a simple demo of yolov8 on BDD100K.
Download BDD100K
BaiduNetDisk: https://pan.baidu.com/s/1fFSzGJt6Op4k7Gyo9QjtYA key: kuld
or our mini dataset bdd100.zip
unzip bdd100.zip
mkdir bdd100
mv images-mini bdd100
mv labels-mini bdd100Preprocess bdd100
labels: json to xml, xml to txt
python json_xml.py
python xml_txt.pybuild mydata
python build_data.pycreate yml
touch mydata.ymlgit clone [email protected]:ultralytics/ultralytics.gitrun train example:
yolo task=detect mode=train model=yolov8n.pt data=mydata.yml batch=32 epochs=10 imgsz=640 workers=10 device=0Ultralytics YOLOv8.2.27 🚀 Python-3.9.19 torch-2.3.0+cu121 CUDA:0 (NVIDIA A100-PCIE-40GB, 40377MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=mydata.yml, epochs=10, time=None, patience=100, batch=32, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=10, project=None, name=train2, 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, bgr=0.0, 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/train2
runOverriding model.yaml nc=80 with nc=10
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 753262 ultralytics.nn.modules.head.Detect [10, [64, 128, 256]]
Model summary: 225 layers, 3012798 parameters, 3012782 gradients, 8.2 GFLOPs
...
10 epochs completed in 0.044 hours.
Optimizer stripped from runs/detect/train2/weights/last.pt, 6.2MB
Optimizer stripped from runs/detect/train2/weights/best.pt, 6.2MB
Validating runs/detect/train2/weights/best.pt...
Ultralytics YOLOv8.2.27 🚀 Python-3.9.19 torch-2.3.0+cu121 CUDA:0 (NVIDIA A100-PCIE-40GB, 40377MiB)
Model summary (fused): 168 layers, 3007598 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████|
all 692 12444 0.559 0.203 0.201 0.0987
car 684 6906 0.564 0.45 0.471 0.251
bus 97 120 0.41 0.458 0.367 0.22
person 223 956 0.422 0.243 0.241 0.0838
bike 58 118 0.372 0.0424 0.0656 0.027
truck 187 265 0.41 0.357 0.307 0.172
motor 21 34 1 0 0.0294 0.0129
rider 42 54 1 0 0.0374 0.0152
traffic sign 562 2320 0.518 0.2 0.212 0.087
traffic light 382 1671 0.337 0.0738 0.0755 0.0196
Speed: 0.3ms preprocess, 1.1ms inference, 0.0ms loss, 0.7ms postprocess per image
Results saved to runs/detect/train2
💡 Learn more at https://docs.ultralytics.com/modes/train
run valid example:
yolo task=detect mode=val model=runs/detect/train2/weights/best.pt data=mydata.yml device=0Ultralytics YOLOv8.2.27 🚀 Python-3.9.19 torch-2.3.0+cu121 CUDA:0 (NVIDIA A100-PCIE-40GB, 40377MiB)
Model summary (fused): 168 layers, 3007598 parameters, 0 gradients, 8.1 GFLOPs
val: Scanning /home/wangyf/yolov8/mydata/valid/labels.cache... 692 images, 0 backgrounds, 0 corrupt: 100%|█
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████|
all 692 12444 0.563 0.202 0.201 0.0992
car 684 6906 0.568 0.45 0.47 0.251
bus 97 120 0.411 0.458 0.369 0.222
person 223 956 0.431 0.24 0.241 0.0849
bike 58 118 0.374 0.0424 0.0683 0.0281
truck 187 265 0.412 0.355 0.307 0.173
motor 21 34 1 0 0.0295 0.0126
rider 42 54 1 0 0.0359 0.0148
traffic sign 562 2320 0.525 0.201 0.214 0.0873
traffic light 382 1671 0.343 0.0724 0.0754 0.0195
Speed: 0.2ms preprocess, 2.5ms inference, 0.0ms loss, 2.5ms postprocess per image
Results saved to runs/detect/val
💡 Learn more at https://docs.ultralytics.com/modes/val
- 使用YOLOV7训练BDD100K数据集（数据格式转化+训练全流程）：http://t.csdnimg.cn/C1yDz
- yolov5训练BDD100K自动驾驶数据集+pyqt5前端检测界面：http://t.csdnimg.cn/MfxIA
- YOLOv8 数据集训练参数详解：http://t.csdnimg.cn/YwqwH
- 数据集下载：https://pan.baidu.com/s/1fFSzGJt6Op4k7Gyo9QjtYA 提取码 kuld
- 官方代码库：https://github.com/ultralytics/ultralytics