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TensorFlow models accelerated with NVIDIA TensorRT
| Date | Stars |
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| 2026-07-25 | 693 |
| 2026-07-28 | 693 |
| 2026-07-30 | 693 |
| 2026-08-06 | 693 |
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TensorFlow/TensorRT Models on Jetson
====================================
<p align="center">
<img src="data/landing_graphic.jpg" alt="landing graphic" height="300px"/>
</p>
This repository contains scripts and documentation to use TensorFlow image classification and object detection models on NVIDIA Jetson. The models are sourced from the [TensorFlow models repository](https://github.com/tensorflow/models)
and optimized using TensorRT.
* [Setup](#setup)
* [Image Classification](#ic)
* [Models](#ic_models)
* [Download pretrained model](#ic_download)
* [Build TensorRT / Jetson compatible graph](#ic_build)
* [Optimize with TensorRT](#ic_trt)
* [Jupyter Notebook Sample](#ic_notebook)
* [Train for custom task](#ic_train)
* [Object Detection](#od)
* [Models](#od_models)
* [Download pretrained model](#od_download)
* [Build TensorRT / Jetson compatible graph](#od_build)
* [Optimize with TensorRT](#od_trt)
* [Jupyter Notebook Sample](#od_notebook)
* [Train for custom task](#od_train)
<a name="setup"></a>
Setup
-----
1. Flash your Jetson TX2 with JetPack 3.2 (including TensorRT).
2. Install miscellaneous dependencies on Jetson
```
sudo apt-get install python-pip python-matplotlib python-pil
```
3. Install TensorFlow 1.7+ (with TensorRT support). Download the [pre-built pip wheel](https://devtalk.nvidia.com/default/topic/1031300/jetson-tx2/tensorflow-1-8-wheel-with-jetpack-3-2-/) and install using pip.
```
pip install tensorflow-1.8.0-cp27-cp27mu-linux_aarch64.whl --user
```
or if you're using Python 3.
```
pip3 install tensorflow-1.8.0-cp35-cp35m-linux_aarch64.whl --user
```
4. Clone this repository
```
git clone --recursive https://github.com/NVIDIA-Jetson/tf_trt_models.git
cd tf_trt_models
```
5. Run the installation script
```
./install.sh
```
or if you want to specify python intepreter
```
./install.sh python3
```
<a name="ic"></a>
Image Classification
--------------------
<img src="data/classification_graphic.jpg" alt="classification" height="300px"/>
<a name="ic_models"></a>
### Models
| Model | Input Size | TF-TRT TX2 | TF TX2 |
|:------|:----------:|-----------:|-------:|
| inception_v1 | 224x224 | 7.36ms | 22.9ms |
| inception_v2 | 224x224 | 9.08ms | 31.8ms |
| inception_v3 | 299x299 | 20.7ms | 74.3ms |
| inception_v4 | 299x299 | 38.5ms | 129ms |
| inception_resnet_v2 | 299x299 | | 158ms |
| resnet_v1_50 | 224x224 | 12.5ms | 55.1ms |
| resnet_v1_101 | 224x224 | 20.6ms | 91.0ms |
| resnet_v1_152 | 224x224 | 28.9ms | 124ms |
| resnet_v2_50 | 299x299 | 26.5ms | 73.4ms |
| resnet_v2_101 | 299x299 | 46.9ms | |
| resnet_v2_152 | 299x299 | 69.0ms | |
| mobilenet_v1_0p25_128 | 128x128 | 3.72ms | 7.99ms |
| mobilenet_v1_0p5_160 | 160x160 | 4.47ms | 8.69ms |
| mobilenet_v1_1p0_224 | 224x224 | 11.1ms | 17.3ms |
**TF** - Original TensorFlow graph (FP32)
**TF-TRT** - TensorRT optimized graph (FP16)
The above benchmark timings were gathered after placing the Jetson TX2 in MAX-N
mode. To do this, run the following commands in a terminal:
```
sudo nvpmodel -m 0
sudo ~/jetson_clocks.sh
```
<a name="ic_download"></a>
### Download pretrained model
As a convenience, we provide a script to download pretrained models sourced from the
TensorFlow models repository.
```python
from tf_trt_models.classification import download_classification_checkpoint
checkpoint_path = download_classification_checkpoint('inception_v2')
```
To manually download the pretrained models, follow the links [here](https://github.com/tensorflow/models/tree/master/research/slim#Pretrained).
<a name="ic_build"></a>
### Build TensorRT / Jetson compatible graph
```python
from tf_trt_models.classification import build_classification_graph
frozen_graph, input_names, output_names = build_classification_graph(
model='inception_v2',
checkpoint=checkpoint_path,
num_classes=1001
)
```
### Optimize with Excerpt of 8,097 characters
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John · NVIDIA
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:abb4fa25bc7ac5a2, topic:object-detection, topic:image-classification, readme:object detection
matched fp:abb4fa25bc7ac5a2, topic:inference, topic:tensorrt
matched fp:abb4fa25bc7ac5a2, topic:neural-network, topic:tensorflow