Saravanan666/pypi-org huggingface dataset:
Package: yolov5processor
Yolo v5 Inference EngineBasically a wrapper for the detection program for ease of use.How to install:pip install yolov5processorHow to use:fromyolov5processor.inferimportExecuteInferencemodel=ExecuteInference(weight="path-to-weight.pt",confidence=0.4,\img_size=640,agnostic_nms=False,gpu=False,iou=0.5)image=cv2.imread("imagepath.jpg")pred=model.predict(image)Analyse the Output:print(pred){"points":[100,120,400,500],"conf":0.7,"class":"apple"}points key represents the bounding box coordinates in the order x1, y1, x2, y2 respectively.
conf key represents the confidence.
class key represents the object class.Update:Added compatibility with torch 1.9 and torchvision 0.10
Package: yolo-v5-tflite
yolo-v5-tfliteYOLOv5 model in TensorFlow Lite (TFLite) for Object Detection over 1000 classes(coco dataset).Installationpip3 install yolo-v5-tfliteHow To Runfrom yolo_v5_tflite.yolo_v5 import detect
detect('zidane.jpg')
Package: yolov5tospace
yolov5tospaceAutomatically create  Gradio apps from YOLOv5 models and push to the Spacesinstallationpipinstallyolov5tospaceapi usagefromyolov5tospaceimportyolov5_to_space_pipelineyolov5_to_space_pipeline(base_name="Visdrone",model_types=["yolov5n"],hfhub_username=YOUR-HFHUB-USERNAME,task="object-detection",test_images_folder=None,)orfromyolov5tospaceimportyolov5_to_space_pipelineyolov5_to_space_pipeline(base_name="Visdrone",hfhub_model_ids=[YOUR-HFHUB-MODELID-1,YOUR-HFHUB-MODELID-2],hfhub_username=YOUR-HFHUB-USERNAME,task="object-detection",test_images_folder=None,)
Package: yolov5-utils
YOLOv5 Installable PackagePackagedultralytics/yolov5Inspired fromhttps://github.com/fcakyon/yolov5-pip.Build PackageBuild wheel file:pipinstallbuild
python-mbuildInstall with prebuilt packagePackage-built has uploaded to pypi and just install with the command:pipinstallyolov5-utilsGeneral importsInsertyolov5_utils.yolov5.prefix when importing modulesmodelsandutils.
For example:fromyolov5_utils.yolov5.models.commonimportDetectMultiBackendAuthorsMsclock-msclock@qq.com-Github account
Package: yolov5windowdetect
Capture window - run yolov5 - show resultspipinstallyolov5windowdetectUpdate 2022/10/25Run multiple detection modelswinca=(Yolov5WindowDetect(pt_file=r"C:\Users\Gamer\anaconda3\envs\dfdir\yolov5\runs\train\playerbutton7\weights\best.pt",repo_or_dir="./yolov5",model="custom",source="local",).add_models(pt_file=r"C:\Users\Gamer\anaconda3\envs\dfdir\yolov5\runs\train\playerbutton6\weights\best.pt",repo_or_dir="./yolov5",model="custom",source="local",).get_hwnd_window(window_title_regex=r"[Bb]lue[sS]tacks.*").take_screenshot_and_run_yolov(confidence_thresh=0.3,show_results=True,quit_key="q",sleep_time=0.04,rununtilstopped=True,))fromyolov5windowdetectimportYolov5WindowDetectwinca=(Yolov5WindowDetect(pt_file=r"C:\Users\.....\best.pt",repo_or_dir="./yolov5",model="custom",source="local",).get_hwnd_window(window_title_regex=r"[Bb]lue[Ss]tacks.*").take_screenshot_and_run_yolov(confidence_thresh=0.3,show_results=True,quit_key="q",sleep_time=0.04,rununtilstopped=True,))# window capture -> don't show results, only results as dfwinca.get_hwnd_window(window_title_regex=r"[Bb]lue[Ss]tacks.*").take_screenshot_and_run_yolov(confidence_thresh=0.3,show_results=False,rununtilstopped=False,).get_results_as_df()# adb capturewinca.get_adb_window(adb_path=r"C:\ProgramData\adb\adb.exe",adb_serial="localhost:5555",).take_screenshot_and_run_yolov(confidence_thresh=0.3,show_results=False,rununtilstopped=False,).get_results_as_df()Example<a href="https://www.youtube.com/watch?v=-jXqL39Tf5w">

     <img src="https://img.youtube.com/vi/-jXqL39Tf5w/0.jpg" style="width:100%;">

  </a>
Package: yolov6detect
Yolov6-Pip: Packaged version of the Yolov6 repositoryOverviewThis repo is a packaged version of theYolov6model.BenchmarkModelSizemAPval0.5:0.95SpeedT4trt fp16 b1(fps)SpeedT4trt fp16 b32(fps)Params(M)FLOPs(G)YOLOv6-N64037.577911874.711.4YOLOv6-S64045.033948418.545.3YOLOv6-M64050.017522634.985.8YOLOv6-L64052.89811659.6150.7YOLOv6-N6128044.922828110.449.8YOLOv6-S6128050.39810841.4198.0YOLOv6-M6128055.2475579.6379.5YOLOv6-L6128057.22629140.4673.4Installationpip install yolov6detectYolov6 Inferencefromyolov6importYOLOV6model=YOLOV6(weights='yolov6s.pt',device='cuda:0')#model = YOLOV6(weights='kadirnar/yolov6t-v2.0', device='cuda:0', hf_model=True)model.classes=Nonemodel.conf=0.25model.iou_=0.45model.show=Falsemodel.save=Truepred=model.predict(source='data/images',yaml='data/coco.yaml',img_size=640)Citation@article{li2022yolov6,title={YOLOv6: A single-stage object detection framework for industrial applications},author={Li, Chuyi and Li, Lulu and Jiang, Hongliang and Weng, Kaiheng and Geng, Yifei and Li, Liang and Ke, Zaidan and Li, Qingyuan and Cheng, Meng and Nie, Weiqiang and others},journal={arXiv preprint arXiv:2209.02976},year={2022}}
Package: yolov7detect
Yolov7-Pip: Packaged version of the Yolov7 repositoryOverviewThis repo is a packaged version of theYolov7model.Installationpip install yolov7detectYolov7 Inferenceimportyolov7# load pretrained or custom modelmodel=yolov7.load('yolov7.pt')#model = yolov7.load('kadirnar/yolov7-v0.1', hf_model=True)# set model parametersmodel.conf=0.25# NMS confidence thresholdmodel.iou=0.45# NMS IoU thresholdmodel.classes=None# (optional list) filter by class# set imageimgs='inference/images'# perform inferenceresults=model(imgs)# inference with larger input size and test time augmentationresults=model(img,size=1280,augment=True)# parse resultspredictions=results.pred[0]boxes=predictions[:,:4]# x1, y1, x2, y2scores=predictions[:,4]categories=predictions[:,5]# show detection bounding boxes on imageresults.show()Citation@article{wang2022yolov7,title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},journal={arXiv preprint arXiv:2207.02696},year={2022}}AcknowledgementA part of the code is borrowed fromYolov5-pip. Many thanks for their wonderful works.
Package: yolov7-easy
Yolov7-Pip: Packaged version of the Yolov7 repositoryOverviewThis repo is a packaged version of theYolov7model.Installationpip install yolov7detectYolov7 Inferenceimportyolov7# load pretrained or custom modelmodel=yolov7.load('yolov7.pt')# set model parametersmodel.conf=0.25# NMS confidence thresholdmodel.iou=0.45# NMS IoU thresholdmodel.classes=None# (optional list) filter by class# set imageimgs='inference/images'# perform inferenceresults=model(imgs)# inference with larger input size and test time augmentationresults=model(img,size=1280,augment=True)# parse resultspredictions=results.pred[0]boxes=predictions[:,:4]# x1, y1, x2, y2scores=predictions[:,4]categories=predictions[:,5]# show detection bounding boxes on imageresults.show()Citation@article{wang2022yolov7,title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},journal={arXiv preprint arXiv:2207.02696},year={2022}}AcknowledgementA part of the code is borrowed fromYolov5-pip. Many thanks for their wonderful works.
Package: yolov7-package
WongKinYiu/yolov7 as independent packageBindings for yolov7 project (https://github.com/WongKinYiu/yolov7)Example of usage:fromyolov7_packageimportYolov7Detectorimportcv2if__name__=='__main__':img=cv2.imread('img.jpg')det=Yolov7Detector(traced=False)classes,boxes,scores=det.detect(img)img=det.draw_on_image(img,boxes[0],scores[0],classes[0])cv2.imshow("image",img)cv2.waitKey()You can usetraced=Trueoption to download and infer traced FP16 version of yolov7 model!PYPI link:https://pypi.org/project/yolov7-package/
Package: yolov7-wky
YoloV7A packaged version of the yolov7 implementation by Wong Kin Yiu:https://github.com/WongKinYiu/yolov7Example usageThe package contains bothmodelsandutilsas importable modules, usingimport modelsandimport utilsrespectively.The scripts found in the root directory of the repository (such asexport.pyandtrain.py) can be ran directly from the command line. To prevent naming
conflicts, they have been renamed to add 'yolov7_' at the beginning. For
example,export.pycan be executed with the following command:yolov7_export--weightsyolov7.pt...
Package: yolov7-wky-package
YoloV7A packaged version of the yolov7 implementation by Wong Kin Yiu:https://github.com/WongKinYiu/yolov7Example usageThe package contains bothmodelsandutilsas importable modules, usingimport modelsandimport utilsrespectively.The scripts found in the root directory of the repository (such asexport.pyandtrain.py) can be ran directly from the command line. To prevent naming
conflicts, they have been renamed to add 'yolov7_' at the beginning. For
example,export.pycan be executed with the following command:yolov7_export--weightsyolov7.pt...
Package: yolov8
:warning: Important Notice for Ultralytics YOLOv8 UsersPlease note that theyolov8Python package here is a placeholder and not the official package from Ultralytics. For the most reliable and up-to-date features, you should instead install the officialultralyticspackage athttps://pypi.org/project/ultralytics/.To do this, simply run the following command in your Python environment:pipinstallultralyticsThis will ensure you are using the most accurate, efficient, and maintained code for your YOLO models. Please seehttps://docs.ultralytics.com/quickstart/for full documentation on installing and getting started with Ultralytics YOLOv8.Thank you for your understanding and cooperation.
Package: yolov8face
yolov8faceYolov8face is a Python wrapper of Ultrlytics that simplifies the process of detecting faces in images using the yolov8n-face model. It takes care of pre-trained model downloading, loading and postprocessing, allowing you to detect faces in images with just a few lines of code.Installationpipinstallyolov8faceUsagefromyolov8faceimportget_bboxbboxes=get_bbox('path-to-image/image.jpg')ExampleLicenseThis project is licensed under the GPL-3.0 License - see theLICENSEfile for details.Please note that this project uses the YOLOv8 model from the Ultralytics team, which is also licensed under the GPL-3.0 License. For commercial uses, please contact the Ultralytics team for an Enterprise License.CreditsThis package was developed byHarsh Thaker.Special thanks to:akanametovfor theyolov8n-facemodel.TheUltralyticsteam for providing the YOLOv8.This project stands on the shoulders of the open-source community. It wouldn't be possible without the numerous open source projects we've utilised.
Package: yolov8-onnx
YOLOV8 ONNXDescriptionThis package is compatible withYoloV8for object detection program, usingONNXformat model (CPU speed can be x2 times faster). This code is referenced fromthis awesome repo.InstallationYou can install the package using pip:pipinstallyolov8_onnxUsageStep 1: Convert your pre-trained model to ONNX format.fromultralyticsimportYOLO# Load your pre-trained modelmodel=YOLO('your-trained-model.pt')# Export the modelmodel.export(format='onnx',imgsz=640,dynamic=True)Step 2: Use in your code.fromyolov8_onnximportDetectEngineengine=DetectEngine(model_path='your-model.onnx',conf_thres=0.5,iou_thres=0.1)output=engine(image)# cv2 imageLicenseThis project is licensed under the Apache License 2.0 - see the LICENSE file for details." I hope this package will be useful for your projects" Nguyễn Trường Lâu (fromakaOCR).
Package: yolov9pip
Yolov9-Pip: Packaged version of the Yolov9 repositoryThis repo is a packaged version of theYolov9model.⭐ Installationpip install yolov9pip🌠 Yolov9 Inferenceimportyolov9# load pretrained or custom modelmodel=yolov9.load("yolov9-c.pt",device="cpu",)# set model parametersmodel.conf=0.25# NMS confidence thresholdmodel.iou=0.45# NMS IoU thresholdmodel.classes=None# (optional list) filter by class# set imageimgs="data/zidane.jpg"# perform inferenceresults=model(imgs)# inference with larger input size and test time augmentationresults=model(img,size=640)# parse resultspredictions=results.pred[0]boxes=predictions[:,:4]# x1, y1, x2, y2scores=predictions[:,4]categories=predictions[:,5]# show detection bounding boxes on imageresults.show()😍 Contributingpipinstall-rdev-requirements.txt
pre-commitinstall
pre-commitrun--all-files🤗 Citation@article{wang2024yolov9,title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},author={Wang, Chien-Yao  and Liao, Hong-Yuan Mark},booktitle={arXiv preprint arXiv:2402.13616},year={2024}}AcknowledgementA part of the code is borrowed fromYolov5-pip. Many thanks for their wonderful works.
Package: yoloxdetect
Yolox-Pip: This is a packaged version of the YOLOX for easy installation and use.OverviewThis repo is a packaged version of theYOLOXfor easy installation and use.Installationpip install yoloxdetectYolox InferencefromyoloxdetectimportYoloxDetectormodel=YoloxDetector(model_path="kadirnar/yolox_s-v0.1.1",# or "data/weights/yolox_s.pth"config_path="configs.yolox_s",device="cuda:0",hf_model=True,)model.classes=Nonemodel.conf=0.25model.iou=0.45model.show=Falsemodel.save=Truepred=model.predict(image='data/images',img_size=640)Citation@article{yolox2021,title={YOLOX: Exceeding YOLO Series in 2021},author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},journal={arXiv preprint arXiv:2107.08430},year={2021}}
Package: yomi
yomiA collection of tools for running tournaments of AI agents against each-other on various gamesInstallpipenv install yomiLocal DevelopmentFor local work, setting up a newPipfileandPipfile.lockcan be acomplished via:pipenv install -e .
pipenv install --dev -e .[dev]ExampleRunning an examplerandom_unlimited_tournamentfrom yomi.base import random_unlimited_tournament
from yomi.agents import RandomAgent
from yomi.games import TicTacToe

results = random_unlimited_tournament(TicTacToe, ((RandomAgent, {'name': 'p1'}), (RandomAgent, {'name': 'p2'})))
print(results[0].mean())Testingpytestflake8
Package: yomidict
yomidictCreate frequency dictionaries for yomichan out of media.Currently supported formats are: epub, html, srt, ass, txtpipinstallyomidictMWE:importyomidictdm=yomidict.DictMaker()filelist=["test.html"]*5+["test.epub"]*2+["test.srt"]*2dm.feed_files(filelist)dm.save("zipfile.zip","name_in_yomichan",use_suffix=True)Docs:DictMaker ObjectwcounterIs a Counter which saves the number of occurences for the tokens that were found during feeding.refcounterKeeps track of in how many files a certain token was found. E.g. a value of 0.5 (if normalized) would mean that the token occurs in 50% of all files that were fed.DictMaker.feedfiles()deffeed_files(self,filelist,skip_errors=True,reset_refcounter=True,normalize_refcounter=True,)skip_erros: does exactly as the name suggests, it skips errors. During processing of a bunch of files all sorts of errors could occur which would abort the feeding. This might be undesirable and so they can be skipped. The errored files will also be taken in consideration when calculating the DictMaker.refcounter.reset_refcounter: resets the refcounter before feeding files.normalize_refcounter: count/total_number_of_files. Therefore, if a token comes up in 8 out of 10 books the value of the counter would be 0.8 instead of 8. This makes it easier to read even without knowing the total number of files that were fed into DictMaker.DictMaker.save()defsave(self,filepath,dictname,only_rank_and_freq=False,use_suffix=True,use_suffix_rank=False,use_suffix_freq=False,)only_rank_and_freq: by default it the word rank, the word frequency and the refcounter_value get saved. This deactivates the refcounter_value.use_suffix: activates use_suffix_rank and use_suffix_freq.use_suffix_rank: if the number is above 1000 the number gets replaced by "num/1000 K" e.g. 2530 becomes 2K and 2434455 becomes 2M.use_suffix_freq: same as use_suffix_freq but for the frequencyDictMaker.feed_text()deffeed_text(self,text,refcounter_add=False)can be used to feed a string into DictMaker.refcounter_add: If true it adds 1 occurrence in refcounter to all the tokens that were found in the fed text.How to feed a large text fileDo you want to use refcounter? If yes, do you know the number of works inside the large text file? No? Don't use refcounter.If you do know the number of works inside the large text file, do you know where one work ends and the other begins? Nice, just read it as chunks and let it add to the refcounter and normalize it in the end. If not, don't use refcounter.To feed a large text file you can just read the text file line by line or sentence by sentence and utilizie theDictMaker._clean_txt()function.dm=yomidict.DictMaker()forlineinlarge_txt_file:dm.feed_text(dm._clean_txt(line))If you know the boundaries of each work and can it eat in chunks you could something like this:dm=yomidict.DictMaker()forworkinlarge_txt_file:dm.feed_text(dm._clean_txt(work),refcounter_add=True)dm.normalize_refcounter(works_in_large_txt_file)
Package: yomigana-ebook
yomigana ebookThis project is aimed at making Japanese eBooks more friendly to those who are learning Japanese now by adding readings for every Kanji in the eBooks.To achieve this, the project utilizes Mecab, a Japanese morphological analyzer, and Unidic, a dictionary developed by NICT, to tokenize words and obtain the corresponding yomigana (reading) of each word. This information is then inserted above or besides the kanji characters in the eBook text, allowing readers to easily read and understand the pronunciation of each word.This tool is also the fastest converter now. (compared toMumumu4/furigana4epub&itsupera/furiganalyse)Usageinstall the package fromPyPI:# install the package
$ pip install yomigana-ebook

# download the unidic dictionary
$ python -m unidic download

# you can convert your japanese ebooks now!
$ yomigana_ebook [your-ebooks]build from the source:$ git clone https://github.com/rabbit19981023/yomigana-ebook.git

# install the project
$ pip install ./yomigana-ebook

# download the unidic dictionary
$ python -m unidic download

# you can convert your japanese ebooks now!
$ yomigana_ebook [your-ebooks]For Windows UsersThere's a known bug in Windows, see polm/fugashi#42 for more informationTo solve it, you must install & use the package in an isolatedvirtualenv:Here are some examples:install the package fromPyPI:# create the virtualenv
$ python -m venv .venv

# get into the virtualenv
$ .venv\Scripts\activate

# install the package
$ pip install yomigana-ebook

# download the unidic dictionary
$ python -m unidic download

# you can convert your japanese ebooks now!
$ yomigana_ebook [your-ebooks]build from source:$ git clone https://github.com/rabbit19981023/yomigana-ebook.git

$ cd yomigana-ebook

# create the virtualenv
$ python -m venv .venv

# get into the virtualenv
$ .venv\Scripts\activate

# install the project
$ pip install .

# download the unidic dictionary
$ python -m unidic download

# you can convert your japanese ebooks now!
$ yomigana_ebook [your-ebooks]To leave thevirtualenv, just by a simple command:$ deactivateRun the web demo via DockerNOTE: you NEED to run these commands in the project root, NOT inweb-demodirectory!first we need to build the image:$ docker build -t yomigana-ebook/web-demo -f Dockerfile.web-demo .then run the container:$ docker run --rm -p 8000:8000 yomigana-ebook/web-demo --host 0.0.0.0finally just open your browser and navigate tohttp://localhost:8000to use it!CreditsThis project was inspired byMumumu4/furigana4epub&itsupera/furiganalyse, and has some code from them.
Package: yomikata
yomikataYomikatauses context to resolve ambiguous words in Japanese. Check out theinteractive demo!Yomikatasupports 130 ambiguous forms and reaches a global accuracy of 94%. See the demo page for detailed performance information.Yomikatafollows the approach ofSato et al. 2022by fine-tuning the Tohoku group'sJapanese BERT transformerto classify words into different readings based on the sentence context. A similar approach was used in English byNicolis et al. 2021.Yomikatarecognizes ~50% more heteronyms than Sato et al. by adding support for words which are not in the original BERT vocabulary, and it expands the originalAozora BunkoandNDL titlestraining data to include thecore BCCWJ corpusand theKWDLC corpus.Usagefromyomikata.dbertimportdBertreader=dBert()reader.furigana('そして、畳の表は、すでに幾年前に換えられたのか分らなかった。')# => そして、畳の{表/おもて}は、すでに幾年前に換えられたのか分らなかった。This example sentence, from the short storyWhen I Was looking for a Room to Let(1923) by Mimei Ogawa, contains the very common heteronym 表, which admits the readingsomote(surface) andhyō(table).Yomikata's dBert (disambiguation BERT) correctly determines that in this sentence it refers to the surface of a tatami mat and should be readomote.The furigana function outputs the sentence with the heteronym annotated. Readings for the other words can be obtained with a simple dictionary lookup.fromyomikata.dictionaryimportDictionarydictreader=Dictionary()# defaults to unidic.dictreader.furigana("そして、畳の{表/おもて}は、すでに幾年前に換えられたのか分らなかった。")# => そして、{畳/たたみ}の{表/おもて}は、すでに{幾年/いくねん}{前/まえ}に{換/か}えられたのか{分/わ}らなかった。WithoutYomikata, the dictionary outputs the wrong reading for the heteronym.Installationpipinstallyomikatapython-myomikatadownloadThe second command is necessary to download the model weights, which are too large to host PyPI.Inference should work fine on CPU.For details on data processing and training, see themain notebook.
Package: yomitai
# yomitai
CLI Application for quick reference of Japanese-Yomikata.## Usage
<pre>
<code>$ yomitai 劈く
つんざく
</code></pre><pre>
<code>$ yomitai –kana 齟齬
ソゴ
$ yomitai –k 齟齬
ソゴ
</code></pre><pre>
<code>$ yomitai –romaji 炙りカルビ
aburi karubi
$ yomitai –r 炙りカルビ
aburi karubi
</code></pre>## TODO
- [ ] add –detail flag using Apple Dictionary or Web Dictionary
Package: yomo
==========Yo Modules==========Python 3 modules for atmospheric research, especially airborne sunphotometry.Yo comes from the Japanese word for the Sun... moduleauthor:: Yohei Shinozuka <Yohei.Shinozuka@nasa.gov>Examples:from yomo import assemble, Lookwith assemble(r'c:\data\source\AIRHOUSEKEEPING\Hskping_P3_20160910_R0.ict') as dataset:with Look(dataset, 'time', 'Relative_Humidity') as lk0:pass