question-mark
Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

Model output for "Train detectors on your own"

See original GitHub issue

It seems that for training detectors on your own, the model output weights aren’t customizable as well as the prediction instances json file. The model output at ./output/model_final.pth don’t match the pretrained models at eg. ./pretrained_ckpt/regionclip/regionclip_pretrained-cc_rn50.pth. Is this intended, if not how to load the finetuned model? Thanks!

Issue Analytics

  • State:closed
  • Created a year ago
  • Comments:5

github_iconTop GitHub Comments

1reaction
YiwuZhongcommented, Jul 11, 2022

Hi @Jawing, thanks for your interests in our work.

The short answer is that you compared the wrong model file (pretrained vs. finetuned). As mention in Model Zoo, we provided trained model weights for both training stages: (1) after pretraining, the trained model is regionclip_pretrained*, (2) after finetuning, the trained model is regionclip_finetuned* (this corresponds to Train detectors on your own and ./output/model_final.pth you mentioned).

0reactions
YiwuZhongcommented, Jul 12, 2022

This is expected. All differences come from offline RPN. As described in Model Zoo, different versions of offline RPN were used in different settings and datasets. Pretrained model regionclip_pretrained-cc_rn50.pth corresponds to rpn_lvis_866, COCO finetuning uses rpn_coco_48 and LVIS finetuning uses rpn_lvis_866_lsj.

Read more comments on GitHub >

github_iconTop Results From Across the Web

How to Train Your Own Object Detector Using TensorFlow ...
We'll be using the EfficientDet based model as an example, but you will also learn how to use any architecture of your choice...
Read more >
How to Train A Custom Object Detection Model with YOLO v5
Define YOLO v5 Model Configuration and Architecture; Train a custom YOLO v5 Detector; Evaluate YOLO v5 performance; Run YOLO v5 Inference on test...
Read more >
How to Train an Object Detection Model with Keras
The Matterport Mask R-CNN project provides a library that allows you to develop and train Mask R-CNN Keras models for your own object...
Read more >
Model Railway Sensors & Detection Part 1 - YouTube
A step by step guide to adding sensors to your layout using an Arduino and JMRI. Adding sensors is the first step in...
Read more >
Custom Object Detection: Training and Inference - ImageAI
You can train your custom detection model completely from scratch or use ... of your saved models in order to pick the one...
Read more >

github_iconTop Related Medium Post

No results found

github_iconTop Related StackOverflow Question

No results found

github_iconTroubleshoot Live Code

Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free

github_iconTop Related Reddit Thread

No results found

github_iconTop Related Hackernoon Post

No results found

github_iconTop Related Tweet

No results found

github_iconTop Related Dev.to Post

No results found

github_iconTop Related Hashnode Post

No results found