Hi, I have successfully converted a darknet weights to params for mxnet but I have a problem of a missing json file? Do you guys have any idea how I could generate that file? I have used this method for converting the weights to params: https://github.com/bowenc0221/mxnet-yolo/tree/master/darknet2mxnet. Thanks guys.

# .json file is missing after converting darknet weights to mxnet params

It appears what you are linking does not contain yolo symbols: https://github.com/bowenc0221/mxnet-yolo/tree/master/Symbols it’s empty.

However good news, if you want to use Darknet for image classification you can use gluon-cv, and if you want yolo with Darknet it’s also available in gluon-cv.

https://gluon-cv.mxnet.io/model_zoo/detection.html

Have a look at the following tutorial:

https://gluon-cv.mxnet.io/build/examples_detection/demo_yolo.html

Hi, thanks for the response. However do you have a link for a none pretrained model as I want to train a YOLO weights from scratch?. What I did with my previous post is that I already have the converted weights that became params to be use with mxnet. Yesterday I was looking for this python code:

```
from gluoncv import model_zoo
import mxnet as mx
import numpy as np
# Download the model from the Gluon model zoo
# You'll find it in ~/.mxnet/models
net = model_zoo.get_model('my_yolov3', pretrained=True)
# Convert the model to symbolic format
net.hybridize()
# Build a fake image to run a single prediction
# This is required to initialize the model properly
x = np.zeros([1,3,224,244])
x = mx.nd.array(x)
# Predict the fake image
net.forward(x)
# Export the model
net.export('my_yolov3')
```

That block of code downloads a pretrained model that stores in **~/.mxnet/models/**. Tried it and all I can see there is a model extended as *.params*. Now I looked deeper into the **model_zoo.py** script and since I have the converted param weights, all I needed is to load that weights locally. I wanted to ask if that is possible without having to download a pretrained weights from the internet but instead locally? Thanks…