how can i add a new objects to ssd-mobilenet v2. When install jetson-inference, there are 91 objects, how can i add a new objects,
Hi,
To add an new object to the model, please re-train the ssd-mobilenet-v2 with the new database.
<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-transfer-learning.md">Back</a> | <a href="pytorch-plants.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
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<sup>Transfer Learning - Classification</sup></s></p>
# Re-training on the Cat/Dog Dataset
The first model that we'll be re-training is a simple model that recognizes two classes: cat or dog.
<img src="https://github.com/dusty-nv/jetson-inference/raw/python/docs/images/pytorch-cat-dog.jpg" width="700">
Provided below is an 800MB dataset that includes 5000 training images, 1000 validation images, and 200 test images, each evenly split between the cat and dog classes. The set of training images is used for transfer learning, while the validation set is used to evaluate classification accuracy during training, and the test images are to be used by us after training completes. The network is never directly trained on the validation and test sets, only the training set.
The images from the dataset are made up of many different breeds of dogs and cats, including large felines like tigers and mountain lions since the amount of cat images available was a bit lower than dogs. Some of the images also picture humans, which the detector is essentially trained to ignore as background and focus on the cat vs. dog content.
To get started, first make sure that you have [PyTorch installed](pytorch-transfer-learning.md#installing-pytorch) on your Jetson, then download the dataset below and kick off the training script. After that, we'll test the re-trained model in TensorRT on some static images and a live camera feed.
## Downloading the Data
During this tutorial, we'll store the datasets on the host device under `jetson-inference/python/training/classification/data`, which is one of the directories that is automatically [mounted into the container](aux-docker.md#mounted-data-volumes). This way the dataset won't be lost when you shutdown the container.
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Thanks.
when i want to run detectnet.py or my-detection.py or etc.in jetson-inference/build/aarch64/bin directory, there are 91 object to recognize for ssd-mobilenet-v2.
My question is how could i add a new object to know that found in jetson-inference/build/aarch64/bin directory?
Hi,
What kind of the new object do you want to add.
Thanks.
buoy, barge, canoe, paddle, jetski.
from here
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
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<br/>
<sup>Transfer Learning - Object Detection</sup></s></p>
# Re-training SSD-Mobilenet
Next, we'll train our own SSD-Mobilenet object detection model using PyTorch and the [Open Images](https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=detection&c=%2Fm%2F06l9r) dataset. SSD-Mobilenet is a popular network architecture for realtime object detection on mobile and embedded devices that combines the [SSD-300](https://arxiv.org/abs/1512.02325) Single-Shot MultiBox Detector with a [Mobilenet](https://arxiv.org/abs/1704.04861) backbone.
<a href="https://arxiv.org/abs/1512.02325"><img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/pytorch-ssd-mobilenet.jpg"></a>
In the example below, we'll train a custom detection model that locates 8 different varieties of fruit, although you are welcome to pick from any of the [600 classes](https://github.com/dusty-nv/pytorch-ssd/blob/master/open_images_classes.txt) in the Open Images dataset to train your model on. You can visually browse the dataset [here](https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=detection).
<img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/pytorch-fruit.jpg">
To get started, first make sure that you have [JetPack 4.4](https://developer.nvidia.com/embedded/jetpack) or newer and [PyTorch installed](pytorch-transfer-learning.md#installing-pytorch) for **Python 3.6** on your Jetson. JetPack 4.4 includes TensorRT 7.1, which is the minimum TensorRT version that supports loading SSD-Mobilenet via ONNX. And the PyTorch training scripts used for training SSD-Mobilenet are for Python3, so PyTorch should be installed for Python 3.6.
## Setup
> **note:** first make sure that you have [JetPack 4.4](https://developer.nvidia.com/embedded/jetpack) or newer on your Jetson and [PyTorch installed](pytorch-transfer-learning.md#installing-pytorch) for **Python 3.6**
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savunmasad:
buoy, barge, canoe, paddle, jetski.
I could not find ‘buoy’ in the Open Images dataset, so for that buoy class you may have to use a similar class, or find the data elsewhere (or collect your own).
However the other classes are in Open Images, so you could download them like so:
$ python3 open_images_downloader.py --class-names "barge,canoe,paddle,jetski" --data=data/my_dataset
Then you would proceed with the training as shown in the GitHub page.
Note that this would make a 4-class model, not added to the 91-class MS COCO model.