WARNING ⚠️ NMS time limit 0.340s exceeded

[haniraid]

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Hi YOLO comunnity. so im running training on my cpu and i have this probleme notice that ive already checked on the previous simular issues and i found this time_limit = 0.1 + 0.02 * bs # seconds to quit after i applied it but the issue still here raidhani@raidhani-All-Series:~/catkin_ws/src/yolov5$ python3 train.py --img 640 --batch 6 --epochs 100 --data /home/raidhani/catkin_ws/src/data/data.yaml --weights yolov5s.pt train: weights=yolov5s.pt, cfg=, data=/home/raidhani/catkin_ws/src/data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=6, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False github: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-368-gb163ff8d Python-3.8.10 torch-1.11.0+cpu CPU

hyperparameters: 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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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 Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Overriding model.yaml nc=80 with nc=10

             from  n    params  module                                  arguments                     

0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 40455 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model summary: 214 layers, 7046599 parameters, 7046599 gradients, 16.0 GFLOPs

Transferred 343/349 items from yolov5s.pt optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.000515625), 60 bias train: Scanning /home/raidhani/catkin_ws/src/data/train/labels.cache... 1008 images, 120 backgrounds, 0 corrupt: 100%|█████████ val: Scanning /home/raidhani/catkin_ws/src/data/valid/labels.cache... 230 images, 31 backgrounds, 0 corrupt: 100%|██████████| 2

AutoAnchor: 4.51 anchors/target, 0.997 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/exp11/labels.jpg... Image sizes 640 train, 640 val Using 6 dataloader workers Logging results to runs/train/exp11 Starting training for 100 epochs...

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
   0/99         0G     0.1032    0.06545    0.05506         64        640: 100%|██████████| 169/169 [07:32<00:00,  2.68s/it
             Class     Images  Instances          P          R      mAP50   mAP50-95:   0%|          | 0/20 [00:00<?, ?it/sWARNING ⚠️ NMS time limit 0.340s exceeded
             Class     Images  Instances          P          R      mAP50   mAP50-95:   5%|▌         | 1/20 [00:01<00:29,  WARNING ⚠️ NMS time limit 0.340s exceeded
             Class     Images  Instances          P          R      mAP50   mAP50-95:  10%|█         | 2/20 [00:03<00:27,  WARNING ⚠️ NMS time limit 0.340s exceeded
             Class     Images  Instances          P          R      mAP50   mAP50-95:  15%|█▌        | 3/20 [00:04<00:26,  WARNING ⚠️ NMS time limit 0.340s exceeded
             Class     Images  Instances          P          R      mAP50   mAP50-95:  20%|██        | 4/20 [00:06<00:24,  WARNING ⚠️ NMS time limit 0.340s exceeded
             Class     Images  Instances          P          R      mAP50   mAP50-95:  25%|██▌       | 5/20 [00:07<00:24,                   Class     Images  Instances          P          R      mAP50   mAP50-95:  25%|██▌       | 5/20 [00:08<00:24,  

Traceback (most recent call last): File "train.py", line 986, in

Environment

YOLOv5 🚀 v7.0-368-gb163ff8d Python-3.8.10 torch-1.11.0+cpu CPU

Minimal Reproducible Example

python3 train.py --img 640 --batch 6 --epochs 100 --data /home/raidhani/catkin_ws/src/data/data.yaml --weights yolov5s.pt

Additional

No response

Are you willing to submit a PR?

• [ ] Yes I'd like to help by submitting a PR!

[UltralyticsAssistant]

👋 Hello @haniraid, thank you for bringing this to our attention! This is an automated response, and an Ultralytics engineer will assist you soon.

For now, please ensure you've provided a minimum reproducible example. It helps us debug more effectively and speeds up the resolution process.

If you haven't already, you may want to explore our ⭐️ Tutorials for guidance on setup and configurations, and verify you are following our Tips for Best Training Results.

Requirements

Ensure your environment meets the following:
Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. Start with:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 can be run in any of these environments with pre-installed dependencies, including CUDA/CUDNN:

• Notebooks with free GPU:
• Google Cloud or AWS: See respective quickstart guides / AWS Quickstart Guide
• Docker Image: See Docker Quickstart Guide

Status

Check the current status of YOLOv5 CI tests:

Discover YOLOv8 🚀

Explore our latest release, YOLOv8 🚀. It's fast, accurate, and user-friendly for various tasks. Get started with:

pip install ultralytics

Please provide any additional context or information to assist us further. We're here to help! 😊

[pderrenger]

@haniraid the "NMS time limit exceeded" warning often indicates that your CPU is struggling with the workload. Consider reducing the batch size or using a machine with a GPU to improve performance. Additionally, ensure you're using the latest version of YOLOv5 and PyTorch. If the issue persists, please let us know.

[github-actions[bot]]

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