yolov8-火灾检测

目标检测 PV:

环境

GPU

  • NVIDIA 3090*2
  • 显卡驱动 535.104.05
  • CUDA版本 12.2
  • CUDAtoolkit (cuda_12.2.2_535.104.05_linux)
  • cuDNN (v8.9.7)

yolo版本

  • v8.1.5 (ultralytics yolov8)

pytorch版本

  • v2.1.2

python环境

  • CentOS7.9
  • anaconda3
  • python3.9

安装

源码主页：https://github.com/ultralytics/ultralytics

官方文档：https://docs.ultralytics.com/zh

克隆源码

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git clone https://hub.nuaa.cf/ultralytics/ultralytics.git

安装依赖

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pip install pip install ultralytics -i https://mirror.baidu.com/pypi/simple

环境验证

python

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import ultralytics
ultralytics.checks()

cli

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yolo predict model=yolov8n.pt source=ultralytics/assets/zidane.jpg

执行完毕后得到输出的结果如下：

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(py39_yolov8) [root@jdz yolov8]# yolo predict model=yolov8n.pt source=ultralytics/assets/zidane.jpg 
Ultralytics YOLOv8.1.5 🚀 Python-3.9.18 torch-2.1.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB)
YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs

image 1/1 /exp/work/video/yolov8/ultralytics/assets/zidane.jpg: 384x640 2 persons, 1 tie, 216.9ms
Speed: 7.3ms preprocess, 216.9ms inference, 762.4ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict
💡 Learn more at https://docs.ultralytics.com/modes/predict

将在Results saved to runs/detect/predict目录下找到输出结果

至此，已验证基础环境正常工作。

训练集

前往kaggle官网寻找火焰训练集，这里使用Fire and Smoke Dataset数据集（https://www.kaggle.com/datasets/dataclusterlabs/fire-and-smoke-dataset）

数据集包含烟、火图像共100张

数据标注

标注工具：labelme

对图像中的火焰和烟雾进行矩形标记，将数据的label分为两类：0-火焰；1-烟雾

数据格式转换

将labelme数据格式转为yolo格式

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import os
import json
import shutil
import random
import numpy as np
from tqdm import tqdm
from settings import PR

# 数据集目录
Dataset_root = os.path.join(PR, 'datasets/protective_clothing')

os.chdir(fr'{Dataset_root}/images')
test_frac = 0.2  # 测试集比例
random.seed(123) # 随机数种子，便于复现

img_paths = os.listdir(fr'{Dataset_root}/images')
random.shuffle(img_paths) # 随机打乱

val_number = int(len(img_paths) * test_frac) # 测试集文件个数
train_files = img_paths[val_number:]         # 训练集文件名列表
val_files = img_paths[:val_number]           # 测试集文件名列表

print('数据集文件总数', len(img_paths))
print('训练集文件个数', len(train_files))
print('测试集文件个数', len(val_files))

if not os.path.exists('train'):
    os.mkdir('train')
    for each in tqdm(train_files):
        shutil.move(each, 'train')

if not os.path.exists('val'):
    os.mkdir('val')
    for each in tqdm(val_files):
        shutil.move(each, 'val')

os.chdir(fr'{Dataset_root}/labelme_jsons')

if not os.path.exists('train'):
    os.mkdir('train')
    for each in tqdm(train_files):
        srt_path = each.split('.')[0] + '.json'
        shutil.move(srt_path, 'train')

if not os.path.exists('val'):
    os.mkdir('val')
    for each in tqdm(val_files):
        srt_path = each.split('.')[0] + '.json'
        shutil.move(srt_path, 'val')


classes = {
    'unprotected': 0,
    'protected': 1
}

os.chdir(Dataset_root)

with open('classes.txt', 'w', encoding='utf-8') as f:
    for each in list(classes.keys()):
        f.write(each + '\n')

if not os.path.exists('labels'):
    os.mkdir('labels')
if not os.path.exists('labels/train'):
    os.mkdir('labels/train')
if not os.path.exists('labels/val'):
    os.mkdir('labels/val')


def process_single_json(labelme_path, save_folder=fr'{Dataset_root}/labels/train'):
    # 载入 labelme格式的 json 标注文件
    with open(labelme_path, 'r', encoding='utf-8') as f:
        labelme = json.load(f)

    img_width = labelme['imageWidth']  # 图像宽度
    img_height = labelme['imageHeight']  # 图像高度

    # 生成 YOLO 格式的 txt 文件
    suffix = labelme_path.split('.')[-2]
    yolo_txt_path = suffix + '.txt'

    with open(yolo_txt_path, 'w', encoding='utf-8') as f:
        for each_ann in labelme['shapes']:  # 遍历每个框

            if each_ann['shape_type'] == 'rectangle':  # 筛选出框

                # 获取类别 ID
                bbox_class_id = classes[each_ann['label']]

                # 左上角和右下角的 XY 像素坐标
                bbox_top_left_x = int(min(each_ann['points'][0][0], each_ann['points'][1][0]))
                bbox_bottom_right_x = int(max(each_ann['points'][0][0], each_ann['points'][1][0]))
                bbox_top_left_y = int(min(each_ann['points'][0][1], each_ann['points'][1][1]))
                bbox_bottom_right_y = int(max(each_ann['points'][0][1], each_ann['points'][1][1]))

                # 框中心点的 XY 像素坐标
                bbox_center_x = int((bbox_top_left_x + bbox_bottom_right_x) / 2)
                bbox_center_y = int((bbox_top_left_y + bbox_bottom_right_y) / 2)

                # 框宽度
                bbox_width = bbox_bottom_right_x - bbox_top_left_x

                # 框高度
                bbox_height = bbox_bottom_right_y - bbox_top_left_y

                # 框中心点归一化坐标
                bbox_center_x_norm = bbox_center_x / img_width
                bbox_center_y_norm = bbox_center_y / img_height

                # 框归一化宽度
                bbox_width_norm = bbox_width / img_width
                # 框归一化高度
                bbox_height_norm = bbox_height / img_height

                # 生成 YOLO 格式的一行标注，指定保留小数点后几位
                bbox_yolo_str = '{} {:.4f} {:.4f} {:.4f} {:.4f}'.format(bbox_class_id, bbox_center_x_norm,
                                                                        bbox_center_y_norm, bbox_width_norm,
                                                                        bbox_height_norm)
                # 写入 txt 文件中
                f.write(bbox_yolo_str + '\n')

    shutil.move(yolo_txt_path, save_folder)
    print('{} --> {} 转换完成'.format(labelme_path, yolo_txt_path))


os.chdir(fr'{Dataset_root}/labelme_jsons/train')

save_folder = fr'{Dataset_root}/labels/train'
for path in os.listdir():
    process_single_json(path, save_folder=save_folder)
print('YOLO格式的txt标注文件已保存至 ', save_folder)

os.chdir(fr'{Dataset_root}/labelme_jsons/val')
save_folder = fr'{Dataset_root}//labels/val'
for path in os.listdir():
    process_single_json(path, save_folder=save_folder)
print('YOLO格式的txt标注文件已保存至 ', save_folder)

创建训练yaml文件

在数据集的根目录下新建fire.yaml文件用于记录训练集信息

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train: /exp/work/video/yolov8/datasets/fire/data/train/images # 训练集
val: /exp/work/video/yolov8/datasets/fire/data/val/images # 验证集
test: /exp/work/video/yolov8/datasets/fire/data/test/images # 测试集
nc: 2 # 分类数量

names: [fire, smoke] # 类别名称

训练

在项目根目录下创建训练脚本yolov8_train.py，并设置双卡训练

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from ultralytics import YOLO

# 加载模型
model = YOLO('yolov8n.pt')  # 加载预训练模型

# 双卡训练
model.train(
    data='datasets/fire/data/fire.yaml',
    epochs=300,
    device=[0, 1])

# 启动验证
model.val()

启动训练脚本，开启训练

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python yolov8_train.py

打印配置信息

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Ultralytics YOLOv8.1.5 🚀 Python-3.9.18 torch-2.1.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB)
                                                          CUDA:1 (NVIDIA GeForce RTX 3090, 24260MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=datasets/fire/data/fire.yaml, epochs=300, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=[0, 1], workers=8, project=None, name=train9, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train9
Overriding model.yaml nc=80 with nc=2
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                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    751702  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
Model summary: 225 layers, 3011238 parameters, 3011222 gradients, 8.2 GFLOPs

Transferred 319/355 items from pretrained weights
DDP: debug command /root/anaconda3/envs/py39_yolov8/bin/python -m torch.distributed.run --nproc_per_node 2 --master_port 45027 /root/.config/Ultralytics/DDP/_temp_bfxihrb7140614302513712.py
WARNING:__main__:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
Ultralytics YOLOv8.1.5 🚀 Python-3.9.18 torch-2.1.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB)
                                                          CUDA:1 (NVIDIA GeForce RTX 3090, 24260MiB)
Overriding model.yaml nc=80 with nc=2
Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

记录模型输出

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Plotting labels to runs/detect/train9/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.000714, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 16 dataloader workers
Logging results to runs/detect/train9

训练开始

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Starting training for 300 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/300      1.38G      2.132      4.599       2.13          6        640: 100%|██████████| 3/3 [00:04<00:00,  1.53s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00,  3.10it/s]
                   all         41         62    0.00191      0.349     0.0223     0.0121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/300      1.44G      1.984      4.085      1.942         18        640: 100%|██████████| 3/3 [00:00<00:00,  5.08it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.57it/s]
                   all         41         62    0.00209      0.373     0.0247     0.0119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      3/300      1.48G      2.421      4.407      2.348         14        640: 100%|██████████| 3/3 [00:00<00:00,  5.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 10.23it/s]
                   all         41         62    0.00205      0.373     0.0335     0.0165

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      4/300      1.48G      1.795      3.787      1.724         18        640: 100%|██████████| 3/3 [00:00<00:00,  5.61it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00,  9.82it/s]
                   all         41         62    0.00239      0.434     0.0445     0.0219

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      5/300      1.48G      1.707      3.619      1.785         17        640: 100%|██████████| 3/3 [00:00<00:00,  5.49it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00,  8.87it/s]
                   all         41         62      0.003      0.495     0.0735     0.0283

......

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    295/300       1.5G     0.3742     0.5917     0.8247          7        640: 100%|██████████| 3/3 [00:00<00:00,  7.21it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.40it/s]
                   all         41         62      0.996          1      0.995      0.946

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    296/300      1.44G     0.4263     0.6716     0.8004          7        640: 100%|██████████| 3/3 [00:00<00:00,  6.86it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.97it/s]
                   all         41         62      0.996          1      0.995      0.945

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    297/300       1.5G      0.473     0.6775     0.8499          7        640: 100%|██████████| 3/3 [00:00<00:00,  7.20it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.12it/s]
                   all         41         62      0.996          1      0.995      0.945

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    298/300      1.51G     0.4877     0.6956     0.8936         11        640: 100%|██████████| 3/3 [00:00<00:00,  6.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.03it/s]
                   all         41         62      0.996          1      0.995      0.942

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    299/300       1.5G     0.4303      0.622     0.8264          6        640: 100%|██████████| 3/3 [00:00<00:00,  6.89it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.62it/s]
                   all         41         62      0.996          1      0.995      0.941

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    300/300      1.44G     0.4593     0.6413     0.8419          6        640: 100%|██████████| 3/3 [00:00<00:00,  6.31it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 11.83it/s]
                   all         41         62      0.995          1      0.995       0.94

训练结束，验证

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300 epochs completed in 0.101 hours.
Optimizer stripped from runs/detect/train9/weights/last.pt, 6.3MB
Optimizer stripped from runs/detect/train9/weights/best.pt, 6.3MB

Validating runs/detect/train9/weights/best.pt...
Ultralytics YOLOv8.1.5 🚀 Python-3.9.18 torch-2.1.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB)
                                                          CUDA:1 (NVIDIA GeForce RTX 3090, 24260MiB)
Model summary (fused): 168 layers, 3006038 parameters, 0 gradients, 8.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 10.62it/s]
                   all         41         62      0.996          1      0.995      0.946
                  fire         41         41      0.999          1      0.995      0.938
                 smoke         41         21      0.992          1      0.995      0.955
Speed: 0.1ms preprocess, 1.4ms inference, 0.0ms loss, 1.0ms postprocess per image
Results saved to runs/detect/train9
Ultralytics YOLOv8.1.5 🚀 Python-3.9.18 torch-2.1.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB)
                                                          CUDA:1 (NVIDIA GeForce RTX 3090, 24260MiB)
Model summary (fused): 168 layers, 3006038 parameters, 0 gradients, 8.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:04<00:00,  1.41s/it]
                   all         41         62      0.996          1      0.995      0.941
                  fire         41         41      0.999          1      0.995      0.939
                 smoke         41         21      0.992          1      0.995      0.942
Speed: 0.2ms preprocess, 14.3ms inference, 0.0ms loss, 16.8ms postprocess per image
Results saved to runs/detect/train92

评估

进入保存训练结果的文件夹内，其中weights文件夹包含了最佳训练模型和最近训练模型，文件夹外存储训练的各项记录和测试曲线等

result.csv文件中记录了300轮epoch的详细结果

预测

在项目根目录下创建预测脚本yolov8_test.py

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from ultralytics import YOLO

model = YOLO('runs/detect/train9/weights/best.pt') # 指定最佳模型
model.predict('video/fire.mp4', save=True, classes=[0, 1]) # 指定输出类别

执行

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video 1/1 (1/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 208.5ms
video 1/1 (2/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 9.6ms
video 1/1 (3/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.9ms
video 1/1 (4/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.9ms
video 1/1 (5/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 8.0ms
video 1/1 (6/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.8ms
video 1/1 (7/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.7ms
video 1/1 (8/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.6ms
video 1/1 (9/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.9ms
video 1/1 (10/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.8ms
video 1/1 (11/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 8.1ms
video 1/1 (12/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 8.1ms
video 1/1 (13/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 8.2ms
video 1/1 (14/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 8.0ms
video 1/1 (15/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.7ms
video 1/1 (16/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.8ms
video 1/1 (17/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 (no detections), 7.6ms
video 1/1 (18/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.6ms
video 1/1 (19/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.8ms
video 1/1 (20/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.6ms
video 1/1 (21/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.8ms
video 1/1 (22/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 2 fires, 7.7ms
video 1/1 (23/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.6ms
video 1/1 (24/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.5ms
video 1/1 (25/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.7ms
video 1/1 (26/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.5ms
video 1/1 (27/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.4ms
video 1/1 (28/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.6ms
video 1/1 (29/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.8ms
video 1/1 (30/306) /exp/work/video/yolov8/video/fire.mp4: 384x640 1 fire, 7.7ms
...
Speed: 2.2ms preprocess, 8.3ms inference, 3.8ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict13

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