How to properly load custom trained model with only 1 label

[AceMcAwesome77]

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Question

I followed the very helpful description for how to train a custom model, and successfully trained a yolo-v3 model with only 1 object type. However when I run the following code to load my model on a different system:

weights_path = '\runs\train\exp10\weights\best.pt' model = torch.hub.load('ultralytics/yolov3', 'yolov3', weights_path) # custom trained model model.eval() results = model(image)

The results produced seem to have all the original yolo labels still in place, such as 'cat', etc. And the label numbers are 15, 27,etc, whereas they should only be 0 for the single label. What is the proper way to load the saved model so that the correct number of output labels and label name I trained on is produced? Thanks!

***Update - I've also tried this, after some further googling of this problem:

model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=False, classes=1) weights = torch.load(weights_path)['model'] model.load_state_dict(weights.state_dict())

but this causes the following error:

file "", line 1, in model.load_state_dict(weights.state_dict()) File "C:\Users\robert.harris\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1604, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for AutoShape: Missing key(s) in state_dict: "model.model.0.conv.weight", "model.model.0.bn.weight", "model.model.0.bn.bias", "model.model.0.bn.running_mean", "model.model.0.bn.running_var","model.model.1.conv.weight", "model.model.1.bn.weight", "model.model.1.bn.bias", "model.model.1.bn.running_mean", "model.model.1.bn.running_var", "model.model.2.cv1.conv.weight", "model.model.2.cv1.bn.weight", "model.model.2.cv1.bn.bias", "model.model.2.cv1.bn.running_mean", "model.model.2.cv1.bn.running_var", "model.model.2.cv2.conv.weight", "model.model.2.cv2.bn.weight", "model.model.2.cv2.bn.bias", "model.model.2.cv2.bn.running_mean", "model.model.2.cv2.bn.running_var", "model.model.3.conv.weight", "model.model.3.bn.weight", "model.model.3.bn.bias", "model.model, .... [300 or so lines of this continued] .... 27.1.cv1.bn.num_batches_tracked", "model.27.1.cv2.conv.weight", "model.27.1.cv2.bn.weight", "model.27.1.cv2.bn.bias", "model.27.1.cv2.bn.running_mean", "model.27.1.cv2.bn.running_var", "model.27.1.cv2.bn.num_batches_tracked", "model.28.anchors", "model.28.m.0.weight", "model.28.m.0.bias", "model.28.m.1.weight", "model.28.m.1.bias", "model.28.m.2.weight", "model.28.m.2.bias".

so I'm still looking for a working solution. Thanks!

Additional

No response

[github-actions[bot]]

👋 Hello @AceMcAwesome77, thank you for your interest in YOLOv3 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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

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

For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at [email protected].

Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov3
$ cd yolov3
$ pip install -r requirements.txt

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• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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[glenn-jocher]

@AceMcAwesome77 I'd recommend using https://github.com/ultralytics/yolov5. You can train YOLOv3 models there as well using yolov3.yaml.

From the YOLOv5 custom training tutorial:

👋 Hello! Thanks for asking about YOLOv5 🚀 dataset formatting. To train correctly your data must be in YOLOv5 format. Please see our Train Custom Data tutorial for full documentation on dataset setup and all steps required to start training your first model. A few excerpts from the tutorial:

1.1 Create dataset.yaml

COCO128 is an example small tutorial dataset composed of the first 128 images in COCO train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. data/coco128.yaml, shown below, is the dataset config file that defines 1) the dataset root directory path and relative paths to train / val / test image directories (or *.txt files with image paths) and 2) a class names dictionary:

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes (80 COCO classes)
names:
  0: person
  1: bicycle
  2: car
  ...
  77: teddy bear
  78: hair drier
  79: toothbrush

1.2 Create Labels

After using a tool like Roboflow Annotate to label your images, export your labels to YOLO format, with one *.txt file per image (if no objects in image, no *.txt file is required). The *.txt file specifications are:

• One row per object
• Each row is class x_center y_center width height format.
• Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide x_center and width by image width, and y_center and height by image height.
• Class numbers are zero-indexed (start from 0).

The label file corresponding to the above image contains 2 persons (class 0) and a tie (class 27):

1.3 Organize Directories

Organize your train and val images and labels according to the example below. YOLOv5 assumes /coco128 is inside a /datasets directory next to the /yolov5 directory. YOLOv5 locates labels automatically for each image by replacing the last instance of /images/ in each image path with /labels/. For example:

../datasets/coco128/images/im0.jpg  # image
../datasets/coco128/labels/im0.txt  # label

Good luck 🍀 and let us know if you have any other questions!

[github-actions[bot]]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv3 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov3/wiki
• Tutorials – https://github.com/ultralytics/yolov3#tutorials
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
• About Us – https://ultralytics.com/about
• Join Our Team – https://ultralytics.com/work
• Contact Us – https://ultralytics.com/contact

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv3 🚀 and Vision AI ⭐!