I have follow the tutorial : Collecting your own Detection Datasets.
May I know which Jetson platform and JetPack version you used?
I use a Jetson NX and the last version of JetPack (4.5.1)
Hi,
You can use pictures but please create the label file with the rule mentioned below:
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
<p align="right"><sup><a href="pytorch-plants.md">Back</a> | <a href="../README.md#hello-ai-world">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
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<sup>Transfer Learning - Object Detection</sup></s></p>
# Collecting your own Detection Datasets
The previously used `camera-capture` tool can also label object detection datasets from live video:
<img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/pytorch-collection-detect.jpg" >
When the `Dataset Type` drop-down is in Detection mode, the tool creates datasets in [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) format (which is supported during training).
> **note:** if you want to label a set of images that you already have (as opposed to capturing them from camera), try using a tool like [`CVAT`](https://github.com/openvinotoolkit/cvat) and export the dataset in Pascal VOC format. Then create a labels.txt in the dataset with the names of each of your object classes.
## Creating the Label File
Under `jetson-inference/python/training/detection/ssd/data`, create an empty directory for storing your dataset and a text file that will define the class labels (usually called `labels.txt`). The label file contains one class label per line, for example:
``` bash
show original
Thanks.
If you already have the pictures, you can use CVAT tool to annotate them. Then export them in Pascal VOC format and as Aasta mentioned, create a labels.txt file for it.