Custom Object Detection Training

[nisarggandhewar]

❓ How to do something using DETR

Describe what you want to do, including:

1. what inputs you will provide, if any:
2. what outputs you are expecting:

NOTE:

1. Only general answers are provided.
   If you want to ask about "why X did not work", please use the
   Unexpected behaviors issue template.

2. About how to implement new models / new dataloader / new training logic, etc., check documentation first.

3. We do not answer general machine learning / computer vision questions that are not specific to DETR, such as how a model works, how to improve your training/make it converge, or what algorithm/methods can be used to achieve X.

How to train a new model for Custom object detection in google colab.

[fmassa]

Hi,

We provide training scripts for usage with the command line, see https://github.com/facebookresearch/detr#training

For more information on how to train on your own dataset, check #9 and #28

As such, I'm closing this issue as a duplicate of #9

[Dicko87]

Hi there,
I have been using DETR on my own dataset and it works very well. I get a good mAP and Recall on the validation set. My question is, how to I run cocoEval to give me the same or similar results to what it got during model training. For example the model achieved and mAP of 0.89 on the validation set. I then decided to see if I could produce the same results again. I ran the model in eval mode on the dataset and set a confidence threshold > 0.8 and saved the results in a json file. I then used cocoeval and gave the validation set json and my new resFile as inputs and the evaluation results gave me an mAP of 0.6, which isn’t right. How do I go about getting the same or similar results as to what the model achieved originally and consequently how do I adjust the confidence threshold and get the precision-recall curves for these different thresholds. I should have said that I did try the cocoeval with all predictions (no filter on the confidences) and my mAP result was still a lot lower than what the model showed me. I guess my real question is, what code / steps should I take so that I can get the same results as what the model gave on the validation set? How to I replicate these figures ? What do I need to do to achieve this?
So far I ran the model in eval mode, ran predictions on my dataset and saved them into a json file. I then ran the code as shown in the attached image, but the results I got were no where near the same as what the model gave.
thank you.