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Add Weights & Biases Logging support #203
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Sorry, I want to know if there is the possibility to save the artifacts during the training, in order to resume the training from the last weights stored in wandb.ai. I'm working on Colab, so this is feature is really important to save the weights and don't lose the work. |
@antimo22 hi. This version doesn't include the artifacts integration. But I can help you with that. Can't you just use yolov5 repo to train a yolov4 model? |
Hi @AyushExel, and thanks for your help. I tried to use a YOLOv4 model with the ultralytics implementation, but there are some differences in terms of formats (cfg vs yaml for the config files, and pt vs weights for the weights). Can I just use some consistent files, contained in other repo? Furthermore, I can't find the pt version of the weights file, there is some utility to convert it? Sorry if I'm clogging up this pull request with my problems. |
@antimo22 Sorry, I'm not sure if you can use .pt and .weights files interchangeably. Can't you just use a YOLOv5 model for training on your task? Or are you required to use yolov4 for some reason? |
Hello, the current version of wanddb integration is newer than this commit. But due to some annoying reasons, I think it is not suitable to update those functions in my repos. If you want to use new wandb functions in yolov5, you could integrate them with this repo by using loggers in yolov5 repo in 10 minutes. |
@WongKinYiu If you're interested, I can build the same integration for this repo. |
Yeah I have already used YOLOv5, but now I want to compare the perfomance with other small models. The problem is that, working with Colab, I can't work without the wandb artifacts. |
@antimo22 I see. Let me know if there's any way I can help you. |
I'll try to integrate the new features of wandb in this repo, even though I don't have experience in this stuff. Thank you for your time! |
This PR adds support for debugging models using W&B, only if the library is already found installed. When using W&B, users of YOLOV5 can debug their models easily inside a customizable dashboard by logging & comparing performance metrics, system usage metrics (like GPU memory), and predictions.
Features:
Bounding Box Debugging
Debug your bounding box predictions in real-time.
![4d482f8f.gif](https://camo.githubusercontent.com/aa111d26ddc4da57be30f38245f937a8dca301067293ee36914be8ace0f1cc70/68747470733a2f2f6170692e77616e64622e61692f66696c65732f6361797573682f696d616765732f70726f6a656374732f3132343131312f35643064313361362e676966)
Automatically log and compare the performance of multiple models
Supports Resuming
When training is resumed from a previous checkpoint, the metrics and images will continue to be logged in the same W&B dashboard if it exists, otherwise, a new W&B run will be created
Adds no dependencies
The library will work as it is supposed to if
wandb
is not installed and will only log metrics and media files to W&B if it is installed. To enable W&B logging, you just need to install the library usingpip install wandb
Adds coco128.yaml
By default, the train.py script uses coco128.yaml for training but that file was missing from the data folder.