Comments (5)
glenn-jocher
commented on April 30, 2024
1
@pranav2812 thank you for the bug report! Yes, you are correct, it seems we forgot to attach the names lists to the updated models. We have fixed this and re-uploaded all models. Please try again.
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pranav-deo
commented on April 30, 2024
1
Thanks for the quick resolvement! 😃
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github-actions
commented on April 30, 2024
Hello @pranav2812, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Google Colab Notebook, Docker Image, and GCP Quickstart Guide for example environments.
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 model or data training question, please note that Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
- Cloud-based AI surveillance systems operating on hundreds of HD video streams in realtime.
- Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
- Custom data training, hyperparameter evolution, and model exportation to any destination.
For more information please visit https://www.ultralytics.com.
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mon28
commented on April 30, 2024
I see the models were updated again a couple of days back and this issue is reoccuring
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glenn-jocher
commented on April 30, 2024
@mon28 we designed a set of unit tests which include this exact use case, and they are all passing in our colab notebook with no errors on the latest repository. If you can't run these successfully on your system, then you are using old/modified code, or your environment is a problem.
# Unit tests
%%shell
git pull
python3 -c "from utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')" && mv ./coco128 ../
for d in 0 # device
do
for x in yolov5s #yolov5m yolov5l yolov5x # models
do
python train.py --weights $x.pt --cfg $x.yaml --epochs 3 --img 320 --device $d --name test
python detect.py --weights weights/last_test.pt --device $d
python detect.py --weights weights/best_test.pt --device $d
done
doneOutput:
Already up to date.
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 408 0 408 0 0 137 0 --:--:-- 0:00:02 --:--:-- 137
0 0 0 0 0 0 0 0 --:--:-- 0:00:03 --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- 0:00:04 --:--:-- 0
100 21.0M 0 21.0M 0 0 4699k 0 --:--:-- 0:00:04 --:--:-- 237M
Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (8.7s)
Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex
{'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, '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': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}
Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5s.yaml', data='data/coco128.yaml', device='0', epochs=3, evolve=False, img_size=[320], multi_scale=False, name='test', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
2020-06-19 16:38:28.749236: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 20672 models.common.Bottleneck [64, 64]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1905152 models.common.BottleneckCSP [512, 512, 2]
10 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
11 -1 1 130815 torch.nn.modules.conv.Conv2d [512, 255, 1, 1]
12 -2 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
13 [-1, 6] 1 0 models.common.Concat [1]
14 -1 1 197120 models.common.Conv [768, 256, 1, 1]
15 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False]
16 -1 1 65535 torch.nn.modules.conv.Conv2d [256, 255, 1, 1]
17 -2 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
18 [-1, 4] 1 0 models.common.Concat [1]
19 -1 1 49408 models.common.Conv [384, 128, 1, 1]
20 -1 1 78720 models.common.BottleneckCSP [128, 128, 1, False]
21 -1 1 32895 torch.nn.modules.conv.Conv2d [128, 255, 1, 1]
22 [-1, 16, 11] 1 0 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]]
Model Summary: 165 layers, 7.07417e+06 parameters, 7.07417e+06 gradients
Optimizer groups: 54 .bias, 60 conv.weight, 51 other
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 408 0 408 0 0 447 0 --:--:-- --:--:-- --:--:-- 446
0 0 0 0 0 0 0 0 --:--:-- 0:00:01 --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- 0:00:01 --:--:-- 0
100 13.6M 0 13.6M 0 0 7962k 0 --:--:-- 0:00:01 --:--:-- 7962k
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (3.7s)
Reading image shapes: 100% 128/128 [00:00<00:00, 4405.17it/s]
Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 7694.21it/s]
Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 10106.76it/s]
Analyzing anchors... Best Possible Recall (BPR) = 0.9623. Attempting to generate improved anchors, please wait...
WARNING: Extremely small objects found. 84 of 929 labels are < 4 pixels in width or height.
Running kmeans for 9 anchors on 916 points...
thr=0.25: 0.9645 best possible recall, 3.74 anchors past thr
n=9, img_size=320, metric_all=0.261/0.653-mean/best, past_thr=0.472-mean: 9,12, 32,19, 27,48, 74,43, 54,92, 77,161, 161,107, 174,237, 299,195
Evolving anchors with Genetic Algorithm: fitness = 0.6670: 100% 1000/1000 [00:00<00:00, 1604.71it/s]
thr=0.25: 0.9849 best possible recall, 3.79 anchors past thr
n=9, img_size=320, metric_all=0.263/0.663-mean/best, past_thr=0.471-mean: 8,10, 23,12, 23,33, 58,37, 48,89, 68,145, 145,110, 181,199, 310,221
New anchors saved to model. Update model *.yaml to use these anchors in the future.
Image sizes 320 train, 320 test
Using 2 dataloader workers
Starting training for 3 epochs...
Epoch gpu_mem GIoU obj cls total targets img_size
0/2 2.31G 0.07842 0.1905 0.03977 0.3087 177 320: 100% 8/8 [00:04<00:00, 1.84it/s]
Class Images Targets P R [email protected] [email protected]:.95: 100% 8/8 [00:03<00:00, 2.28it/s]
all 128 929 0.172 0.609 0.408 0.24
Epoch gpu_mem GIoU obj cls total targets img_size
1/2 2.31G 0.06492 0.1971 0.04188 0.3039 140 320: 100% 8/8 [00:03<00:00, 2.29it/s]
Class Images Targets P R [email protected] [email protected]:.95: 100% 8/8 [00:02<00:00, 3.25it/s]
all 128 929 0.174 0.646 0.464 0.275
Epoch gpu_mem GIoU obj cls total targets img_size
2/2 2.32G 0.06308 0.1993 0.03756 0.3 191 320: 100% 8/8 [00:03<00:00, 2.34it/s]
Class Images Targets P R [email protected] [email protected]:.95: 100% 8/8 [00:02<00:00, 3.24it/s]
all 128 929 0.149 0.658 0.494 0.305
Optimizer stripped from weights/last_test.pt
Optimizer stripped from weights/best_test.pt
3 epochs completed in 0.006 hours.
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='0', fourcc='mp4v', img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='inference/images', view_img=False, weights='weights/last_test.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
image 1/2 inference/images/bus.jpg: 640x512 5 persons, 1 buss, 2 skateboards, Done. (0.010s)
image 2/2 inference/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.011s)
Results saved to /content/yolov5/inference/output
Done. (0.123s)
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='0', fourcc='mp4v', img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='inference/images', view_img=False, weights='weights/best_test.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
image 1/2 inference/images/bus.jpg: 640x512 4 persons, 1 buss, 2 skateboards, Done. (0.010s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.010s)
Results saved to /content/yolov5/inference/output
Done. (0.104s)from yolov5.
Related Issues (20)
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- Error loading self trained model HOT 4
- Image not found error HOT 1
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