All Projects → ItchyHiker → Iris_Landmarks_PyTorch

ItchyHiker / Iris_Landmarks_PyTorch

Licence: other
Iris landmarks localization

Programming Languages

python
139335 projects - #7 most used programming language
shell
77523 projects

Projects that are alternatives of or similar to Iris Landmarks PyTorch

libdeepvac
Use PyTorch model in C++ project
Stars: ✭ 98 (+188.24%)
Mutual labels:  ncnn
YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
Stars: ✭ 6,570 (+19223.53%)
Mutual labels:  ncnn
ncnn-android-squeezenet
The squeezenet image classification android example
Stars: ✭ 100 (+194.12%)
Mutual labels:  ncnn
RobotVision2
移动端实时疲劳驾驶检测
Stars: ✭ 23 (-32.35%)
Mutual labels:  ncnn
perceptronCobol
A perceptron written in COBOL
Stars: ✭ 64 (+88.24%)
Mutual labels:  iris-dataset
Dyamic Graph Representation
Official Dynamic Graph Representation PyTorch implement for iris/face recognition
Stars: ✭ 22 (-35.29%)
Mutual labels:  iris-recognition
ncnn-yolov4-int8
NCNN+Int8+YOLOv4 quantitative modeling and real-time inference
Stars: ✭ 20 (-41.18%)
Mutual labels:  ncnn
BurningEyes
Iris recognition program according to John Daugman's papers
Stars: ✭ 16 (-52.94%)
Mutual labels:  iris-recognition
daisykit
Daisykit is an easy AI toolkit for software engineers to integrate pretrained AI models and pipelines into their projects. - with NCNN, OpenCV, Python wrappers
Stars: ✭ 22 (-35.29%)
Mutual labels:  ncnn
ncnn-tensorflow
add tensorflow ops to ncnn
Stars: ✭ 29 (-14.71%)
Mutual labels:  ncnn
YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
Stars: ✭ 1,230 (+3517.65%)
Mutual labels:  ncnn
ffcnn
ffcnn is a cnn neural network inference framework, written in 600 lines C language.
Stars: ✭ 50 (+47.06%)
Mutual labels:  ncnn
cain-ncnn-vulkan
CAIN, Channel Attention Is All You Need for Video Frame Interpolation implemented with ncnn library
Stars: ✭ 85 (+150%)
Mutual labels:  ncnn
Jetson-Nano-image
Jetson Nano image with deep learning frameworks
Stars: ✭ 46 (+35.29%)
Mutual labels:  ncnn
extra keras datasets
📃🎉 Additional datasets for tensorflow.keras
Stars: ✭ 20 (-41.18%)
Mutual labels:  iris-dataset
PSGAN-NCNN
PSGAN running with ncnn⚡妆容迁移/仿妆⚡Imitation Makeup/Makeup Transfer⚡
Stars: ✭ 140 (+311.76%)
Mutual labels:  ncnn
ClothingTransfer-NCNN
CT-Net, OpenPose, LIP_JPPNet, DensePose running with ncnn⚡服装迁移/虚拟试穿⚡ClothingTransfer/Virtual-Try-On⚡
Stars: ✭ 166 (+388.24%)
Mutual labels:  ncnn
YoloV3-ncnn-Raspberry-Pi-4
MobileNetV2_YOLOV3 for ncnn framework
Stars: ✭ 20 (-41.18%)
Mutual labels:  ncnn
lite.ai.toolkit
🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, MODNet, YOLOR, NanoDet, YOLOX, SCRFD, YOLOX . MNN, NCNN, TNN, ONNXRuntime, CPU/GPU.
Stars: ✭ 1,354 (+3882.35%)
Mutual labels:  ncnn
InferenceHelper
C++ Helper Class for Deep Learning Inference Frameworks: TensorFlow Lite, TensorRT, OpenCV, OpenVINO, ncnn, MNN, SNPE, Arm NN, NNabla, ONNX Runtime, LibTorch, TensorFlow
Stars: ✭ 142 (+317.65%)
Mutual labels:  ncnn

Dense Iris Landmarks

Repo for dense iris landmarks localization with synthesized eye dataset.

Repo Structure

./config.py: config file

./loss.py: loss function

./checkpoint.py: save the trained model

./tools: some utitilies

./test: test results

Prepare Dataset

There are two methods to prepare the training data.

  1. You could use the software here to synthesiz all kinds of data yourself. Then use scripts in ./gen_dataset to generate training data.

  2. You could also use the dataset I provided. directly. Just download the dataset and put train images in ./data directory. In this case the annotations are already prepared in annotations directory.

    Adress:https://pan.baidu.com/s/1gzYAVvEuhuu6L8tos3zXAQ
    Password:990n

Train

config all the training parameters in config.py RUN python training/train.py to train your model.

Training results are kept in results directory.

Test

Put well croped eye images in ./data/test RUN python test/test_image.py to test your model.

result.jpg

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].