DeepDetect (https://www.deepdetect.com/) is a machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications. It has support for both training and inference, with automatic conversion to embedded platforms with TensorRT (NVidia GPU) and NCNN (ARM CPU).
It implements support for supervised and unsupervised deep learning of images, text, time series and other data, with focus on simplicity and ease of use, test and connection into existing applications. It supports classification, object detection, segmentation, regression, autoencoders, ...
And it relies on external machine learning libraries through a very generic and flexible API. At the moment it has support for:
- the deep learning libraries Caffe, Tensorflow, Caffe2, Torch, NCNN Tensorrt and Dlib
- distributed gradient boosting library XGBoost
- clustering with T-SNE
- similarity search with Annoy and FAISS
Please join either the community on Gitter, where we help users get through with installation, API, neural nets and connection to external applications.
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- From docker
- From source
- From Amazon AMI: GPU and CPU
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Ecosystem
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Documentation:
- Introduction
- API Quickstart: setup an image classifier API service in a few minutes
- API Tutorials: training from text, data and images, setup of prediction services, and export to external software (e.g. ElasticSearch)
- API Reference
- Examples: MLP for data, text, multi-target regression to CNN and GoogleNet, finetuning, etc...)
- FAQ
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Demos:
- Image classification Web application using HTML and javascript
- Image similarity search using python client
- Image object detection using python client
- Image segmentation using python client
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Performance tools and report done on NVidia Desktop and embedded GPUs, along with Raspberry Pi 3.
- high-level API for machine learning and deep learning
- support for Caffe, Tensorflow, XGBoost, T-SNE, Caffe2, NCNN, TensorRT, Pytorch
- classification, regression, autoencoders, object detection, segmentation, time-series
- JSON communication format
- remote Python and Javacript clients
- dedicated server with support for asynchronous training calls
- high performances, benefit from multicore CPU and GPU
- built-in similarity search via neural embeddings
- connector to handle large collections of images with on-the-fly data augmentation (e.g. rotations, mirroring)
- connector to handle CSV files with preprocessing capabilities
- connector to handle text files, sentences, and character-based models
- connector to handle SVM file format for sparse data
- range of built-in model assessment measures (e.g. F1, multiclass log loss, ...)
- range of special losses (e.g Dice, contour, ...)
- no database dependency and sync, all information and model parameters organized and available from the filesystem
- flexible template output format to simplify connection to external applications
- templates for the most useful neural architectures (e.g. Googlenet, Alexnet, ResNet, convnet, character-based convnet, mlp, logistic regression, SSD, DeepLab, PSPNet, U-Net, CRNN, ShuffleNet, SqueezeNet, MobileNet, RefineDet, VOVNet, ...)
- support for sparse features and computations on both GPU and CPU
- built-in similarity indexing and search of predicted features, images, objects and probability distributions
| Caffe | Caffe2 | XGBoost | TensorRT | NCNN | Libtorch | Tensorflow | T-SNE | Dlib | |
|---|---|---|---|---|---|---|---|---|---|
| Serving | |||||||||
| Training (CPU) | Y | Y | Y | N/A | N/A | Y | N | Y | N |
| Training (GPU) | Y | Y | Y | N/A | N/A | Y | N | Y | N |
| Inference (CPU) | Y | Y | Y | N | Y | Y | Y | N/A | Y |
| Inference (GPU) | Y | Y | Y | Y | N | Y | Y | N/A | Y |
| Models | |||||||||
| Classification | Y | Y | Y | Y | Y | Y | Y | N/A | Y |
| Object Detection | Y | Y | N | Y | Y | N | N | N/A | Y |
| Segmentation | Y | N | N | N | N | N | N | N/A | N |
| Regression | Y | N | Y | N | N | Y | N | N/A | N |
| Autoencoder | Y | N | N/A | N | N | N | N | N/A | N |
| NLP | Y | N | Y | N | N | Y | N | Y | N |
| OCR / Seq2Seq | Y | N | N | N | Y | N | N | N | N |
| Time-Series | Y | N | N | N | Y | Y | N | N | N |
| Data | |||||||||
| CSV | Y | N | Y | N | N | N | N | Y | N |
| SVM | Y | N | Y | N | N | N | N | N | N |
| Text words | Y | N | Y | N | N | N | N | N | N |
| Text characters | Y | N | N | N | N | N | N | Y | N |
| Images | Y | Y | N | Y | Y | Y | Y | Y | Y |
| Time-Series | Y | N | N | N | Y | N | N | N | N |
- Python client:
- REST client: https://github.com/jolibrain/deepdetect/tree/master/clients/python
- 'a la scikit' bindings: https://github.com/ArdalanM/pyDD
- Javacript client: https://github.com/jolibrain/deepdetect-js
- Java client: https://github.com/kfadhel/deepdetect-api-java
- Early C# client: jolibrain#98
- Log DeepDetect training metrics via Tensorboard: https://github.com/jolibrain/dd_board
More models:
- List of free, even for commercial use, deep neural nets for image classification, and character-based convolutional nets for text classification: https://www.deepdetect.com/applications/list_models/
- DeepDetect (https://www.deepdetect.com/)
- Caffe (https://github.com/jolibrain/caffe)
- XGBoost (https://github.com/dmlc/xgboost)
- T-SNE (https://github.com/DmitryUlyanov/Multicore-TSNE)
DeepDetect is designed, implemented and supported by Jolibrain with the help of other contributors.
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