Tensorflow Object Detection Library Packaged

pip install tensorflow-object-detection-api==0.1.1


TensorFlow Research Models

This folder contains machine learning models implemented by researchers in TensorFlow. The models are maintained by their respective authors. To propose a model for inclusion, please submit a pull request.


  • adversarial_crypto: protecting communications with adversarial neural cryptography.
  • adversarial_text: semi-supervised sequence learning with adversarial training.
  • attention_ocr: a model for real-world image text extraction.
  • audioset: Models and supporting code for use with AudioSet.
  • autoencoder: various autoencoders.
  • brain_coder: Program synthesis with reinforcement learning.
  • cognitive_mapping_and_planning: implementation of a spatial memory based mapping and planning architecture for visual navigation.
  • compression: compressing and decompressing images using a pre-trained Residual GRU network.
  • cvt_text: semi-supervised sequence learning with cross-view training.
  • deep_contextual_bandits: code for a variety of contextual bandits algorithms using deep neural networks and Thompson sampling.
  • deep_speech: automatic speech recognition.
  • deeplab: deep labeling for semantic image segmentation.
  • delf: deep local features for image matching and retrieval.
  • differential_privacy: differential privacy for training data.
  • domain_adaptation: domain separation networks.
  • fivo: filtering variational objectives for training generative sequence models.
  • gan: generative adversarial networks.
  • im2txt: image-to-text neural network for image captioning.
  • inception: deep convolutional networks for computer vision.
  • keypointnet: discovery of latent 3D keypoints via end-to-end geometric eeasoning [demo].
  • learning_to_remember_rare_events: a large-scale life-long memory module for use in deep learning.
  • learning_unsupervised_learning: a meta-learned unsupervised learning update rule.
  • lexnet_nc: a distributed model for noun compound relationship classification.
  • lfads: sequential variational autoencoder for analyzing neuroscience data.
  • lm_1b: language modeling on the one billion word benchmark.
  • lm_commonsense: commonsense reasoning using language models.
  • maskgan: text generation with GANs.
  • namignizer: recognize and generate names.
  • neural_gpu: highly parallel neural computer.
  • neural_programmer: neural network augmented with logic and mathematic operations.
  • next_frame_prediction: probabilistic future frame synthesis via cross convolutional networks.
  • object_detection: localizing and identifying multiple objects in a single image.
  • pcl_rl: code for several reinforcement learning algorithms, including Path Consistency Learning.
  • ptn: perspective transformer nets for 3D object reconstruction.
  • marco: automating the evaluation of crystallization experiments.
  • qa_kg: module networks for question answering on knowledge graphs.
  • real_nvp: density estimation using real-valued non-volume preserving (real NVP) transformations.
  • rebar: low-variance, unbiased gradient estimates for discrete latent variable models.
  • resnet: deep and wide residual networks.
  • seq2species: deep learning solution for read-level taxonomic classification.
  • skip_thoughts: recurrent neural network sentence-to-vector encoder.
  • slim: image classification models in TF-Slim.
  • street: identify the name of a street (in France) from an image using a Deep RNN.
  • struct2depth: unsupervised learning of depth and ego-motion.
  • swivel: the Swivel algorithm for generating word embeddings.
  • syntaxnet: neural models of natural language syntax.
  • tcn: Self-supervised representation learning from multi-view video.
  • textsum: sequence-to-sequence with attention model for text summarization.
  • transformer: spatial transformer network, which allows the spatial manipulation of data within the network.
  • vid2depth: learning depth and ego-motion unsupervised from raw monocular video.
  • video_prediction: predicting future video frames with neural advection.