I am trying to run a custom object detection model using yolov5.
Did the following steps;
%cd yolov5
%pip install -qr requirements.txt # install dependencies
import torch
import os
from IPython.display import Image, clear_output # to display images
print(f"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")
output-
Cloning into 'yolov5'... remote: Enumerating objects: 11888, done. remote: Counting objects: 100% (88/88), done. remote: Compressing objects: 100% (75/75), done. remote: Total 11888 (delta 40), reused 42 (delta 13), pack-reused 11800 Receiving objects: 100% (11888/11888), 12.46 MiB | 20.35 MiB/s, done. Resolving deltas: 100% (8152/8152), done. /content/yolov5 |████████████████████████████████| 1.6 MB 6.6 MB/s Setup complete. Using torch 1.12.1+cu113 (CPU)
!python train.py --img 320 --batch 2 --epochs 2 --data customdata.yaml --weights yolov5s.pt --cache
here in customdata.yaml, i have given the paths of my datasets (annoted)
After running the command, Model builds, but further it gives an error.
train: weights=yolov5s.pt, cfg=, data=customdata.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=2, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, 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
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 v6.2-34-ge0700cc Python-3.7.13 torch-1.12.1+cu113 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
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 runs in Weights & Biases
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 in ClearML
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=6
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 29667 models.yolo.Detect [6, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 270 layers, 7035811 parameters, 7035811 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=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning '/content/datasets/coco128/labels/train201.cache' images and labels... 2 found, 0 missing, 0 empty, 0 corrupt: 100% 2/2 [00:00<?, ?it/s]
train: Caching images (0.0GB ram): 100% 2/2 [00:00<00:00, 420.78it/s]
val: Scanning '/content/datasets/coco128/labels/train202' images and labels...2 found, 0 missing, 0 empty, 0 corrupt: 100% 2/2 [00:00<00:00, 64.04it/s]
val: New cache created: /content/datasets/coco128/labels/train202.cache
val: Caching images (0.0GB ram): 100% 2/2 [00:00<00:00, 418.41it/s]
AutoAnchor: 6.20 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp9/labels.jpg...
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to runs/train/exp9
Starting training for 2 epochs...
Epoch gpu_mem box obj cls labels img_size
0/1 0G 0.1249 0.01741 0.05573 7 320: 100% 1/1 [00:03<00:00, 3.61s/it]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 2.92it/s]
all 2 5 0.00324 0.333 0.0079 0.00237
Epoch gpu_mem box obj cls labels img_size
1/1 0G 0.1228 0.0139 0.06047 4 320: 100% 1/1 [00:01<00:00, 1.08s/it]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 3.07it/s]
all 2 5 0.00388 0.333 0.0065 0.0026
2 epochs completed in 0.002 hours.
Optimizer stripped from runs/train/exp9/weights/last.pt, 14.3MB
Optimizer stripped from runs/train/exp9/weights/best.pt, 14.3MB
Validating runs/train/exp9/weights/best.pt...
Fusing layers...
Model summary: 213 layers, 7026307 parameters, 0 gradients, 15.8 GFLOPs
Class Images Labels P R mAP@.5 mAP@.5:.95: 0% 0/1 [00:00<?, ?it/s]Exception in thread Thread-16:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/content/yolov5/utils/plots.py", line 243, in plot_images
cls = names[cls] if names else cls
KeyError: 0
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 3.09it/s]
Exception in thread Thread-17:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/content/yolov5/utils/plots.py", line 243, in plot_images
cls = names[cls] if names else cls
KeyError: 0
Traceback (most recent call last):
File "train.py", line 627, in <module>
main(opt)
File "train.py", line 523, in main
train(opt.hyp, opt, device, callbacks)
File "train.py", line 419, in train
compute_loss=compute_loss) # val best model with plots
File "/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/content/yolov5/val.py", line 267, in run
tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
File "/content/yolov5/utils/metrics.py", line 84, in ap_per_class
plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
File "/content/yolov5/utils/metrics.py", line 330, in plot_pr_curve
ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)
KeyError: 2
Please suggest some solution.
