'yolov8n-pose.yaml'

[palmcorp]

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where can i download 'yolov8n-pose.yaml

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[pderrenger]

Hello!

Thank you for reaching out. It looks like you're looking for the yolov8n-pose.yaml file. This file is part of the configuration files used for YOLOv8 models, specifically for pose estimation.

You can find the configuration files for YOLOv8, including yolov8n-pose.yaml, in the Ultralytics GitHub repository. Here is a direct link to the YOLOv8 configuration files.

If you have any further questions or need additional assistance, feel free to ask. We're here to help! 😊

[palmcorp]

Hi Paula,

Thanks again for your help.

Oops, Checked the references.. and no joy:

I’m trying to track down why Yolo wants to see 15 columns in the labels. As near as I can see we have that.

Should I chnge kpt_shape: [5, 2] -> kpt_shape: [17,2]???

Is there some parameter in yolov8n-pose.yaml that is overriding what is in our data.yaml?

Should I remove loading a pertained yolo model before calling my model.training command?

Not sure which direction to take things at this point.

REERENCE CHECK:

Ref one: GitHub Repository has several .yaml files , but not yolov8n-pose.yaml

Closest is config.yaml

Ref two: is 404 - page not found

LABEL COMPARISON

Here are one of our labels compared to a cocolabel.

ECHO LABELS

0 class

686445 0.531960 0.082891 0.323967 (bbox)

1. 0.667188 0.399061 1.000000 label #1

2. 0.670312 0.396714 2.000000

3. 0.0 0.00000000 0.0000000

4. .678125 0.394366 2.000000

5. 0.0 0.00000000 0.0000000

6. 0.689063 0.415493 2.000000

7. 0.696875 0.415493 2.000000

8. 0.682813 0.469484 2.000000

9. 0.671875 0.483568 2.000000

10. 0.671875 0.516432 2.000000

11. 0.656250 0.504695 2.000000

12. 0.695312 0.530516 2.000000

13. 0.706250 0.523474 2.000000

14. 0.698438 0.610329 2.000000

15. 0.709375 0.603286 2.000000

16. 0.710938 0.680751 2.000000

17. 0.717187 0.671362 2.000000

17 KEY POINTS

Following is a typical COCO label:

0 (class)

0.620039 0.593900 0.172415 0.146080 (bbox)

1. 0.658793 0.552000 2.000000 label#1

2. 0.664042 0.550000 2.000000 etc.

3 0.656168 0.548000 2.000000

4 0.679790 0.554000 2.000000

5 0.000000 0.000000 0.000000

6 0.679790 0.570000 2.000000

7 0.648294 0.568000 2.000000

8 0.674541 0.588000 2.000000

9 0.606299 0.582000 2.000000

10 0.627297 0.562000 2.000000

11 0.603675 0.564000 2.000000

12 0.627297 0.616000 2.000000

13 0.000000 0.000000 0.000000

14 0.595801 0.604000 2.000000

15 0.000000 0.000000 0.000000

16 0.569554 0.644000 2.000000

17 0.000000 0.000000 0.000000

17 keypoints

From: Paula Derrenger @.*** Sent: Tuesday, June 11, 2024 4:48 PM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hello!

Thank you for reaching out. It looks like you're looking for the yolov8n-pose.yaml file. This file is part of the configuration files used for YOLOv8 models, specifically for pose estimation.

You can find the configuration files for YOLOv8, including yolov8n-pose.yaml, in the Ultralytics GitHub repository https://github.com/ultralytics/ultralytics . Here is a direct link to the YOLOv8 configuration files https://github.com/ultralytics/ultralytics/tree/main/models/yolo .

If you have any further questions or need additional assistance, feel free to ask. We're here to help! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2161790711 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FWML3ZBZZZ65V2UHPDZG6ECRAVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNRRG44TANZRGE . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FWDOGC5VHSWTW7WR4TZG6ECRA5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUA3JHPO.gif Message ID: @.***>

[pderrenger]

Hi Paula,

Thank you for providing detailed information about your issue. Let's work through this together to find a solution.

Minimum Reproducible Example

To better assist you, could you please provide a minimum reproducible code example? This will help us understand the context and reproduce the issue on our end. You can refer to our documentation for guidance on creating one. It's crucial for us to be able to reproduce the bug before we can investigate a solution.

Version Check

Please ensure that you are using the most recent versions of torch, ultralytics, and hub-sdk. If not, please upgrade your packages and try again:

pip install --upgrade torch ultralytics hub-sdk

Label Format

Regarding the label format, YOLOv8 expects a specific structure for pose estimation. The kpt_shape parameter in your yolov8n-pose.yaml should match the number of keypoints in your dataset. If your dataset has 17 keypoints, you should set kpt_shape: [17, 2].

Pretrained Model

Loading a pretrained model should not interfere with your custom training as long as the label formats and configurations are correctly set. However, if you suspect it might be causing issues, you can try training without loading the pretrained model to see if it resolves the problem.

Example Configuration

Here’s an example of what your yolov8n-pose.yaml might look like:

# YOLOv8n-pose model configuration
kpt_shape: [17, 2]
...

Data.yaml

Ensure your data.yaml file is correctly formatted and includes the necessary parameters:

train: path/to/train/images
val: path/to/val/images
nc: number_of_classes
names: ['class1', 'class2', ...]

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[palmcorp]

Hi Paula,

Good point. Will compile a more complete answer.

Best,

Paul

From: Paula Derrenger @.*** Sent: Tuesday, June 11, 2024 10:26 PM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hi Paula,

Thank you for providing detailed information about your issue. Let's work through this together to find a solution.

Minimum Reproducible Example

To better assist you, could you please provide a minimum reproducible code example? This will help us understand the context and reproduce the issue on our end. You can refer to our documentation https://docs.ultralytics.com/help/minimum_reproducible_example for guidance on creating one. It's crucial for us to be able to reproduce the bug before we can investigate a solution.

Version Check

Please ensure that you are using the most recent versions of torch, ultralytics, and hub-sdk. If not, please upgrade your packages and try again:

pip install --upgrade torch ultralytics hub-sdk

Label Format

Regarding the label format, YOLOv8 expects a specific structure for pose estimation. The kpt_shape parameter in your yolov8n-pose.yaml should match the number of keypoints in your dataset. If your dataset has 17 keypoints, you should set kpt_shape: [17, 2].

Pretrained Model

Loading a pretrained model should not interfere with your custom training as long as the label formats and configurations are correctly set. However, if you suspect it might be causing issues, you can try training without loading the pretrained model to see if it resolves the problem.

Example Configuration

Here’s an example of what your yolov8n-pose.yaml might look like:

YOLOv8n-pose model configuration

kpt_shape: [17, 2] ...

Data.yaml

Ensure your data.yaml file is correctly formatted and includes the necessary parameters:

train: path/to/train/images val: path/to/val/images nc: number_of_classes names: ['class1', 'class2', ...]

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2162133069 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FRIVYKGDYY422CYL3LZG7LX3AVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNRSGEZTGMBWHE . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FRTOZBK225NGW6FERLZG7LX3A5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUA36EE2.gif Message ID: @.***>

[palmcorp]

Have shared a colb notebook for minimum case. Returns the error w/o data.

Best

Paul

From: Paula Derrenger @.*** Sent: Tuesday, June 11, 2024 10:26 PM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hi Paula,

Thank you for providing detailed information about your issue. Let's work through this together to find a solution.

Minimum Reproducible Example

To better assist you, could you please provide a minimum reproducible code example? This will help us understand the context and reproduce the issue on our end. You can refer to our documentation https://docs.ultralytics.com/help/minimum_reproducible_example for guidance on creating one. It's crucial for us to be able to reproduce the bug before we can investigate a solution.

Version Check

Please ensure that you are using the most recent versions of torch, ultralytics, and hub-sdk. If not, please upgrade your packages and try again:

pip install --upgrade torch ultralytics hub-sdk

Label Format

Regarding the label format, YOLOv8 expects a specific structure for pose estimation. The kpt_shape parameter in your yolov8n-pose.yaml should match the number of keypoints in your dataset. If your dataset has 17 keypoints, you should set kpt_shape: [17, 2].

Pretrained Model

Loading a pretrained model should not interfere with your custom training as long as the label formats and configurations are correctly set. However, if you suspect it might be causing issues, you can try training without loading the pretrained model to see if it resolves the problem.

Example Configuration

Here’s an example of what your yolov8n-pose.yaml might look like:

YOLOv8n-pose model configuration

kpt_shape: [17, 2] ...

Data.yaml

Ensure your data.yaml file is correctly formatted and includes the necessary parameters:

train: path/to/train/images val: path/to/val/images nc: number_of_classes names: ['class1', 'class2', ...]

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2162133069 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FRIVYKGDYY422CYL3LZG7LX3AVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNRSGEZTGMBWHE . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FRTOZBK225NGW6FERLZG7LX3A5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUA36EE2.gif Message ID: @.***>

[palmcorp]

I've shared an item with you:

Copy of MinimumReproducableExample_v2_118.ipynb https://colab.research.google.com/drive/1SuFUnkmyAw5ct-O-4_eNpQc79Eh4_9M8?invite=CPDUq_oD&ts=666a8f71

It's not an attachment -- it's stored online. To open this item, just click
the link above.

Hi Paula, This returns the error w/o data so am sending this colab notebook. here is data5.yaml = = = = = = =

keypoint data sets

Data

path: D:POSE/data

path: /content/datasets/echo-pose train: images/train2017 # subsets to D val: images/val2017 # relative to path

Keypoints

kpt_shape: [5,2] # number of keypoints, number of dim flip_indx: [0,1,2,3,4] # no flipping

backbone: uskng standard backbone

backbone:

[from, repeats, module, args]

• [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
• [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
• [-1, 3, C2f, [128, True]]
• [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
• [-1, 6, C2f, [256, True]]
• [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
• [-1, 6, C2f, [512, True]]
• [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
• [-1, 3, C2f, [1024, True]]
• [-1, 1, SPPF, [1024, 5]] # 9

  usn standard head

head:

• [-1, 1, nn.Upsample, [None, 2, "nearest"]]

• [[-1, 8], 1, Concat, [1]] # cat backbone P5

• [-1, 3, C2, [768, False]] # 14

• [-1, 1, nn.Upsample, [None, 2, "nearest"]]

• [[-1, 6], 1, Concat, [1]] # cat backbone P4

• [-1, 3, C2, [512, False]] # 17

• [-1, 1, nn.Upsample, [None, 2, "nearest"]]

• [[-1, 4], 1, Concat, [1]] # cat backbone P3

• [-1, 3, C2, [256, False]] # 20 (P3/8-small)

• [-1, 1, Conv, [256, 3, 2]]

• [[-1, 17], 1, Concat, [1]] # cat head P4

• [-1, 3, C2, [512, False]] # 23 (P4/16-medium)

• [-1, 1, Conv, [512, 3, 2]]

• [[-1, 14], 1, Concat, [1]] # cat head P5

• [-1, 3, C2, [768, False]] # 26 (P5/32-large)

• [-1, 1, Conv, [768, 3, 2]]

• [[-1, 11], 1, Concat, [1]] # cat head P6

• [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)

• [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)

Classes

names: 0: class0 1: class1 2: class2 3: class3 4: class4

[pderrenger]

@palmcorp hi Paul,

Thank you for sharing the Colab notebook and the data5.yaml file. Let's dive into the issue and work towards a solution.

Key Points to Address

1. Keypoint Shape Mismatch: Your data5.yaml specifies kpt_shape: [5,2], which indicates 5 keypoints. However, if your dataset actually contains 17 keypoints, you should update this to kpt_shape: [17,2].

2. Configuration File: Ensure that your yolov8n-pose.yaml configuration file aligns with the number of keypoints in your dataset. Here’s an example configuration snippet:

   # YOLOv8n-pose model configuration
   kpt_shape: [17, 2]
   ...

3. Pretrained Model: If you are loading a pretrained model, ensure that it is compatible with your custom dataset configuration. If you suspect it might be causing issues, try training without loading the pretrained model to see if it resolves the problem.

Next Steps

1. Update data5.yaml: Change the kpt_shape parameter to match the number of keypoints in your dataset:

   kpt_shape: [17, 2]  # number of keypoints, number of dimensions

2. Verify Versions: Ensure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

   pip install --upgrade torch ultralytics hub-sdk

3. Run the Notebook: After making the above changes, run your Colab notebook again to see if the issue persists.

Example Code

Here’s a simplified example of how your yolov8n-pose.yaml might look:

# YOLOv8n-pose model configuration
kpt_shape: [17, 2]
...

Additional Resources

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem. This will help us provide more targeted assistance.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[palmcorp]

Hi Paula,

No Joy.

1. Updated data.yaml to data17.yaml

2. Ran a short program to just load the model.

Can you determine from the console printout where the problem is occurring?

Best,

Paul

CODE:

def POSE_LOAD(data_yaml_path):

model = YOLO(data_yaml_path, verbose = True)

model = YOLO('yolov8n-pose.pt') # load a pretrained model (recommended for training)

model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights

return model

print ("short start with Paula")

Data_Yaml_Path = os.path.join ("/content/yaml_files/data17.yaml")

if not os.path.isfile(Data_Yaml_Path):

pdb.set_trace()

model=POSE_LOAD (Data_Yaml_Path)

pdb.set_trace()

CONSOLE:

short start with Paula

               from  n    params  module                                       arguments                     

0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2]

1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]

2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True]

3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]

4 -1 6 2101248 ultralytics.nn.modules.block.C2f [256, 256, 6, True]

5 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2]

6 -1 6 8396800 ultralytics.nn.modules.block.C2f [512, 512, 6, True]

7 -1 1 4720640 ultralytics.nn.modules.conv.Conv [512, 1024, 3, 2]

8 -1 3 17836032 ultralytics.nn.modules.block.C2f [1024, 1024, 3, True]

9 -1 1 2624512 ultralytics.nn.modules.block.SPPF [1024, 1024, 5]

10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

11 [-1, 8] 1 0 ultralytics.nn.modules.conv.Concat [1]

12 -1 3 10132992 ultralytics.nn.modules.block.C2 [2048, 768, 3, False]

13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

14 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]

15 -1 3 4461568 ultralytics.nn.modules.block.C2 [1280, 512, 3, False]

16 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

17 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]

18 -1 3 1149440 ultralytics.nn.modules.block.C2 [768, 256, 3, False]

19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2]

20 [-1, 17] 1 0 ultralytics.nn.modules.conv.Concat [1]

21 -1 3 4330496 ultralytics.nn.modules.block.C2 [1024, 512, 3, False]

22 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2]

23 [-1, 14] 1 0 ultralytics.nn.modules.conv.Concat [1]

24 -1 3 9936384 ultralytics.nn.modules.block.C2 [1792, 768, 3, False]

25 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2]

26 [-1, 11] 1 0 ultralytics.nn.modules.conv.Concat [1]

27 -1 3 18098176 ultralytics.nn.modules.block.C2 [2816, 1024, 3, False]

---

IndexError Traceback (most recent call last)

https://localhost:8080/ in <cell line: 5>()

  3 if not os.path.isfile(Data_Yaml_Path):

  4   pdb.set_trace()

----> 5 model=POSE_LOAD (Data_Yaml_Path)

  6 pdb.set_trace()

---

7 frames

---

https://localhost:8080/ in POSE_LOAD(data_yaml_path)

  1 print ("Model Steps : load/train/recognize ")

  2 def POSE_LOAD(data_yaml_path):

----> 3 model = YOLO(data_yaml_path, verbose = True)

  4   model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)

  5   model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights

/usr/local/lib/python3.10/dist-packages/ultralytics/models/yolo/model.py https://localhost:8080/ in init(self, model, task, verbose)

 21         else:

 22             # Continue with default YOLO initialization

---> 23 super().init(model=model, task=task, verbose=verbose)

 24 

 25     @property

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py https://localhost:8080/ in init(self, model, task, verbose)

148         # Load or create new YOLO model

149         if Path(model).suffix in {".yaml", ".yml"}:

--> 150 self._new(model, task=task, verbose=verbose)

151         else:

152             self._load(model, task=task)

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py https://localhost:8080/ in _new(self, cfg, task, model, verbose)

217         self.cfg = cfg

218         self.task = task or guess_model_task(cfg_dict)

--> 219 self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model

220         self.overrides["model"] = self.cfg

221         self.overrides["task"] = self.task

/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py https://localhost:8080/ in init(self, cfg, ch, nc, data_kpt_shape, verbose)

382             LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")

383             cfg["kpt_shape"] = data_kpt_shape

--> 384 super().init(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

385 

386     def init_criterion(self):

/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py https://localhost:8080/ in init(self, cfg, ch, nc, verbose)

285             LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")

286             self.yaml["nc"] = nc  # override YAML value

--> 287 self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist

288         self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict

289         self.inplace = self.yaml.get("inplace", True)

/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py https://localhost:8080/ in parse_model(d, ch, verbose)

915             c2 = sum(ch[x] for x in f)

916         elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn}:

--> 917 args.append([ch[x] for x in f])

918             if m is Segment:

919                 args[2] = make_divisible(min(args[2], max_channels) * width, 8)

/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py https://localhost:8080/ in (.0)

915             c2 = sum(ch[x] for x in f)

916         elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn}:

--> 917 args.append([ch[x] for x in f])

918             if m is Segment:

919                 args[2] = make_divisible(min(args[2], max_channels) * width, 8)

IndexError: list index out of range

From: Paula Derrenger @.*** Sent: Thursday, June 13, 2024 2:14 PM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

@palmcorp https://github.com/palmcorp hi Paul,

Thank you for sharing the Colab notebook and the data5.yaml file. Let's dive into the issue and work towards a solution.

Key Points to Address

1. Keypoint Shape Mismatch: Your data5.yaml specifies kpt_shape: [5,2], which indicates 5 keypoints. However, if your dataset actually contains 17 keypoints, you should update this to kpt_shape: [17,2].

2. Configuration File: Ensure that your yolov8n-pose.yaml configuration file aligns with the number of keypoints in your dataset. Here’s an example configuration snippet:

3. YOLOv8n-pose model configuration

4. kpt_shape: [17, 2] ...

5. Pretrained Model: If you are loading a pretrained model, ensure that it is compatible with your custom dataset configuration. If you suspect it might be causing issues, try training without loading the pretrained model to see if it resolves the problem.

Next Steps

1. Update data5.yaml: Change the kpt_shape parameter to match the number of keypoints in your dataset:

kpt_shape: [17, 2] # number of keypoints, number of dimensions

2. Verify Versions: Ensure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

pip install --upgrade torch ultralytics hub-sdk

3. Run the Notebook: After making the above changes, run your Colab notebook again to see if the issue persists.

Example Code

Here’s a simplified example of how your yolov8n-pose.yaml might look:

YOLOv8n-pose model configuration

kpt_shape: [17, 2] ...

Additional Resources

If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem. This will help us provide more targeted assistance.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2166806093 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FQZN6B2DWUYPS7HVQ3ZHIDSBAVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNRWHAYDMMBZGM . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FWMY2VCGM2B2WFN7V3ZHIDSBA5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUBE3LE2.gif Message ID: @.***>

[pderrenger]

Hi Paul,

Thank you for the detailed update and for sharing your code and console output. Let's work through this step-by-step to identify and resolve the issue.

Key Observations

1. Keypoint Shape: You mentioned updating data.yaml to data17.yaml. Ensure that kpt_shape in data17.yaml correctly reflects the number of keypoints in your dataset.
2. Error Analysis: The error traceback indicates an IndexError: list index out of range, which suggests there might be a mismatch in the expected input dimensions or configurations.

Steps to Resolve

1. Verify data17.yaml Configuration: Ensure that data17.yaml has the correct kpt_shape parameter:

   kpt_shape: [17, 2]  # number of keypoints, number of dimensions

2. Check Model Configuration: Make sure your yolov8n-pose.yaml file aligns with the data17.yaml configuration. Here’s an example snippet:

   # YOLOv8n-pose model configuration
   kpt_shape: [17, 2]
   ...

3. Update and Verify Package Versions: Ensure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

   pip install --upgrade torch ultralytics hub-sdk

4. Simplify Model Loading: Simplify your model loading function to isolate the issue. Here’s a revised version:

   from ultralytics import YOLO
   
   def POSE_LOAD(data_yaml_path):
      model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
      return model
   
   print("short start with Paula")
   Data_Yaml_Path = "/content/yaml_files/data17.yaml"
   
   if not os.path.isfile(Data_Yaml_Path):
      raise FileNotFoundError(f"{Data_Yaml_Path} not found")
   
   model = POSE_LOAD(Data_Yaml_Path)

5. Run the Notebook: After making the above changes, run your Colab notebook again to see if the issue persists.

Additional Debugging

If the issue persists, please provide the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[palmcorp]

Hi Paula,

Is it correct that kpt_shape: [17, 2] appears in multiple .yaml files? I’ll check to confirm that we are consistent. Will also --upgrade torch ultralytics hub-sdk in every notebook.

Thanks

Paul

From: Paula Derrenger @.*** Sent: Friday, June 14, 2024 1:02 AM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hi Paul,

Thank you for the detailed update and for sharing your code and console output. Let's work through this step-by-step to identify and resolve the issue.

Key Observations

1. Keypoint Shape: You mentioned updating data.yaml to data17.yaml. Ensure that kpt_shape in data17.yaml correctly reflects the number of keypoints in your dataset.
2. Error Analysis: The error traceback indicates an IndexError: list index out of range, which suggests there might be a mismatch in the expected input dimensions or configurations.

Steps to Resolve

1. Verify data17.yaml Configuration: Ensure that data17.yaml has the correct kpt_shape parameter:

kpt_shape: [17, 2] # number of keypoints, number of dimensions

2. Check Model Configuration: Make sure your yolov8n-pose.yaml file aligns with the data17.yaml configuration. Here’s an example snippet:

3. YOLOv8n-pose model configuration

4. kpt_shape: [17, 2] ...

5. Update and Verify Package Versions: Ensure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

pip install --upgrade torch ultralytics hub-sdk

6. Simplify Model Loading: Simplify your model loading function to isolate the issue. Here’s a revised version:

7. from ultralytics import YOLO

9. def POSE_LOAD(data_yaml_path):

10. model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights

11. return model

12. 13.print("short start with Paula") 14.Data_Yaml_Path = "/content/yaml_files/data17.yaml"

13. 16.if not os.path.isfile(Data_Yaml_Path):

14. raise FileNotFoundError(f"{Data_Yaml_Path} not found")

15. model = POSE_LOAD(Data_Yaml_Path)

16. Run the Notebook: After making the above changes, run your Colab notebook again to see if the issue persists.

Additional Debugging

If the issue persists, please provide the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2167474193 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FWTH4X6QPDBXYS33FLZHKPQBAVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNRXGQ3TIMJZGM . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FT6GBALUP3LTQEC4JTZHKPQBA5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUBGEEBC.gif Message ID: @.***>

[pderrenger]

Hi Paul,

Thank you for your proactive approach! Yes, it is crucial that the kpt_shape: [17, 2] parameter is consistent across all relevant .yaml files to avoid any configuration mismatches. Ensuring uniformity in these settings will help prevent errors related to keypoint dimensions.

Steps to Follow

1. Consistency Check: Verify that kpt_shape: [17, 2] is consistently set in all your configuration files, including data17.yaml and yolov8n-pose.yaml.

2. Package Upgrades: Upgrading torch, ultralytics, and hub-sdk in every notebook is a great step. You can do this with:

   pip install --upgrade torch ultralytics hub-sdk

3. Simplified Model Loading: Ensure your model loading function is straightforward to isolate any potential issues. Here’s a reminder of the simplified function:

   from ultralytics import YOLO
   
   def POSE_LOAD(data_yaml_path):
      model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
      return model
   
   print("short start with Paula")
   Data_Yaml_Path = "/content/yaml_files/data17.yaml"
   
   if not os.path.isfile(Data_Yaml_Path):
      raise FileNotFoundError(f"{Data_Yaml_Path} not found")
   
   model = POSE_LOAD(Data_Yaml_Path)

Additional Debugging

If the issue persists after these steps, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[palmcorp]

Hello Paula,

Now this is confusing. I substituted COCO images + labels for mine in the test and still get the following errors:

Freezing layer 'model.22.dfl.conv.weight'

train: Scanning /content/datasets/echo-pose/labels/train... 2 images, 0 backgrounds, 2 corrupt: 100%|██████████| 2/2 [00:00<00:00, 114.30it/s]

train: WARNING ⚠️ /content/datasets/echo-pose/images/train/000000000001.jpg: ignoring corrupt image/label: labels require 39 columns each

I put a copy on Github if you want to look at it LMK.

Here are the code snippits again:

Also noticed some overload: (what determines the number of channels? (nc=5)?)

Overriding model.yaml kpt_shape=[17, 3] with kpt_shape=[17, 2]

Overriding model.yaml nc=1 with nc=5

print ("Model Steps : load/train/recognize ")

def POSE_LOAD(data_yaml_path):

model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights

return model

def POSE_TRAIN(model, model_yaml_path):

model.train(data=model_yaml_path, epochs=EPOCHS, imgsz=640)

return model

def POSE_VAL(best_path):

if best_path == None:

model = YOLO('yolov8n-pose.pt')

metrics =2

else:

model = YOLO(best_path)

# Validate the model

metrics = model.val(data=best_path)  # no arguments needed, dataset and settings remembered

metrics.box.map    # map50-95

metrics.box.map50  # map50

metrics.box.map75  # map75

metrics.box.maps

return metrics

Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt to 'yolov8n-pose.pt'...

100%|██████████| 6.51M/6.51M [00:00<00:00, 67.7MB/s]

Transferred 397/397 items from pretrained weights

PYDEV DEBUGGER WARNING:

sys.settrace() should not be used when the debugger is being used.

This may cause the debugger to stop working correctly.

If this is needed, please check:

http://pydev.blogspot.com/2007/06/why-cant-pydev-debugger-work-with.html

to see how to restore the debug tracing back correctly.

Call Location:

File "/usr/lib/python3.10/bdb.py", line 336, in set_trace

sys.settrace(self.trace_dispatch)

--Return--

None

(7)()

  5   pdb.set_trace()

  6 model=POSE_LOAD (Data_Yaml_Path)

----> 7 pdb.set_trace()

  8 

  9 #Model_Yaml_Path =os.path.join (PROJ_BASE,YAML_DIR,YOLO17)

ipdb> print Data_Yaml_Path)

*** SyntaxError: unmatched ')'

ipdb> print (Data_Yaml_Path)

/content/yaml_files/dataP17.yaml

ipdb> l

  2 #Data_Yaml_Path = os.path.join (PROJ_BASE,YAML_DIR, DATA17)

  3 Data_Yaml_Path = os.path.join (PROJ_BASE,YAML_DIR, DATA17P)

  4 if not os.path.isfile(Data_Yaml_Path):

  5   pdb.set_trace()

  6 model=POSE_LOAD (Data_Yaml_Path)

----> 7 pdb.set_trace()

  8 

  9 #Model_Yaml_Path =os.path.join (PROJ_BASE,YAML_DIR,YOLO17)

 10 Model_Yaml_Path =os.path.join ("/content/yaml_files/dataP17.yaml")

 11 if not os.path.isfile(Model_Yaml_Path):

 12   pdb.set_trace()

ipdb> c

PYDEV DEBUGGER WARNING:

sys.settrace() should not be used when the debugger is being used.

This may cause the debugger to stop working correctly.

If this is needed, please check:

http://pydev.blogspot.com/2007/06/why-cant-pydev-debugger-work-with.html

to see how to restore the debug tracing back correctly.

Call Location:

File "/usr/lib/python3.10/bdb.py", line 347, in set_continue

sys.settrace(None)

Ultralytics YOLOv8.2.32 🚀 Python-3.10.12 torch-2.3.0+cu121 CPU (Intel Xeon 2.20GHz)

engine/trainer: task=pose, mode=train, model=yolov8n-pose.yaml, data=/content/yaml_files/dataP17.yaml, epochs=2, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, 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=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/pose/train

Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...

100%|██████████| 755k/755k [00:00<00:00, 14.6MB/s]

Overriding model.yaml kpt_shape=[17, 3] with kpt_shape=[17, 2]

Overriding model.yaml nc=1 with nc=5

               from  n    params  module                                       arguments                     

0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]

1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]

2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]

3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]

4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]

5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]

6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]

7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]

8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]

9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]

10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]

12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]

13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]

15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]

16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]

17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]

18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]

19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]

20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]

21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]

22 [15, 18, 21] 1 924565 ultralytics.nn.modules.head.Pose [5, [17, 2], [64, 128, 256]]

YOLOv8n-pose summary: 250 layers, 3184101 parameters, 3184085 gradients, 8.8 GFLOPs

Transferred 355/397 items from pretrained weights

TensorBoard: Start with 'tensorboard --logdir runs/pose/train', view at http://localhost:6006/

Freezing layer 'model.22.dfl.conv.weight'

train: Scanning /content/datasets/echo-pose/labels/train... 2 images, 0 backgrounds, 2 corrupt: 100%|██████████| 2/2 [00:00<00:00, 114.30it/s]

train: WARNING ⚠️ /content/datasets/echo-pose/images/train/000000000001.jpg: ignoring corrupt image/label: labels require 39 columns each

train: WARNING ⚠️ /content/datasets/echo-pose/images/train/000000000016.jpg: ignoring corrupt image/label: labels require 39 columns each

train: New cache created: /content/datasets/echo-pose/labels/train.cache

WARNING ⚠️ No images found in /content/datasets/echo-pose/labels/train.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance.

---

ValueError Traceback (most recent call last)

https://localhost:8080/ in <cell line: 13>()

 11 if not os.path.isfile(Model_Yaml_Path):

 12   pdb.set_trace()

---> 13 POSE_TRAIN (model,Data_Yaml_Path)

---

10 frames

---

https://localhost:8080/ in POSE_TRAIN(model, model_yaml_path)

 13 

 14 def POSE_TRAIN(model, model_yaml_path):

---> 15 model.train(data=model_yaml_path, epochs=EPOCHS, imgsz=640)

 16   return model

17 

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py https://localhost:8080/ in train(self, trainer, **kwargs)

672 

673         self.trainer.hub_session = self.session  # attach optional HUB session

--> 674 self.trainer.train()

675         # Update model and cfg after training

676         if RANK in {-1, 0}:

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py https://localhost:8080/ in train(self)

197 

198         else:

--> 199 self._do_train(world_size)

200 

201     def _setup_scheduler(self):

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py https://localhost:8080/ in _do_train(self, world_size)

316         if world_size > 1:

317             self._setup_ddp(world_size)

--> 318 self._setup_train(world_size)

319 

320         nb = len(self.train_loader)  # number of batches

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py https://localhost:8080/ in _setup_train(self, world_size)

280         # Dataloaders

281         batch_size = self.batch_size // max(world_size, 1)

--> 282 self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode="train")

283         if RANK in {-1, 0}:

284             # Note: When training DOTA dataset, double batch size could get OOM on images with >2000 objects.

/usr/local/lib/python3.10/dist-packages/ultralytics/models/yolo/detect/train.py https://localhost:8080/ in get_dataloader(self, dataset_path, batch_size, rank, mode)

 47         assert mode in {"train", "val"}, f"Mode must be 'train' or 'val', not {mode}."

 48         with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP

---> 49 dataset = self.build_dataset(dataset_path, mode, batch_size)

 50         shuffle = mode == "train"

 51         if getattr(dataset, "rect", False) and shuffle:

/usr/local/lib/python3.10/dist-packages/ultralytics/models/yolo/detect/train.py https://localhost:8080/ in build_dataset(self, img_path, mode, batch)

 41         """

 42         gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)

---> 43 return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs)

 44 

 45     def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):

/usr/local/lib/python3.10/dist-packages/ultralytics/data/build.py https://localhost:8080/ in build_yolo_dataset(cfg, img_path, batch, data, mode, rect, stride, multi_modal)

 85     """Build YOLO Dataset."""

 86     dataset = YOLOMultiModalDataset if multi_modal else YOLODataset

---> 87 return dataset(

 88         img_path=img_path,

 89         imgsz=cfg.imgsz,

/usr/local/lib/python3.10/dist-packages/ultralytics/data/dataset.py https://localhost:8080/ in init(self, data, task, *args, **kwargs)

 62         self.data = data

 63         assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."

---> 64 super().init(*args, **kwargs)

 65 

 66     def cache_labels(self, path=Path("./labels.cache")):

/usr/local/lib/python3.10/dist-packages/ultralytics/data/base.py https://localhost:8080/ in init(self, img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls, classes, fraction)

 72         self.fraction = fraction

 73         self.im_files = self.get_img_files(self.img_path)

---> 74 self.labels = self.get_labels()

 75         self.update_labels(include_class=classes)  # single_cls and include_class

 76         self.ni = len(self.labels)  # number of images

/usr/local/lib/python3.10/dist-packages/ultralytics/data/dataset.py https://localhost:8080/ in get_labels(self)

159         # Check if the dataset is all boxes or all segments

160         lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels)

--> 161 len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))

162         if len_segments and len_boxes != len_segments:

163             LOGGER.warning(

ValueError: not enough values to unpack (expected 3, got 0)

From: Paula Derrenger @.*** Sent: Friday, June 14, 2024 3:00 PM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hi Paul,

Thank you for your proactive approach! Yes, it is crucial that the kpt_shape: [17, 2] parameter is consistent across all relevant .yaml files to avoid any configuration mismatches. Ensuring uniformity in these settings will help prevent errors related to keypoint dimensions.

Steps to Follow

1. Consistency Check: Verify that kpt_shape: [17, 2] is consistently set in all your configuration files, including data17.yaml and yolov8n-pose.yaml.

2. Package Upgrades: Upgrading torch, ultralytics, and hub-sdk in every notebook is a great step. You can do this with:

pip install --upgrade torch ultralytics hub-sdk

3. Simplified Model Loading: Ensure your model loading function is straightforward to isolate any potential issues. Here’s a reminder of the simplified function:

4. from ultralytics import YOLO

6. def POSE_LOAD(data_yaml_path):

7. model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights

8. return model

9. 10.print("short start with Paula") 11.Data_Yaml_Path = "/content/yaml_files/data17.yaml"

10. 13.if not os.path.isfile(Data_Yaml_Path):

11. raise FileNotFoundError(f"{Data_Yaml_Path} not found")

12. model = POSE_LOAD(Data_Yaml_Path)

Additional Debugging

If the issue persists after these steps, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2168816388 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FTYPRIRZHGAS3U24OLZHNRWBAVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNRYHAYTMMZYHA . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FRRPAWDXQ5COL42PATZHNRWBA5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUBIWBQI.gif Message ID: @.***>

[pderrenger]

Hi Paul,

Thank you for your detailed message and for sharing the code snippets and error logs. Let's address the issues step-by-step to help you resolve them.

Key Observations and Steps to Resolve

1. Corrupt Images/Labels: The warning indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. This discrepancy is likely causing the training to fail.

   • Verify Label Format: Ensure that your label files are correctly formatted. For pose estimation, each label should include the class and keypoint coordinates. If you are using 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns.
   • Check Dataset: Double-check your dataset to ensure that all images and labels are correctly paired and formatted.

2. Configuration Consistency: Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

3. Package Upgrades: Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

   pip install --upgrade torch ultralytics hub-sdk

4. Simplified Model Loading: Simplify your model loading function to isolate the issue. Here’s a revised version:

   from ultralytics import YOLO
   
   def POSE_LOAD():
      model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
      return model
   
   print("short start with Paula")
   model = POSE_LOAD()

Example Code for Label Verification

Here’s a small script to verify the format of your label files:

import os

def verify_labels(label_dir, expected_columns=39):
    for label_file in os.listdir(label_dir):
        if label_file.endswith('.txt'):
            with open(os.path.join(label_dir, label_file), 'r') as f:
                for line in f:
                    columns = line.strip().split()
                    if len(columns) != expected_columns:
                        print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.")
                        return False
    return True

label_dir = '/content/datasets/echo-pose/labels/train'
if verify_labels(label_dir):
    print("All label files are correctly formatted.")
else:
    print("Some label files are incorrectly formatted.")

Next Steps

1. Verify Label Format: Run the above script to ensure your label files are correctly formatted.
2. Consistency Check: Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.
3. Upgrade Packages: Upgrade torch, ultralytics, and hub-sdk to the latest versions.
4. Simplify Model Loading: Use the simplified model loading function provided above.

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[palmcorp]

Hi Paula,

Thanks for your persistence and help.

The images and labels were extracted from a running COCO project like the following:

https://docs.deci.ai/super-gradients/latest/YOLONAS-POSE.html

https://github.com/ultralytics/ultralytics/issues/1915

https://www.youtube.com/watch?v=m9fH9OWn8YM

So the label & image should be correct and yet they are being rejected. Check out the enclosed.

There has to be some configuration problem but it is not visible to me.

LMK if you see any logic bugs or can point me to some use cases.

Best,

Paul

From: Paula Derrenger @.*** Sent: Saturday, June 15, 2024 7:02 PM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hi Paul,

Thank you for your detailed message and for sharing the code snippets and error logs. Let's address the issues step-by-step to help you resolve them.

Key Observations and Steps to Resolve

1. Corrupt Images/Labels: The warning indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. This discrepancy is likely causing the training to fail.

• Verify Label Format: Ensure that your label files are correctly formatted. For pose estimation, each label should include the class and keypoint coordinates. If you are using 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns.
• Check Dataset: Double-check your dataset to ensure that all images and labels are correctly paired and formatted.

2. Configuration Consistency: Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

3. Package Upgrades: Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

pip install --upgrade torch ultralytics hub-sdk

4. Simplified Model Loading: Simplify your model loading function to isolate the issue. Here’s a revised version:

5. from ultralytics import YOLO

7. def POSE_LOAD():

8. model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights

9. return model

10. 11.print("short start with Paula") model = POSE_LOAD()

Example Code for Label Verification

Here’s a small script to verify the format of your label files:

import os

def verify_labels(label_dir, expected_columns=39): for label_file in os.listdir(label_dir): if label_file.endswith('.txt'): with open(os.path.join(label_dir, label_file), 'r') as f: for line in f: columns = line.strip().split() if len(columns) != expected_columns: print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.") return False return True

label_dir = '/content/datasets/echo-pose/labels/train' if verify_labels(label_dir): print("All label files are correctly formatted.") else: print("Some label files are incorrectly formatted.")

Next Steps

1. Verify Label Format: Run the above script to ensure your label files are correctly formatted.
2. Consistency Check: Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.
3. Upgrade Packages: Upgrade torch, ultralytics, and hub-sdk to the latest versions.
4. Simplify Model Loading: Use the simplified model loading function provided above.

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2171011174 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FXOFH57YUDTRGBDLH3ZHTWZ7AVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNZRGAYTCMJXGQ . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FVF4FYGG4XS5KK2C2TZHTWZ7A5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUBM4AGM.gif Message ID: @.***>

0 0.666952 0.484420 0.666096 0.766320 0.552553 0.210000 2.000000 0.585586 0.194000 2.000000 0.525526 0.188000 2.000000 0.000000 0.000000 0.000000 0.477477 0.194000 2.000000 0.717718 0.350000 2.000000 0.393393 0.372000 2.000000 0.762763 0.490000 2.000000 0.402402 0.522000 1.000000 0.645646 0.546000 2.000000 0.000000 0.000000 0.000000 0.717718 0.608000 2.000000 0.513514 0.620000 2.000000 0.963964 0.694000 2.000000 0.780781 0.744000 2.000000 0.000000 0.000000 0.000000 0.453453 0.778000 1.000000

0 0.671279 0.617945 0.645759 0.726859 0.519751 0.381250 2.000000 0.550936 0.348438 2.000000 0.488565 0.367188 2.000000 0.642412 0.354687 2.000000 0.488565 0.395313 2.000000 0.738046 0.526563 2.000000 0.446985 0.534375 2.000000 0.846154 0.771875 2.000000 0.442827 0.812500 2.000000 0.925156 0.964063 2.000000 0.507277 0.698438 2.000000 0.702703 0.942187 2.000000 0.555094 0.950000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

[pderrenger]

Hi Paul,

Thank you for your persistence and for providing additional context. Let's work through this together to identify and resolve the issue.

Key Observations

1. Label Format: The error message indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. For pose estimation with 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns. It seems there might be a discrepancy here.

2. Configuration Consistency: Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

3. Package Versions: Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

   pip install --upgrade torch ultralytics hub-sdk

Steps to Resolve

1. Verify Label Format: Run the following script to ensure your label files are correctly formatted:

   import os
   
   def verify_labels(label_dir, expected_columns=35):
      for label_file in os.listdir(label_dir):
          if label_file.endswith('.txt'):
              with open(os.path.join(label_dir, label_file), 'r') as f:
                  for line in f:
                      columns = line.strip().split()
                      if len(columns) != expected_columns:
                          print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.")
                          return False
      return True
   
   label_dir = '/content/datasets/echo-pose/labels/train'
   if verify_labels(label_dir):
      print("All label files are correctly formatted.")
   else:
      print("Some label files are incorrectly formatted.")

2. Consistency Check: Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.

3. Simplify Model Loading: Use the simplified model loading function provided earlier to isolate the issue:

   from ultralytics import YOLO
   
   def POSE_LOAD():
      model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
      return model
   
   print("short start with Paula")
   model = POSE_LOAD()

Additional Debugging

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[palmcorp]

Hi Peggy

Thinks again. Let’s look at the steps outlined.

1. Label Format

For a limited run I ran 2 image and 2 labels . here is the breakdown of the first label 000000000001.txt (attached). I see class=0, bounding box = 0.666952 0.484420 0.666096 0.766320 followed by 17 2 keypoints. Where am I going wrong?

0

0.666952 0.484420 0.666096 0.766320

0.552553 0.210000 2.000000

0.585586 0.194000 2.000000

0.525526 0.188000 2.000000

0.000000 0.000000 0.000000

0.477477 0.194000 2.000000

0.717718 0.350000 2.000000

0.393393 0.372000 2.000000

0.762763 0.490000 2.000000

0.402402 0.522000 1.000000

0.645646 0.546000 2.000000

0.000000 0.000000 0.000000

0.717718 0.608000 2.000000

0.513514 0.620000 2.000000

0.963964 0.694000 2.000000

0.780781 0.744000 2.000000

0.000000 0.000000 0.000000

0.453453 0.778000 1.000000

2. Configuration Consistency

   Following are the snippets from datap.yaml and yolovn-poseVp.yaml that deal with kpt_shape:

File: kpt_shape

dagaP17.yaml kpt_shape: [17,2] # number of keypoints, number of dim

yolov8n-poseVp.yaml kpt_shape: [17, 3]

I changed yolv8n-poseVp.yaml to [17.2]

3. Ran the following at start:

pip install --upgrade torch ultralytics hub-sdk

I’M at a loss: here is the error report:

Attached are the yaml files and the first image & label? I don’t see the problem.

Ultralytics YOLOv8.2.32 🚀 Python-3.10.12 torch-2.3.0+cu121 CPU (Intel Xeon 2.20GHz)

engine/trainer: task=pose, mode=train, model=yolov8n-pose.yaml, data=/content/yaml_files/yolov8n-poseVp.yaml, epochs=2, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, 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=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/pose/train

Downloading https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...

100%|██████████| 755k/755k [00:00<00:00, 14.8MB/s]

Overriding model.yaml kpt_shape=[17, 3] with kpt_shape=[17, 2]

               from  n    params  module                                       arguments                     

0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]

1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]

2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]

3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]

4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]

5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]

6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]

7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]

8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]

9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]

10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]

12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]

13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']

14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]

15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]

16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]

17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]

18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]

19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]

20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]

21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]

22 [15, 18, 21] 1 923785 ultralytics.nn.modules.head.Pose [1, [17, 2], [64, 128, 256]]

YOLOv8n-pose summary: 250 layers, 3183321 parameters, 3183305 gradients, 8.8 GFLOPs

Transferred 361/397 items from pretrained weights

TensorBoard: Start with 'tensorboard --logdir runs/pose/train', view at http://localhost:6006/ http://localhost:6006/

Freezing layer 'model.22.dfl.conv.weight'

train: Scanning /content/datasets/echo-pose/labels/train... 2 images, 0 backgrounds, 2 corrupt: 100%|██████████| 2/2 [00:00<00:00, 85.21it/s]

train: WARNING ⚠️ /content/datasets/echo-pose/images/train/000000000001.jpg: ignoring corrupt image/label: labels require 39 columns each

train: WARNING ⚠️ /content/datasets/echo-pose/images/train/000000000016.jpg: ignoring corrupt image/label: labels require 39 columns each

train: New cache created: /content/datasets/echo-pose/labels/train.cache

WARNING ⚠️ No images found in /content/datasets/echo-pose/labels/train.cache, training may not work correctly. See https://docs.ultralytics.com/datasets https://docs.ultralytics.com/datasets for dataset formatting guidance.

---

ValueError Traceback (most recent call last)

https://localhost:8080/ in <cell line: 13>()

 11 if not os.path.isfile(Model_Yaml_Path):

 12   pdb.set_trace()

---> 13 POSE_TRAIN (model,Model_Yaml_Path)

---

10 frames

---

https://localhost:8080/ /usr/local/lib/python3.10/dist-packages/ultralytics/data/dataset.py in get_labels(self)

159         # Check if the dataset is all boxes or all segments

160         lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels)

--> 161 len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))

162         if len_segments and len_boxes != len_segments:

163             LOGGER.warning(

ValueError: not enough values to unpack (expected 3, got 0)

Hi Paul,

Thank you for your persistence and for providing additional context. Let's work through this together to identify and resolve the issue.

Key Observations

1. Label Format: The error message indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. For pose estimation with 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns. It seems there might be a discrepancy here.

2. Configuration Consistency: Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

3. Package Versions: Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

pip install --upgrade torch ultralytics hub-sdk

Steps to Resolve

1. Verify Label Format: Run the following script to ensure your label files are correctly formatted:

2. import os

4. def verify_labels(label_dir, expected_columns=35):

5. for label_file in os.listdir(label_dir):

6. if label_file.endswith('.txt'):

7. with open(os.path.join(label_dir, label_file), 'r') as f:

8. for line in f:

9. columns = line.strip().split()

10. if len(columns) != expected_columns:

11. print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.")

12. return False

13. return True

14. 15.label_dir = '/content/datasets/echo-pose/labels/train' 16.if verify_labels(label_dir):

15. print("All label files are correctly formatted.") 18.else: print("Some label files are incorrectly formatted.")

16. Consistency Check: Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.

17. Simplify Model Loading: Use the simplified model loading function provided earlier to isolate the issue:

21.from ultralytics import YOLO

22. 23.def POSE_LOAD():

23. model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights
24. return model

25. 27.print("short start with Paula") model = POSE_LOAD()

Additional Debugging

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2171270321 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FXANSDEJ7EJK3OFJO3ZHVHEVAVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNZRGI3TAMZSGE . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FVCIWJD3DY3F5MYUCLZHVHEVA5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUBNL2LC.gif Message ID: @.***>

0 0.666952 0.484420 0.666096 0.766320 0.552553 0.210000 2.000000 0.585586 0.194000 2.000000 0.525526 0.188000 2.000000 0.000000 0.000000 0.000000 0.477477 0.194000 2.000000 0.717718 0.350000 2.000000 0.393393 0.372000 2.000000 0.762763 0.490000 2.000000 0.402402 0.522000 1.000000 0.645646 0.546000 2.000000 0.000000 0.000000 0.000000 0.717718 0.608000 2.000000 0.513514 0.620000 2.000000 0.963964 0.694000 2.000000 0.780781 0.744000 2.000000 0.000000 0.000000 0.000000 0.453453 0.778000 1.000000

[palmcorp]

Hi Paula,

Thanks for the verifier, great idea.

This version might work better. Added consideration for the and fields. Also need to consider 2D and 3D keypoints.

Best

Paul

= = = =

FROM="Paula email 6-16-24"

import os, pdb

VERSION= "0.2"

KEYSIZE=3 # FOR 2D keys, otherwie 4

def verify_labels(label_dir, expected_columns=35):

print ("chcking labels for: ", label_dir)

for label_file in os.listdir(label_dir):

  if label_file.endswith('.txt'):

    with open(os.path.join(label_dir, label_file), 'r') as f:

      for line in f:

        columns = line.strip().split()

        print ("class= ", columns[0])

        print ("B BOX= ", columns[1:5])

        HEADER = 5

        Nkeypoints =int (len(columns[5:len(columns)])/KEYSIZE)

        if Nkeypoints != expected_columns:

                                   print(f"Label file {label_file} has {len(columns)-HEADER} columns, expected {expected_columns} columns.")

                                   return False

return True

print (" Label checker V", VERSION, " FROM: ", FROM, "\n")

label_dir=ld = '/content/datasets/echo-pose/labels/train'

if not os.path.exists (ld):

print (ld, "doesnt exist")

if verify_labels(label_dir):

               print("All label files are correctly formatted.")

else:

        print("Some label files are incorrectly formatted.")

From: Paula Derrenger @.*** Sent: Sunday, June 16, 2024 1:55 AM To: ultralytics/hub Cc: Paul Petronelli; Mention Subject: Re: [ultralytics/hub] 'yolov8n-pose.yaml' (Issue #721)

Hi Paul,

Thank you for your persistence and for providing additional context. Let's work through this together to identify and resolve the issue.

Key Observations

1. Label Format: The error message indicates that your labels require 39 columns each, but the provided labels do not meet this requirement. For pose estimation with 17 keypoints, each label should have 1 class column + 17 keypoints * 2 (x, y coordinates) = 35 columns. It seems there might be a discrepancy here.

2. Configuration Consistency: Ensure that kpt_shape: [17, 2] is consistently set in all relevant .yaml files, including data17.yaml and yolov8n-pose.yaml.

3. Package Versions: Make sure you are using the latest versions of torch, ultralytics, and hub-sdk. You can upgrade your packages using:

pip install --upgrade torch ultralytics hub-sdk

Steps to Resolve

1. Verify Label Format: Run the following script to ensure your label files are correctly formatted:

2. import os

4. def verify_labels(label_dir, expected_columns=35):

5. for label_file in os.listdir(label_dir):

6. if label_file.endswith('.txt'):

7. with open(os.path.join(label_dir, label_file), 'r') as f:

8. for line in f:

9. columns = line.strip().split()

10. if len(columns) != expected_columns:

11. print(f"Label file {label_file} has {len(columns)} columns, expected {expected_columns} columns.")

12. return False

13. return True

14. 15.label_dir = '/content/datasets/echo-pose/labels/train' 16.if verify_labels(label_dir):

15. print("All label files are correctly formatted.") 18.else: print("Some label files are incorrectly formatted.")

16. Consistency Check: Ensure kpt_shape: [17, 2] is consistent across all relevant .yaml files.

17. Simplify Model Loading: Use the simplified model loading function provided earlier to isolate the issue:

21.from ultralytics import YOLO

22. 23.def POSE_LOAD():

23. model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights
24. return model

25. 27.print("short start with Paula") model = POSE_LOAD()

Additional Debugging

If the issue persists, please share the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/hub/issues/721#issuecomment-2171270321 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ABXH6FXANSDEJ7EJK3OFJO3ZHVHEVAVCNFSM6AAAAABJFEIHZKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNZRGI3TAMZSGE . You are receiving this because you were mentioned. https://github.com/notifications/beacon/ABXH6FVCIWJD3DY3F5MYUCLZHVHEVA5CNFSM6AAAAABJFEIHZKWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTUBNL2LC.gif Message ID: @.***>

[pderrenger]

Hi Paul,

Thank you for your continued efforts and for sharing the updated label verification script. It's great to see your proactive approach in refining the solution. Let's address the current situation and ensure we get everything working smoothly.

Key Observations

1. Label Format: Your label format seems correct with the class, bounding box, and keypoints. However, the error message indicates a requirement of 39 columns, which suggests there might be an additional dimension or configuration mismatch.

2. Configuration Consistency: Ensuring kpt_shape: [17, 2] is consistent across all relevant .yaml files is crucial. It looks like you've already made this adjustment.

3. Package Versions: You've upgraded the packages, which is excellent. This ensures compatibility and access to the latest features and fixes.

Steps to Resolve

1. Enhanced Label Verification Script: Your updated script is a good start. Here’s a refined version that considers both 2D and 3D keypoints:

   import os
   
   VERSION = "0.2"
   KEYSIZE = 3  # FOR 2D keys, otherwise 4
   
   def verify_labels(label_dir, expected_columns=35):
      print("Checking labels for:", label_dir)
      for label_file in os.listdir(label_dir):
          if label_file.endswith('.txt'):
              with open(os.path.join(label_dir, label_file), 'r') as f:
                  for line in f:
                      columns = line.strip().split()
                      print("class=", columns[0])
                      print("B BOX=", columns[1:5])
                      HEADER = 5
                      Nkeypoints = int(len(columns[5:]) / KEYSIZE)
                      if Nkeypoints != expected_columns:
                          print(f"Label file {label_file} has {len(columns) - HEADER} columns, expected {expected_columns} columns.")
                          return False
      return True
   
   print("Label checker V", VERSION, "\n")
   label_dir = '/content/datasets/echo-pose/labels/train'
   if not os.path.exists(label_dir):
      print(label_dir, "doesn't exist")
   
   if verify_labels(label_dir):
      print("All label files are correctly formatted.")
   else:
      print("Some label files are incorrectly formatted.")

2. Check for Additional Dimensions: Ensure that your labels and configuration files are not expecting an additional dimension (e.g., visibility or confidence score for each keypoint). This might explain the discrepancy in the expected number of columns.

3. Simplified Model Loading: Use the simplified model loading function to isolate the issue:

   from ultralytics import YOLO
   
   def POSE_LOAD():
      model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt')  # build from YAML and transfer weights
      return model
   
   print("short start with Paula")
   model = POSE_LOAD()

Additional Debugging

If the issue persists, please provide the specific error messages and any additional context. This will help us diagnose the problem more effectively.

Thank you for your patience and cooperation. We're here to help you get this resolved! 😊

[github-actions[bot]]

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