pykeras's Stars
pmndrs/zustand
🐻 Bear necessities for state management in React
wagoodman/dive
A tool for exploring each layer in a docker image
PaddlePaddle/PaddleOCR
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
docker/awesome-compose
Awesome Docker Compose samples
ManimCommunity/manim
A community-maintained Python framework for creating mathematical animations.
postcss/autoprefixer
Parse CSS and add vendor prefixes to rules by Can I Use
valkey-io/valkey
A flexible distributed key-value datastore that supports both caching and beyond caching workloads.
VinciGit00/Scrapegraph-ai
Python scraper based on AI
zhanymkanov/fastapi-best-practices
FastAPI Best Practices and Conventions we used at our startup
rapidsai/cudf
cuDF - GPU DataFrame Library
H-M-H/Weylus
Use your tablet as graphic tablet/touch screen on your computer.
pop-os/shell
Pop!_OS Shell
ultrajson/ultrajson
Ultra fast JSON decoder and encoder written in C with Python bindings
FreedomIntelligence/LLMZoo
⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡
deepdoctection/deepdoctection
A Repo For Document AI
pydata/numexpr
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
autodistill/autodistill
Images to inference with no labeling (use foundation models to train supervised models).
rapidsai/cugraph
cuGraph - RAPIDS Graph Analytics Library
google-research/deduplicate-text-datasets
hezarai/hezar
The all-in-one AI library for Persian, supporting a wide variety of tasks and modalities!
mrrfv/open-android-backup
Back up your device without vendor lock-ins, using insecure software or root. Supports encryption and compression out of the box. Works cross-platform.
keyding/Operator-Mono
A nice code font
sysid/sse-starlette
gridhead/nvidia-auto-installer-for-fedora-linux
A CLI tool which lets you install proprietary NVIDIA drivers and much more easily on Fedora Linux (32 or above and Rawhide)
codeniko/shape_predictor_81_face_landmarks
Custom shape predictor model trained to find 81 facial feature landmarks given any image
snok/asgi-correlation-id
Request ID propagation for ASGI apps
Blosc/python-blosc
A Python wrapper for the extremely fast Blosc compression library
michalfaber/tensorflow_Realtime_Multi-Person_Pose_Estimation
Multi-Person Pose Estimation project for Tensorflow 2.0 with a small and fast model based on MobilenetV3
lrsoenksen/SPL_UD_DL
A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to im- proved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatolog- ical patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.
pykeras/SPL_UD_DL
A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to im- proved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatolog- ical patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.