precision-Recall curve and compute AP for each classpx, py = np.linspace(0, 1, 1000), []  # for plottingap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))for ci, c in enumerate(unique_classes):i = pred_cls == cn_l = nt[ci]  # number of labelsn_p = i.sum()  # number of predictionsif n_p == 0 or n_l == 0:continue# Accumulate FPs and TPsfpc = (1 - tp[i]).cumsum(0)tpc = tp[i].cumsum(0)# Recallrecall = tpc / (n_l + eps)  # recall curver[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases# Precisionprecision = tpc / (tpc + fpc)  # precision curvep[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score# AP from recall-precision curvefor j in range(tp.shape[1]):ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])if plot and j == 0:py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5# Compute F1 (harmonic mean of precision and recall)f1 = 2 * p * r / (p + r + eps)i = smooth(f1.mean(0), 0.1).argmax()  # max F1 indexp, r, f1 = p[:, i], r[:, i], f1[:, i]tp = (r * nt).round()  # true positivesfp = (tp / (p + eps) - tp).round()  # false positivesreturn tp, fp, p, r, f1, ap, unique_classes.astype(int)def compute_ap(recall, precision):""" Compute the average precision, given the recall and precision curves# Argumentsrecall:    The recall curve (list)precision: The precision curve (list)# ReturnsAverage precision, precision curve, recall curve"""# Append sentinel values to beginning and endmrec = np.concatenate(([0.0], recall, [1.0]))mpre = np.concatenate(([1.0], precision, [0.0]))# Compute the precision envelopempre = np.flip(np.maximum.accumulate(np.flip(mpre)))# Integrate area under curvemethod = 'interp'  # methods: 'continuous', 'interp'if method == 'interp':x = np.linspace(0, 1, 101)  # 101-point interp (COCO)ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrateelse:  # 'continuous'i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changesap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curvereturn ap, mpre, mrecdef parse_opt():parser = argparse.ArgumentParser()parser.add_argument('--label_coco', type=str, default='E:/mmde/mmdetection-3.0.0/mmdetection-3.0.0/test.json',help='label coco path')# parser.add_argument('--pred_coco', type=str, default='runs/val/exp/predictions.json', help='pred coco path')parser.add_argument('--pred_coco', type=str, default='E:/mmde/mmdetection-3.0.0/mmdetection-3.0.0/res90.pkl', help='pred coco path')parser.add_argument('--iou', type=float, default=0.7, help='iou threshold')parser.add_argument('--conf', type=float, default=0.001, help='conf threshold')opt = parser.parse_known_args()[0]return optif __name__ == '__main__':opt = parse_opt()iouv = torch.linspace(0.5, 0.95, 10)  # iou vector for mAP@0.5:0.95niou = iouv.numel()stats = []label_coco_json_path, pred_coco_json_path = opt.label_coco, opt.pred_cocowith open(label_coco_json_path) as f:label = json.load(f)classes = []for data in label['categories']:classes.append(data['name'])image_id_hw_dict = {}for data in label['images']:image_id_hw_dict[data['id']] = [data['height'], data['width']]label_id_dict = {}for data in tqdm.tqdm(label['annotations'], desc='Process label...'):if data['image_id'] not in label_id_dict:label_id_dict[data['image_id']] = []category_id = data['category_id']x_min, y_min, w, h = data['bbox'][0], data['bbox'][1], data['bbox'][2], data['bbox'][3]x_max, y_max = x_min + w, y_min + hlabel_id_dict[data['image_id']].append(np.array([int(category_id), x_min, y_min, x_max, y_max]))if pred_coco_json_path.endswith('json'):with open(pred_coco_json_path) as f:pred = json.load(f)pred_id_dict = {}for data in tqdm.tqdm(pred, desc='Process pred...'):if data['image_id'] not in pred_id_dict:pred_id_dict[data['image_id']] = []score = data['score']category_id = data['category_id']x_min, y_min, w, h = data['bbox'][0], data['bbox'][1], data['bbox'][2], data['bbox'][3]x_max, y_max = x_min + w, y_min + hpred_id_dict[data['image_id']].append(np.array([x_min, y_min, x_max, y_max, float(score), int(category_id)]))else:with open(pred_coco_json_path, 'rb') as f:pred = pickle.load(f)pred_id_dict = {}for data in tqdm.tqdm(pred, desc='Process pred...'):image_id = os.path.splitext(os.path.basename(data['img_path']))[0]if image_id not in pred_id_dict:pred_id_dict[image_id] = []for i in range(data['pred_instances']['labels'].size(0)):score = data['pred_instances']['scores'][i]category_id = data['pred_instances']['labels'][i]bboxes = data['pred_instances']['bboxes'][i]x_min, y_min, x_max, y_max = bboxes.cpu().detach().numpy()# x_min, x_max = x_min / data['scale_factor'][0], x_max / data['scale_factor'][0]# y_min, y_max = y_min / data['scale_factor'][1], y_max / data['scale_factor'][1]pred_id_dict[image_id].append(np.array([x_min, y_min, x_max, y_max, float(score), int(category_id)]))for idx, image_id in enumerate(tqdm.tqdm(list(image_id_hw_dict.keys()), desc="Cal mAP...")):label = np.array(label_id_dict[image_id])if image_id not in pred_id_dict:pred = np.empty((0, 6))else:pred = torch.from_numpy(np.array(pred_id_dict[image_id]))nl, npr = label.shape[0], pred.shape[0]correct = torch.zeros(npr, niou, dtype=torch.bool)if npr == 0:if nl:stats.append((correct, *torch.zeros((2, 0)), torch.from_numpy(label[:, 0])))continueif nl:correct = process_batch(pred, torch.from_numpy(label), iouv)stats.append((correct, pred[:, 4], pred[:, 5], torch.from_numpy(label[:, 0])))stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)]tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats)print(f'precision:{p}')print(f'recall:{r}')print(f'mAP@0.5:{ap[:, 0]}')table = PrettyTable()table.title = f"Metrice"table.field_names = ["Classes", 'Precision', 'Recall', 'mAP50', 'mAP50-95']table.add_row(['all', f'{np.mean(p):.3f}', f'{np.mean(r):.3f}', f'{np.mean(ap[:, 0]):.3f}', f'{np.mean(ap):.3f}'])for cls_idx, classes in enumerate(classes):table.add_row([classes, f'{p[cls_idx]:.3f}', f'{r[cls_idx]:.3f}', f'{ap[cls_idx, 0]:.3f}',f'{ap[cls_idx, :].mean():.3f}'])print(table)

六、输出结果

        此外，查看FLOPs参数，用命令：

python tools/analysis_tools/get_flops.py work_dirs/faster-rcnn_r50_fpn_1x_coco-0.937-best0.965/faster-rcnn_r50_fpn_1x_coco.py

结果如下图：