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https://github.com/jcjohnson/densecap
Dense image captioning in Torch
https://github.com/jcjohnson/densecap
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Dense image captioning in Torch
- Host: GitHub
- URL: https://github.com/jcjohnson/densecap
- Owner: jcjohnson
- License: mit
- Created: 2015-12-01T02:10:48.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2018-07-31T03:41:10.000Z (over 6 years ago)
- Last Synced: 2024-03-16T02:39:55.122Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 8.64 MB
- Stars: 1,558
- Watchers: 69
- Forks: 434
- Open Issues: 53
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# DenseCap
This is the code for the paper
**[DenseCap: Fully Convolutional Localization Networks for Dense Captioning](http://cs.stanford.edu/people/karpathy/densecap/)**,
[Justin Johnson](http://cs.stanford.edu/people/jcjohns/)\*,
[Andrej Karpathy](http://cs.stanford.edu/people/karpathy/)\*,
[Li Fei-Fei](http://vision.stanford.edu/feifeili/),
(\* equal contribution)
Presented at [CVPR 2016](http://cvpr2016.thecvf.com/) (oral)
The paper addresses the problem of **dense captioning**, where a computer detects objects in images and describes them in natural language. Here are a few example outputs:
The model is a deep convolutional neural network trained in an end-to-end fashion on the [Visual Genome](https://visualgenome.org/) dataset.
We provide:
- A [pretrained model](#pretrained-model)
- Code to [run the model on new images](#running-on-new-images), on either CPU or GPU
- Code to run a [live demo with a webcam](#webcam-demos)
- [Evaluation code](#evaluation) for dense captioning
- Instructions for [training the model](#training)
If you find this code useful in your research, please cite:
```
@inproceedings{densecap,
title={DenseCap: Fully Convolutional Localization Networks for Dense Captioning},
author={Johnson, Justin and Karpathy, Andrej and Fei-Fei, Li},
booktitle={Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition},
year={2016}
}
```
## Installation
DenseCap is implemented in [Torch](http://torch.ch/), and depends on the following packages: [torch/torch7](https://github.com/torch/torch7), [torch/nn](https://github.com/torch/nn), [torch/nngraph](https://github.com/torch/nngraph), [torch/image](https://github.com/torch/image), [lua-cjson](https://luarocks.org/modules/luarocks/lua-cjson), [qassemoquab/stnbhwd](https://github.com/qassemoquab/stnbhwd), [jcjohnson/torch-rnn](https://github.com/jcjohnson/torch-rnn)
After installing torch, you can install / update these dependencies by running the following:
```bash
luarocks install torch
luarocks install nn
luarocks install image
luarocks install lua-cjson
luarocks install https://raw.githubusercontent.com/qassemoquab/stnbhwd/master/stnbhwd-scm-1.rockspec
luarocks install https://raw.githubusercontent.com/jcjohnson/torch-rnn/master/torch-rnn-scm-1.rockspec
```
### (Optional) GPU acceleration
If have an NVIDIA GPU and want to accelerate the model with CUDA, you'll also need to install
[torch/cutorch](https://github.com/torch/cutorch) and [torch/cunn](https://github.com/torch/cunn);
you can install / update these by running:
```bash
luarocks install cutorch
luarocks install cunn
luarocks install cudnn
```
### (Optional) cuDNN
If you want to use NVIDIA's cuDNN library, you'll need to register for the CUDA Developer Program (it's free)
and download the library from [NVIDIA's website](https://developer.nvidia.com/cudnn); you'll also need to install
the [cuDNN bindings for Torch](https://github.com/soumith/cudnn.torch) by running
```bash
luarocks install cudnn
```
## Pretrained model
You can download a pretrained DenseCap model by running the following script:
```bash
sh scripts/download_pretrained_model.sh
```
This will download a zipped version of the model (about 1.1 GB) to `data/models/densecap/densecap-pretrained-vgg16.t7.zip`, unpack
it to `data/models/densecap/densecap-pretrained-vgg16.t7` (about 1.2 GB) and then delete the zipped version.
This is not the exact model that was used in the paper, but is has comparable performance; using 1000 region proposals per image,
it achieves a mAP of 5.70 on the test set which is slightly better than the mAP of 5.39 that we report in the paper.
## Running on new images
To run the model on new images, use the script `run_model.lua`. To run the pretrained model on the provided `elephant.jpg` image,
use the following command:
```bash
th run_model.lua -input_image imgs/elephant.jpg
```
By default this will run in GPU mode; to run in CPU only mode, simply add the flag `-gpu -1`.
This command will write results into the folder `vis/data`. We have provided a web-based visualizer to view these
results; to use it, change to the `vis` directory and start a local HTTP server:
```bash
cd vis
python -m SimpleHTTPServer 8181
```
Then point your web browser to [http://localhost:8181/view_results.html](http://localhost:8181/view_results.html).
If you have an entire directory of images on which you want to run the model, use the `-input_dir` flag instead:
```bash
th run_model.lua -input_dir /path/to/my/image/folder
```
This run the model on all files in the folder `/path/to/my/image/folder/` whose filename does not start with `.`.
The web-based visualizer is the prefered way to view results, but if you don't want to use it then you can instead
render an image with the detection boxes and captions "baked in"; add the flag `-output_dir` to specify a directory
where output images should be written:
```bash
th run_model.lua -input_dir /path/to/my/image/folder -output_dir /path/to/output/folder/
```
The `run_model.lua` script has several other flags; you can [find details here](doc/FLAGS.md#run_modellua).
## Training
To train a new DenseCap model, you will following the following steps:
1. Download the raw images and region descriptions from [the Visual Genome website](https://visualgenome.org/api/v0/api_home.html)
2. Use the script `preprocess.py` to generate a single HDF5 file containing the entire dataset
[(details here)](doc/FLAGS.md#preprocesspy)
3. Use the script `train.lua` to train the model [(details here)](doc/FLAGS.md#trainlua)
4. Use the script `evaluate_model.lua` to evaluate a trained model on the validation or test data
[(details here)](doc/FLAGS.md#evaluate_modellua)
For more instructions on training see [INSTALL.md](doc/INSTALL.md) in `doc` folder.
## Evaluation
In the paper we propose a metric for automatically evaluating dense captioning results.
Our metric depends on [METEOR](http://www.cs.cmu.edu/~alavie/METEOR/README.html), and
our evaluation code requires both Java and Python 2.7. The following script will download
and unpack the METEOR jarfile:
```bash
sh scripts/setup_eval.sh
```
The evaluation code is **not required** to simply run a trained model on images; you can
[find more details about the evaluation code here](eval/README.md).
## Webcam demos
If you have a powerful GPU, then the DenseCap model is fast enough to run in real-time. We provide two
demos to allow you to run DenseCap on frames from a webcam.
### Single-machine demo
If you have a single machine with both a webcam and a powerful GPU, then you can
use this demo to run DenseCap in real time at up to 10 frames per second. This demo depends on a few extra
Lua packages:
- [clementfarabet/lua---camera](https://github.com/clementfarabet/lua---camera)
- [torch/qtlua](https://github.com/torch/qtlua)
You can install / update these dependencies by running the following:
```bash
luarocks install camera
luarocks install qtlua
```
You can start the demo by running the following:
```bash
qlua webcam/single_machine_demo.lua
```
### Client / server demo
If you have a machine with a powerful GPU and another machine with a webcam, then
this demo allows you use the GPU machine as a server and the webcam machine as a client; frames will be
streamed from the client to to the server, the model will run on the server, and predictions will be shipped
back to the client for viewing. This allows you to run DenseCap on a laptop, but with network and filesystem
overhead you will typically only achieve 1 to 2 frames per second.
The server is written in Flask; on the server machine run the following to install dependencies:
```bash
cd webcam
virtualenv .env
source .env/bin/activate
pip install -r requirements.txt
cd ..
```
For technical reasons, the server needs to serve content over SSL; it expects to find SSL key
files and certificate files in `webcam/ssl/server.key` and `webcam/ssl/server.crt` respectively.
You can generate a self-signed SSL certificate by running the following:
```bash
mkdir webcam/ssl
# Step 1: Generate a private key
openssl genrsa -des3 -out webcam/ssl/server.key 1024
# Enter a password
# Step 2: Generate a certificate signing request
openssl req -new -key webcam/ssl/server.key -out webcam/ssl/server.csr
# Enter the password from above and leave all other fields blank
# Step 3: Strip the password from the keyfile
cp webcam/ssl/server.key webcam/ssl/server.key.org
openssl rsa -in webcam/ssl/server.key.org -out webcam/ssl/server.key
# Step 4: Generate self-signed certificate
openssl x509 -req -days 365 -in webcam/ssl/server.csr -signkey webcam/ssl/server.key -out webcam/ssl/server.crt
# Enter the password from above
```
You can now run the following two commands to start the server; both will run forever:
```bash
th webcam/daemon.lua
python webcam/server.py
```
On the client, point a web browser at the following page:
```
https://cs.stanford.edu/people/jcjohns/densecap/demo/web-client.html?server_url=SERVER_URL
```
but you should replace SERVER_URL with the actual URL of the server.
**Note**: If the server is using a self-signed SSL certificate, you may need to manually
tell your browser that the certificate is safe by pointing your client's web browser directly
at the server URL; you will get a message that the site is unsafe; for example on Chrome
you will see the following:
Afterward you should see a message telling you that the DenseCap server is running, and
the web client should work after refreshing.