This is a tensorflow-based rotation detection benchmark, also called AlphaRotate.

Overview

AlphaRotate: A Rotation Detection Benchmark using TensorFlow

Documentation Status PyPI Downloads License

Abstract

AlphaRotate is maintained by Xue Yang with Shanghai Jiao Tong University supervised by Prof. Junchi Yan.

Papers and codes related to remote sensing/aerial image detection: DOTA-DOAI .

Techniques:

The above-mentioned rotation detectors are all modified based on the following horizontal detectors:

3

Projects

0

Latest Performance

DOTA (Task1)

Baseline

Backbone Neck Training/test dataset Data Augmentation Epoch NMS
ResNet50_v1d 600->800 FPN trainval/test × 13 (AP50) or 17 (AP50:95) is enough for baseline (default is 13) gpu nms (slightly worse <1% than cpu nms but faster)
Method Baseline DOTA1.0 DOTA1.5 DOTA2.0 Model Anchor Angle Pred. Reg. Loss Angle Range Configs
- RetinaNet-R 67.25 56.50 42.04 Baidu Drive (bi8b) R Reg. (∆⍬) smooth L1 [-90,0) dota1.0, dota1.5, dota2.0
- RetinaNet-H 64.17 56.10 43.06 Baidu Drive (bi8b) H Reg. (∆⍬) smooth L1 [-90,90) dota1.0, dota1.5, dota2.0
- RetinaNet-H 65.33 57.21 44.58 Baidu Drive (bi8b) H Reg. (sin⍬, cos⍬) smooth L1 [-90,90) dota1.0, dota1.5, dota2.0
- RetinaNet-H 65.73 58.87 44.16 Baidu Drive (bi8b) H Reg. (∆⍬) smooth L1 [-90,0) dota1.0, dota1.5, dota2.0
IoU-Smooth L1 RetinaNet-H 66.99 59.17 46.31 Baidu Drive (qcvc) H Reg. (∆⍬) iou-smooth L1 [-90,0) dota1.0, dota1.5, dota2.0
RIDet RetinaNet-H 66.06 58.91 45.35 Baidu Drive (njjv) H Quad. hungarian loss - dota1.0, dota1.5, dota2.0
RSDet RetinaNet-H 67.27 61.42 46.71 Baidu Drive (2a1f) H Quad. modulated loss - dota1.0, dota1.5, dota2.0
CSL RetinaNet-H 67.38 58.55 43.34 Baidu Drive (sdbb) H Cls.: Gaussian (r=1, w=10) smooth L1 [-90,90) dota1.0, dota1.5, dota2.0
DCL RetinaNet-H 67.39 59.38 45.46 Baidu Drive (m7pq) H Cls.: BCL (w=180/256) smooth L1 [-90,90) dota1.0, dota1.5, dota2.0
- FCOS 67.69 61.05 48.10 Baidu Drive (pic4) - Quad smooth L1 - dota1.0, dota1.5, dota2.0
RSDet++ FCOS 67.91 62.18 48.81 Baidu Drive (8ww5) - Quad modulated loss - dota1.0, dota1.5 dota2.0
GWD RetinaNet-H 68.93 60.03 46.65 Baidu Drive (7g5a) H Reg. (∆⍬) gwd [-90,0) dota1.0, dota1.5, dota2.0
GWD + SWA RetinaNet-H 69.92 60.60 47.63 Baidu Drive (qcn0) H Reg. (∆⍬) gwd [-90,0) dota1.0, dota1.5, dota2.0
BCD RetinaNet-H 71.23 60.78 47.48 Baidu Drive (0puk) H Reg. (∆⍬) bcd [-90,0) dota1.0, dota1.5, dota2.0
KLD RetinaNet-H 71.28 62.50 47.69 Baidu Drive (o6rv) H Reg. (∆⍬) kld [-90,0) dota1.0, dota1.5, dota2.0
KFIoU RetinaNet-H 70.64 62.71 48.04 Baidu Drive (o72o) H Reg. (∆⍬) kf [-90,0) dota1.0, dota1.5, dota2.0
R3Det RetinaNet-H 70.66 62.91 48.43 Baidu Drive (n9mv) H->R Reg. (∆⍬) smooth L1 [-90,0) dota1.0, dota1.5, dota2.0
DCL R3Det 71.21 61.98 48.71 Baidu Drive (eg2s) H->R Cls.: BCL (w=180/256) iou-smooth L1 [-90,0)->[-90,90) dota1.0, dota1.5, dota2.0
GWD R3Det 71.56 63.22 49.25 Baidu Drive (jb6e) H->R Reg. (∆⍬) smooth L1->gwd [-90,0) dota1.0, dota1.5, dota2.0
BCD R3Det 72.22 63.53 49.71 Baidu Drive (v60g) H->R Reg. (∆⍬) bcd [-90,0) dota1.0, dota1.5, dota2.0
KLD R3Det 71.73 65.18 50.90 Baidu Drive (tq7f) H->R Reg. (∆⍬) kld [-90,0) dota1.0, dota1.5, dota2.0
KFIoU R3Det 72.28 64.69 50.41 Baidu Drive (u77v) H->R Reg. (∆⍬) kf [-90,0) dota1.0, dota1.5, dota2.0
- R2CNN (Faster-RCNN) 72.27 66.45 52.35 Baidu Drive (02s5) H->R Reg. (∆⍬) smooth L1 [-90,0) dota1.0, dota1.5 dota2.0

SOTA

Method Backbone DOTA1.0 Model MS Data Augmentation Epoch Configs
R2CNN-BCD ResNet152_v1d-FPN 79.54 Baidu Drive (h2u1) 34 dota1.0
RetinaNet-BCD ResNet152_v1d-FPN 78.52 Baidu Drive (0puk) 51 dota1.0
R3Det-BCD ResNet50_v1d-FPN 79.08 Baidu Drive (v60g) 51 dota1.0
R3Det-BCD ResNet152_v1d-FPN 79.95 Baidu Drive (v60g) 51 dota1.0

Note:

  • Single GPU training: SAVE_WEIGHTS_INTE = iter_epoch * 1 (DOTA1.0: iter_epoch=27000, DOTA1.5: iter_epoch=32000, DOTA2.0: iter_epoch=40000)
  • Multi-GPU training (better): SAVE_WEIGHTS_INTE = iter_epoch * 2

Installation

Manual configuration

pip install -r requirements.txt
pip install -v -e .  # or "python setup.py develop"

Or, you can simply install AlphaRotate with the following command:

pip install alpharotate

Docker

docker images: yangxue2docker/yx-tf-det:tensorflow1.13.1-cuda10-gpu-py3

Note: For 30xx series graphics cards, I recommend this blog to install tf1.xx, or download image from tensorflow-release-notes according to your development environment, e.g. nvcr.io/nvidia/tensorflow:20.11-tf1-py3

Download Model

Pretrain weights

Download a pretrain weight you need from the following three options, and then put it to $PATH_ROOT/dataloader/pretrained_weights.

  1. MxNet pretrain weights (recommend in this repo, default in NET_NAME): resnet_v1d, resnet_v1b, refer to gluon2TF.
  1. Tensorflow pretrain weights: resnet50_v1, resnet101_v1, resnet152_v1, efficientnet, mobilenet_v2, darknet53 (Baidu Drive (1jg2), Google Drive).
  2. Pytorch pretrain weights, refer to pretrain_zoo.py and Others.

Trained weights

  1. Please download trained models by this project, then put them to $PATH_ROOT/output/pretained_weights.

Train

  1. If you want to train your own dataset, please note:

    (1) Select the detector and dataset you want to use, and mark them as #DETECTOR and #DATASET (such as #DETECTOR=retinanet and #DATASET=DOTA)
    (2) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py
    (3) Copy $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py to $PATH_ROOT/libs/configs/cfgs.py
    (4) Add category information in $PATH_ROOT/libs/label_name_dict/label_dict.py     
    (5) Add data_name to $PATH_ROOT/dataloader/dataset/read_tfrecord.py  
    
  2. Make tfrecord
    If image is very large (such as DOTA dataset), the image needs to be cropped. Take DOTA dataset as a example:

    cd $PATH_ROOT/dataloader/dataset/DOTA
    python data_crop.py
    

    If image does not need to be cropped, just convert the annotation file into xml format, refer to example.xml.

    cd $PATH_ROOT/dataloader/dataset/  
    python convert_data_to_tfrecord.py --root_dir='/PATH/TO/DOTA/' 
                                       --xml_dir='labeltxt'
                                       --image_dir='images'
                                       --save_name='train' 
                                       --img_format='.png' 
                                       --dataset='DOTA'
    
  3. Start training

    cd $PATH_ROOT/tools/#DETECTOR
    python train.py
    

Test

  1. For large-scale image, take DOTA dataset as a example (the output file or visualization is in $PATH_ROOT/tools/#DETECTOR/test_dota/VERSION):

    cd $PATH_ROOT/tools/#DETECTOR
    python test_dota.py --test_dir='/PATH/TO/IMAGES/'  
                        --gpus=0,1,2,3,4,5,6,7  
                        -ms (multi-scale testing, optional)
                        -s (visualization, optional)
                        -cn (use cpu nms, slightly better <1% than gpu nms but slower, optional)
    
    or (recommend in this repo, better than multi-scale testing)
    
    python test_dota_sota.py --test_dir='/PATH/TO/IMAGES/'  
                             --gpus=0,1,2,3,4,5,6,7  
                             -s (visualization, optional)
                             -cn (use cpu nms, slightly better <1% than gpu nms but slower, optional)
    

    Notice: In order to set the breakpoint conveniently, the read and write mode of the file is' a+'. If the model of the same #VERSION needs to be tested again, the original test results need to be deleted.

  2. For small-scale image, take HRSC2016 dataset as a example:

    cd $PATH_ROOT/tools/#DETECTOR
    python test_hrsc2016.py --test_dir='/PATH/TO/IMAGES/'  
                            --gpu=0
                            --image_ext='bmp'
                            --test_annotation_path='/PATH/TO/ANNOTATIONS'
                            -s (visualization, optional)
    

Tensorboard

cd $PATH_ROOT/output/summary
tensorboard --logdir=.

1

2

Citation

If you find our code useful for your research, please consider cite.

@article{yang2021alpharotate,
    author  = {Yang, Xue and Zhou, Yue and Yan, Junchi},
    title   = {AlphaRotate: A Rotation Detection Benchmark using TensorFlow},
    year    = {2021},
    url     = {https://github.com/yangxue0827/RotationDetection}
}

Reference

1、https://github.com/endernewton/tf-faster-rcnn
2、https://github.com/zengarden/light_head_rcnn
3、https://github.com/tensorflow/models/tree/master/research/object_detection
4、https://github.com/fizyr/keras-retinanet

Comments
  • "ValueError: too many values to unpack" RotationDetection/alpharotate/libs/models/detectors/scrdet/build_whole_network.py 147Line

    Problem path: RotationDetection/alpharotate/libs/models/detectors/scrdet/build_whole_network.py

    147 Line. feature, pa_mask = self.build_backbone(input_img_batch)

    Since only one variable is returned, why store it in two variables?

    It is error that "ValueError: too many values to unpack"

    what is pa_mask parameter's mean and rule?

    opened by sangheonEN 38
  • Gradient about IOU-Smooth L1 loss in SCRDet

    Gradient about IOU-Smooth L1 loss in SCRDet

    here's the relative link

    In the link I say the backward gradient will be 0 eternally.

    In other point, make the |u| underivable that gradient will not be 0. But the gradient of u/|u| is not 1 anymore.

    @yangxue0827 Could you please help me out? Many thanks!

    opened by igo312 10
  • How may I use this repo for building orientation detection ?

    How may I use this repo for building orientation detection ?

    Sorry if my question is irrelevant, but this is what I am instructed to do as an assignment. I am instructed to use this repo for detecting the orientation of various building in an image. Please guide me for the same.

    opened by neutr0nStar 8
  • Request for model files tested on DOTA-v1.5

    Request for model files tested on DOTA-v1.5

    opened by chandlerbing65nm 7
  • Alternative cloud for trained models

    Alternative cloud for trained models

    Hello @yangxue0827,

    I cannot create a Baidu account. Could you upload the trained models in another cloud such as Dropbox, Onedrive o Google Drive? If it is not possible ... could you send me all these trained models via email? Thank you so much.

    Best regards, Roberto Valle

    opened by bobetocalo 7
  • Download Trained Model

    Download Trained Model

    Hello

    I would like to know, where I can download the trained model (not pretrained):

    "Please download trained models by this project, then put them to trained_weights."

    When I go to Baidu and enter the code from here: https://github.com/yangxue0827/RotationDetection/issues/29#issuecomment-896431674 I still cannot download as some pop up comes up that I cannot read. Is there maybe any other place (Google Drive) where I can find it?

    Best regards and thank you

    opened by Testbild 6
  • Where is trained models?

    Where is trained models?

    Thank you for your contribution to the detection community. And I notice that “Latest: More results and trained models are available in the MODEL_ZOO.md.” in README.md,but I can’t find the “MODEL_ZOO.md”. If you could provide the trained model,words cannot express how thankful I am.

    opened by 1995gatch 6
  • ValueError: Tried to convert 'input' to a tensor and failed. Error: None values not supported.

    ValueError: Tried to convert 'input' to a tensor and failed. Error: None values not supported.

    在跑scrnet时出现问题,打印g之后发现 $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ Tensor("tower_0/clip_by_norm_88:0", shape=(3, 3, 256, 256), dtype=float32, device=/device:GPU:0) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ Tensor("tower_1/clip_by_norm_88:0", shape=(3, 3, 256, 256), dtype=float32, device=/device:GPU:1) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ Tensor("tower_0/clip_by_norm_89:0", shape=(1, 1, 256, 1024), dtype=float32, device=/device:GPU:0) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ Tensor("tower_1/clip_by_norm_89:0", shape=(1, 1, 256, 1024), dtype=float32, device=/device:GPU:1) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ None

    opened by xxx0320 6
  • 关于R3det_kl的训练问题

    关于R3det_kl的训练问题

    大佬你好! 想请教一下,在r3det_kl的训练过程中,使用resnet_50的pretrained weights,reg_loss一直震荡,想问问是哪一步出现了问题呢?

    cfgs设置和loss图像如下。

    SAVE_WEIGHTS_INTE = 27000 * 1 CLS_WEIGHT = 1.0 REG_WEIGHT = 2.0 REG_LOSS_MODE = 3 # KLD loss

    sendpix0

    opened by yanglcs 5
  • How to test train and test  retinanet-gwd in HRSC2016 dataset?

    How to test train and test retinanet-gwd in HRSC2016 dataset?

    1.i have downloaded trained models by this project, then put them to $PATH_ROOT/output/pretained_weights. the pretained_weights is resnet_v1d. 2. i have compiled .

    cd $PATH_ROOT/libs/utils/cython_utils
    rm *.so
    rm *.c
    rm *.cpp
    python setup.py build_ext --inplace (or make)
    
    cd $PATH_ROOT/libs/utils/
    rm *.so
    rm *.c
    rm *.cpp
    python setup.py build_ext --inplace
    
    1. i have Copied $PATH_ROOT/libs/configs/HRSC2016/gwd/cfgs_res50_hrsc2016_gwd_v6.py to$PATH_ROOT/libs/configs/cfgs.py
    2. the structure directory of HRSC2016 Dataset image

    5.when i python tools/gwd/train.py ,i got some errors.

    2021-09-05 07:49:28.459600: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at matching_files_op.cc:49 : Not found: ../../dataloader/tfrecord; No such file or directory 2021-09-05 07:49:28.534617: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at matching_files_op.cc:49 : Not found: ../../dataloader/tfrecord; No such file or directory Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call return fn(*args) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn target_list, run_metadata) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.NotFoundError: ../../dataloader/tfrecord; No such file or directory [[{{node get_batch/matching_filenames/MatchingFiles}}]]

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last): File "train.py", line 160, in trainer.main() File "train.py", line 155, in main self.log_printer(gwd, optimizer, global_step, tower_grads, total_loss_dict, num_gpu, graph) File "../../tools/train_base.py", line 196, in log_printer sess.run(init_op) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 956, in run run_metadata_ptr) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1180, in _run feed_dict_tensor, options, run_metadata) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run run_metadata) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.NotFoundError: ../../dataloader/tfrecord; No such file or directory [[node get_batch/matching_filenames/MatchingFiles (defined at /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:1748) ]]

    Original stack trace for 'get_batch/matching_filenames/MatchingFiles': File "train.py", line 160, in trainer.main() File "train.py", line 53, in main is_training=True) File "../../dataloader/dataset/read_tfrecord.py", line 115, in next_batch filename_tensorlist = tf.train.match_filenames_once(pattern) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/input.py", line 76, in match_filenames_once name=name, initial_value=io_ops.matching_files(pattern), File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/gen_io_ops.py", line 464, in matching_files "MatchingFiles", pattern=pattern, name=name) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/op_def_library.py", line 794, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py", line 513, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py", line 3357, in create_op attrs, op_def, compute_device) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py", line 3426, in _create_op_internal op_def=op_def) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py", line 1748, in init self._traceback = tf_stack.extract_stack()

    Can you help me solve this problem? i hope your reply

    opened by myGithubSiki 5
  • 为何nms没有起作用?

    为何nms没有起作用?

    大佬您好,我想请教下为何nms好像没有起作用。系统是Ubuntu18.04,nms部分已经编译了。算法用的是gwd,训练和测试均能正常进行,就是有的好像经过了nms处理,有的又没有,而且训练和测试的时候我都把nms开启了

    post-processing

    NMS = True NMS_IOU_THRESHOLD = 0.45 MAXIMUM_DETECTIONS = 200 FILTERED_SCORE = 0.5 VIS_SCORE = 0.4

    test and eval

    TEST_SAVE_PATH = os.path.join(ROOT_PATH, 'tools/test_result') EVALUATE_R_DIR = os.path.join(ROOT_PATH, 'output/evaluate_result_pickle/') USE_07_METRIC = True EVAL_THRESHOLD = 0.45

    训练: train

    测试: DJI_0005_000090

    opened by Chrispaoge 4
  • not found the implementation of indirect angle regression

    not found the implementation of indirect angle regression

    Great work, thanks for sharing the project. I did not find the implementation of the indirect angle regression which is explained in your TPAMI paper. Have I missed or this part is not included in this public project? Thanks.

    opened by menchael 0
  • Baidu signup impossible for non-chinese users, access to models possible through other apps?

    Baidu signup impossible for non-chinese users, access to models possible through other apps?

    Hi, Baidu doesn't accept international phone numbers so won't allow signing up. This will extremely limit the usage of your model. Have you copied your model weights in another platform such as google drive or microsoft onedrive?

    Please let me know. Thank you

    opened by shnamin 0
  • Tranfer Learning

    Tranfer Learning

    Hello, I have a question regarding training a custom dataset.

    How can I transfer learning of some specific classes from the pre-trained weights (e.g. dota) to my custom training if my custom classes are different from the pre-trained classes?
    

    Best regards and Thank you

    opened by wafa-bouzouita 1
Releases(v1.0.1)
Owner
yangxue
Welcome academic cooperation. WeChat: yangxue-0826
yangxue
PyTorch implementation of Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Simple PyTorch Implementation of "Grokking" Implementation of Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets Usage Running

Teddy Koker 15 Sep 29, 2022
Read and write layered TIFF ImageSourceData and ImageResources tags

Read and write layered TIFF ImageSourceData and ImageResources tags Psdtags is a Python library to read and write the Adobe Photoshop(r) specific Imag

Christoph Gohlke 4 Feb 05, 2022
A simple pytorch pipeline for semantic segmentation.

SegmentationPipeline -- Pytorch A simple pytorch pipeline for semantic segmentation. Requirements : torch=1.9.0 tqdm albumentations=1.0.3 opencv-pyt

petite7 4 Feb 22, 2022
Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training pro

Yang Wenhan 44 Dec 06, 2022
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)

Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation (ICCV 2021) [中文|EN] 概述 本工作主要探索一种高效的多传感器(激光雷达和摄像头)融合点云语义分割方法。现有的多传感器融合方法主要将点云投影

ICE 126 Dec 30, 2022
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
MoveNet Single Pose on OpenVINO

MoveNet Single Pose tracking on OpenVINO Running Google MoveNet Single Pose models on OpenVINO. A convolutional neural network model that runs on RGB

35 Nov 11, 2022
Codes for the compilation and visualization examples to the HIF vegetation dataset

High-impedance vegetation fault dataset This repository contains the codes that compile the "Vegetation Conduction Ignition Test Report" data, which a

1 Dec 12, 2021
Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

Lbl2Vec Lbl2Vec is an algorithm for unsupervised document classification and unsupervised document retrieval. It automatically generates jointly embed

sebis - TUM - Germany 61 Dec 20, 2022
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

Project This repo has been populated by an initial template to help get you started. Please make sure to update the content to build a great experienc

Microsoft 674 Dec 26, 2022
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
Synthetic LiDAR sequential point cloud dataset with point-wise annotations

SynLiDAR dataset: Learning From Synthetic LiDAR Sequential Point Cloud This is official repository of the SynLiDAR dataset. For technical details, ple

78 Dec 27, 2022
An implementation of a discriminant function over a normal distribution to help classify datasets.

CS4044D Machine Learning Assignment 1 By Dev Sony, B180297CS The question, report and source code can be found here. Github Repo Solution 1 Based on t

Dev Sony 6 Nov 09, 2021
sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code

sequitur sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code. It implements three differ

Jonathan Shobrook 305 Dec 21, 2022
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression YOLOv5 with alpha-IoU losses implemented in PyTorch. Example r

Jacobi(Jiabo He) 147 Dec 05, 2022
Double pendulum simulator using a symplectic Euler's method and Hamiltonian mechanics

Symplectic Double Pendulum Simulator Double pendulum simulator using a symplectic Euler's method. The program calculates the momentum and position of

Scott Marino 1 Jan 12, 2022
“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品

“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品,并且能够返回完整地购物清单及顾客应付的实际商品总价格,极大地降低零售行业实际运营过程中巨大的人力成本,提升零售行业无人化、自动化、智能化水平。

thomas-yanxin 192 Jan 05, 2023
Hardware accelerated, batchable and differentiable optimizers in JAX.

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

Google 621 Jan 08, 2023
Pretrained Cost Model for Distributed Constraint Optimization Problems

Pretrained Cost Model for Distributed Constraint Optimization Problems Requirements PyTorch 1.9.0 PyTorch Geometric 1.7.1 Directory structure baseline

2 Aug 28, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022