RuntimeError: Cannot find callable custom_yolov5s in hubconf

[I-am-vishalmaurya]

RuntimeError: Cannot find callable custom_yolov5s in hubconf

Originally posted by @I-am-vishalmaurya in https://github.com/ultralytics/yolov5/issues/1787#issuecomment-968971941

[glenn-jocher]

@I-am-vishalmaurya your command is incorrect so an error is the expected result. See PyTorch Hub tutorial to get started using YOLOv5 PyTorch Hub models:

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[github-actions[bot]]

👋 Hello @I-am-vishalmaurya, thank you for your interest in YOLOv5 🚀! 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.

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Requirements

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

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

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

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[I-am-vishalmaurya]

import torch

Model

model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom this is the code for loading the custom file right?

[glenn-jocher]

@I-am-vishalmaurya yes

[I-am-vishalmaurya]

After reading some documentation I have reached here.

[I-am-vishalmaurya]

and to clone the repository I have used this

clone YOLOv5 repository

!git clone https://github.com/ultralytics/yolov5 # clone repo %cd yolov5 !git reset --hard 886f1c03d839575afecb059accf74296fad395b6

[I-am-vishalmaurya]

Okay I have resolved this issue

[sharmas1ddharth]

Okay I have resolved this issue

How did you do this can you please share with me.

[glenn-jocher]

@sharmas1ddharth 👋 Hello! Thanks for asking about handling inference results. YOLOv5 🚀 PyTorch Hub models allow for simple model loading and inference in a pure python environment without using detect.py.

Simple Inference Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes an image for inference. 'yolov5s' is the YOLOv5 'small' model. For details on all available models please see the README. Custom models can also be loaded, including custom trained PyTorch models and their exported variants, i.e. ONNX, TensorRT, TensorFlow, OpenVINO YOLOv5 models.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, etc.
# model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt')  # custom trained model

# Images
im = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, URL, PIL, OpenCV, numpy, list

# Inference
results = model(im)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

results.xyxy[0]  # im predictions (tensor)
results.pandas().xyxy[0]  # im predictions (pandas)
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

See YOLOv5 PyTorch Hub Tutorial for details.

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

[sharmas1ddharth]

@sharmas1ddharth 👋 Hello! Thanks for asking about handling inference results. YOLOv5 🚀 PyTorch Hub models allow for simple model loading and inference in a pure python environment without using detect.py.

Simple Inference Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes an image for inference. 'yolov5s' is the YOLOv5 'small' model. For details on all available models please see the README. Custom models can also be loaded, including custom trained PyTorch models and their exported variants, i.e. ONNX, TensorRT, TensorFlow, OpenVINO YOLOv5 models.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, etc.
# model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt')  # custom trained model

# Images
im = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, URL, PIL, OpenCV, numpy, list

# Inference
results = model(im)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

results.xyxy[0]  # im predictions (tensor)
results.pandas().xyxy[0]  # im predictions (pandas)
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

See YOLOv5 PyTorch Hub Tutorial for details.

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

Thank you so much @glenn-jocher it's working

[RAJ-DSML]

@glenn-jocher 👋 Hi, I'm trying to perform this same thing as @I-am-vishalmaurya

but I got this error: raise RuntimeError(f'Cannot find callable {model} in hubconf') RuntimeError: Cannot find callable yolov3 in hubconf

Are these steps essential for the code to work?

$ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ pip install -r requirements.txt

[glenn-jocher]

@RAJ-DSML hey there! It looks like you're trying to load a model that isn't directly available via the YOLOv5 PyTorch Hub. The error you're seeing is because yolov3 isn't a model option in the YOLOv5 repository. You'll want to use one of the available YOLOv5 models (yolov5s, yolov5m, yolov5l, yolov5x) or a custom model if you have trained one.

Here's a quick example of how to load a YOLOv5 model:

import torch

# Load YOLOv5s model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

And yes, cloning the repository and installing requirements are essential steps to ensure all dependencies are correctly set up for running YOLOv5 models. Make sure you have the latest version of the repository and have installed all necessary packages from requirements.txt. 😊