glenn-jocher
commented
1 week ago Hi there!
It looks like you're running into an issue because the ResNetLayer module isn't recognized within your YOLOv8 configuration. This could be happening if the ResNetLayer isn't defined in your current environment or isn't properly imported in the script where you're trying to load the model.
Here are a couple of suggestions to troubleshoot and resolve this issue:
-
Check Module Name: Ensure that 'ResNetLayer' is the correct name of the module you're trying to use. If it's a custom layer, double-check that it's properly defined and imported into the script where you initialize
YOLO. -
Verify Environment: Make sure that all necessary packages and custom modules are correctly installed in your working environment. If
ResNetLayeris part of an external library, it needs to be installed and accessible. -
Alternative Import Method: If
ResNetLayeris defined in a file that isn't automatically imported, you may need to manually import it. For example:from custom_layers import ResNetLayer # Adjust the import statement as per your setup
If you can share more about where ResNetLayer is sourced from, I can provide more precise guidance! 👍
Enryuwashere
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Question
Hello, Im new in computer vision and I was able to train the model successfully and I want to learn to modify the model but Im facing some errors. I tried to change the backbone of the model yolov8 by using resnet50 and here inside the yolov8.yaml "# Ultralytics YOLO 🚀, AGPL-3.0 license
YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
Parameters
nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
[depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512] # YOLOv8l summary: 406 layers, 49291520 parameters, 49291504 gradients, 152.0 GFLOPs x: [1.00, 1.25, 512]
YOLOv8.0n backbone
backbone:
[from, repeats, module, args]
YOLOv8.0n head
head:
[-1, 1, nn.Upsample, [None, 2, 'nearest']]
[[-1, 3], 1, Concat, [1]] # cat backbone P4
[-1, 3, C2f, [512]] # 7
[-1, 1, nn.Upsample, [None, 2, 'nearest']]
[[-1, 2], 1, Concat, [1]] # cat backbone P3
[-1, 3, C2f, [256]] # 10 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]]
[[-1, 7], 1, Concat, [1]] # cat head P4
[-1, 3, C2f, [512]] # 13 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]]
[[-1, 4], 1, Concat, [1]] # cat head P5
[-1, 3, C2f, [1024]] # 16 (P5/32-large)
[[10, 13, 16], 1, Detect, [nc]] # Detect(P3, P4, P5)"
when I was trying to run the model I was facing this error
"WARNING no model scale passed. Assuming scale='l'.
KeyError Traceback (most recent call last) Cell In[6], line 4 1 from ultralytics import YOLO 3 #Load a model ----> 4 model = YOLO(r"C:\Users\caomi\ultralytics\yolov8-resnet50.yaml") 6 #Load a pretrained model 7 model = YOLO("yolov8n.pt") #use for segmentation pls change to yolov8n.pt
File ~.conda\envs\YOLOv8\lib\site-packages\ultralytics\yolo\engine\model.py:105, in YOLO.init(self, model, task) 103 model, suffix = Path(model).with_suffix('.pt'), '.pt' # add suffix, i.e. yolov8n -> yolov8n.pt 104 if suffix == '.yaml': --> 105 self._new(model, task) 106 else: 107 self._load(model, task)
File ~.conda\envs\YOLOv8\lib\site-packages\ultralytics\yolo\engine\model.py:138, in YOLO._new(self, cfg, task, verbose) 136 self.cfg = cfg 137 self.task = task or guess_model_task(cfg_dict) --> 138 self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1) # build model 139 self.overrides['model'] = self.cfg 141 # Below added to allow export from yamls
File ~.conda\envs\YOLOv8\lib\site-packages\ultralytics\nn\tasks.py:189, in DetectionModel.init(self, cfg, ch, nc, verbose) 187 LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") 188 self.yaml['nc'] = nc # override yaml value --> 189 self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist 190 self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict 191 self.inplace = self.yaml.get('inplace', True)
File ~.conda\envs\YOLOv8\lib\site-packages\ultralytics\nn\tasks.py:491, in parse_model(d, ch, verbose) 489 layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out 490 for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args --> 491 m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m] # get module 492 for j, a in enumerate(args): 493 if isinstance(a, str):
KeyError: 'ResNetLayer'"
can someone help me to solve this error? Thank you
Additional
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