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1. star4s

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   questionFurther information is requestedFurther information is requested

   on Feb 17, 2022
2. github-actions commented on Feb 17, 2022

   

   github-actionsBot

   on Feb 17, 2022

   Last edited by UltralyticsAssistant

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   👋 Hello @star4s, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

   If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

   If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

   For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.

   Requirements

   Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

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

   Environments

   YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

   • Google Colab and Kaggle notebooks with free GPU:
   • Google Cloud Deep Learning VM. See GCP Quickstart Guide
   • Amazon Deep Learning AMI. See AWS Quickstart Guide
   • Docker Image. See Docker Quickstart Guide

   Status

   

   If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
3. glenn-jocher commented on Feb 17, 2022

   

   glenn-jocher

   on Feb 17, 2022

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   @star4s this doesn't have anything to do with evolution.

   AutoAnchor needs data to work, you have almost zero data in your dataset, or all your objects are the exact same size.
4. glenn-jocher

   added a commit that references this issue on Feb 17, 2022

   Handle `scipy.cluster.vq.kmeans` too few points

   ...

   This issue will close once commit 7de1647 is merged into the 'master' branch.

   7de1647
5. glenn-jocher

   closed this as completedin #6668on Feb 17, 2022
6. glenn-jocher

   added a commit that references this issue on Feb 17, 2022

   Robust `scipy.cluster.vq.kmeans` too few points (#6668)

   ...

   This issue will close once commit 2e5c67e is merged into the 'master' branch.

   2e5c67e
7. glenn-jocher commented on Feb 17, 2022

   

   glenn-jocher

   on Feb 17, 2022

   Last edited by UltralyticsAssistant

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   @star4s good news 😃! Your original issue may now be fixed ✅ in PR #6668. To receive this update:

   • Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
   • PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
   • Notebooks – View updated notebooks
   • Docker – sudo docker pull ultralytics/yolov5:latest to update your image

   Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!
8. star4s commented on Feb 17, 2022

   

   star4s

   on Feb 17, 2022

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   **After I apply to your modified code with autoanchor.py , I have more errors!

   After several AutoAnchor process, there are no more error.

   and then I meet to some error like that.**

   hyperparameters: lr0=0.00095, lrf=0.081, momentum=0.98, weight_decay=0.00051, warmup_epochs=2.58013, warmup_momentum=0.78619, warmup_bias_lr=0.12241, box=0.07791, cls=0.45521, cls_pw=1.32921, obj=1.05062, obj_pw=0.8944, iou_t=0.2, anchor_t=2.79565, anchors=4.49633, fl_gamma=0.0, hsv_h=0.0, hsv_s=0.0, hsv_v=0.0, degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.0, mosaic=0.0, mixup=0.0, copy_paste=0.0
   Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
   Overriding model.yaml anchors with anchors=4.49633

                from  n    params  module                                  arguments                     
   

   0 -1 1 8800 models.common.Conv [3, 80, 6, 2, 2]
   1 -1 1 115520 models.common.Conv [80, 160, 3, 2]
   2 -1 4 309120 models.common.C3 [160, 160, 4]
   3 -1 1 461440 models.common.Conv [160, 320, 3, 2]
   4 -1 8 2259200 models.common.C3 [320, 320, 8]
   5 -1 1 1844480 models.common.Conv [320, 640, 3, 2]
   6 -1 12 13125120 models.common.C3 [640, 640, 12]
   7 -1 1 5531520 models.common.Conv [640, 960, 3, 2]
   8 -1 4 11070720 models.common.C3 [960, 960, 4]
   9 -1 1 11061760 models.common.Conv [960, 1280, 3, 2]
   10 -1 4 19676160 models.common.C3 [1280, 1280, 4]
   11 -1 1 4099840 models.common.SPPF [1280, 1280, 5]
   12 -1 1 1230720 models.common.Conv [1280, 960, 1, 1]
   13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
   14 [-1, 8] 1 0 models.common.Concat [1]
   15 -1 4 11992320 models.common.C3 [1920, 960, 4, False]
   16 -1 1 615680 models.common.Conv [960, 640, 1, 1]
   17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
   18 [-1, 6] 1 0 models.common.Concat [1]
   19 -1 4 5332480 models.common.C3 [1280, 640, 4, False]
   20 -1 1 205440 models.common.Conv [640, 320, 1, 1]
   21 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
   22 [-1, 4] 1 0 models.common.Concat [1]
   23 -1 4 1335040 models.common.C3 [640, 320, 4, False]
   24 -1 1 922240 models.common.Conv [320, 320, 3, 2]
   25 [-1, 20] 1 0 models.common.Concat [1]
   26 -1 4 4922880 models.common.C3 [640, 640, 4, False]
   27 -1 1 3687680 models.common.Conv [640, 640, 3, 2]
   28 [-1, 16] 1 0 models.common.Concat [1]
   29 -1 4 11377920 models.common.C3 [1280, 960, 4, False]
   30 -1 1 8296320 models.common.Conv [960, 960, 3, 2]
   31 [-1, 12] 1 0 models.common.Concat [1]
   32 -1 4 20495360 models.common.C3 [1920, 1280, 4, False]
   33 [23, 26, 29, 32] 1 89712 models.yolo.Detect [2, [[0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7]], [320, 640, 960, 1280]]
   Model Summary: 733 layers, 140067472 parameters, 140067472 gradients, 208.4 GFLOPs

   Transferred 954/963 items from yolov5x6.pt
   Scaled weight_decay = 0.00051
   optimizer: SGD with parameter groups 159 weight (no decay), 163 weight, 163 bias
   train: Scanning '/yolov5/datasets/OP1_test/labels/train.cache' images and labels... 14 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████████████████| 14/14 [00:00<?, ?it/s]
   train: Caching images (0.1GB ram): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:00<00:00, 115.34it/s]
   val: Scanning '/yolov5/datasets/OP1_test/labels/train.cache' images and labels... 14 found, 0 missing, 0 empty, 0 corrupt: 100%|████████████████████████| 14/14 [00:00<?, ?it/s]

   AutoAnchor: 0.00 anchors/target, 0.000 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve...
   AutoAnchor: Running kmeans for 16 anchors on 14 points...
   AutoAnchor: ERROR: Cannot take a larger sample than population when 'replace=False'
   Traceback (most recent call last):
   File "train.py", line 638, in
   main(opt)
   File "train.py", line 616, in main
   results = train(hyp.copy(), opt, device, callbacks)
   File "train.py", line 248, in train
   check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
   File "/yolov5/utils/autoanchor.py", line 54, in check_anchors
   new_bpr = metric(anchors)[0]
   File "yolov5/utils/autoanchor.py", line 35, in metric
   r = wh[:, None] / k[None]
   RuntimeError: The size of tensor a (14) must match the size of tensor b (4) at non-singleton dimension 1
9. glenn-jocher commented on Feb 18, 2022

   

   glenn-jocher

   on Feb 18, 2022

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   @star4s I already told you:

   AutoAnchor needs data to work, you have almost zero data in your dataset, or all your objects are the exact same size.
10. star4s commented on Feb 20, 2022

    

    star4s

    on Feb 20, 2022

    Last edited by star4s

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    @glenn-jocher
    Hi, Thank you for your answer and your help.

    My Custom Data information:
    . image size: 2352 X 1728
    . class number: 17

    My command for my custom data:

    python train.py --img 2352 --batch 2 --epochs 5 --data test.yaml --cfg './models/yolov5x6.yaml' --weights yolov5x6.pt --cache --evolve 1000

    My yolov5x6.yaml :

    parameters

    nc: 17 # number of classes
    depth_multiple: 1.33 # model depth multiple
    width_multiple: 1.25 # layer channel multiple

    anchors

    anchors:

    • [ 19,27, 44,40, 38,94 ] # P3/8
    • [ 96,68, 86,152, 180,137 ] # P4/16
    • [ 140,301, 303,264, 238,542 ] # P5/32
    • [ 436,615, 739,380, 925,792 ] # P6/64

    YOLOv5 backbone

    backbone:

    [from, number, module, args]

    [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
    [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
    [ -1, 3, C3, [ 128 ] ],
    [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
    [ -1, 9, C3, [ 256 ] ],
    [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
    [ -1, 9, C3, [ 512 ] ],
    [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
    [ -1, 3, C3, [ 768 ] ],
    [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
    [ -1, 3, C3, [ 1024, False ] ], # 11
    ]

    YOLOv5 head

    head:
    [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
    [ -1, 3, C3, [ 768, False ] ], # 15
    [ -1, 1, Conv, [ 512, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
    [ -1, 3, C3, [ 512, False ] ], # 19
    [ -1, 1, Conv, [ 256, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
    [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
    [ -1, 1, Conv, [ 256, 3, 2 ] ],
    [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
    [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
    [ -1, 1, Conv, [ 512, 3, 2 ] ],
    [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
    [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
    [ -1, 1, Conv, [ 768, 3, 2 ] ],
    [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
    [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge)
    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
    ]

    I got change many data for AutoAnchor, and then I run Hyperpameter Evolution.
    I have the strange best result between coco128 and my data.

    coco128 :
    Best generation: 246
    Last generation: 299
    metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss
    0.62719, 0.79158, 0.79514, 0.52422, 0.034782

    My custom data:
    Best generation: 154
    Last generation: 299
    metrics/precision, metrics/recall , metrics/mAP_0.5 , metrics/mAP_0.5:0.95, val/box_loss
    2.945e-06 , 0.00092593, 1.4938e-06, 2.9876e-07, 0.031016

    This mean some thing wrong for my custom data?
    I have wrong annotation?

    The most metrics/precision and metrics/recall from my custom data was 0.
    Why is zero in the case of most metrics/precision and metrics/recall from my custom data?

    Thank you for your attention.
11. glenn-jocher commented on Feb 21, 2022

    

    glenn-jocher

    on Feb 21, 2022

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    @star4s evolution should be run from a stable starting scenario. If your starting scenario returns zero mAP evolution will not help.
12. star4s commented on Feb 21, 2022

    

    star4s

    on Feb 21, 2022

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    @glenn-jocher
    Thank you for your answer and your help.

    You mentioned about a stable starting scenario.

    What is the mean of a stable starting scenario?

    Do I Need to modify structures of yolov5x6.yaml such as YOLOv5 head and YOLOv5 backbone?
13. glenn-jocher commented on Feb 22, 2022

    

    glenn-jocher

    on Feb 22, 2022

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    @star4s Nothing needs to be modified anywhere. Evolution improves results on your base scenario. If your base scenario is returning zero mAP there's not much for evolution to work with.

    Like any nonlinear optimization problem the final result is a function of the initial guess.
14. bfineran

    added a commit that references this issue on Apr 8, 2022

    Update Neuralmagic-YoloV5 integration to v6.1 (#29)

    ...

    This issue will close once commit 14eb1dc is merged into the 'master' branch.

    14eb1dc
15. BjarneKuehl

    added a commit that references this issue on Aug 26, 2022

    Robust `scipy.cluster.vq.kmeans` too few points (ultralytics#6668)

    ...

    This issue will close once commit 363e141 is merged into the 'feature-fourChannels' branch.

    363e141