return GhostNet(cfgs, **kwargs) 我们在models/mobilev3.py中插入MobileNetv3网络,网络结构来源于github网友复现的pytorch版本,真即插即用! 分类效果: 检测效果: 修改后的部分源码如下: class MobileNetV3(nn.Module): def __init__(self, num_classes=1000): super(MobileNetV3, self).__init__() self....
In order to track thr progress, mAP metric is calculated on validation. Installation pip install -U retinaface_pytorch Example inference importcv2fromretinaface.pre_trained_modelsimportget_model image = <numpy array with shape (height, width, 3)> model = get_model("resnet50_2020-07-20", max...
importcv2fromretinaface.pre_trained_modelsimportget_model image = <numpy array with shape (height, width, 3)> model=get_model("resnet50_2020-07-20",max_size=2048)model.eval()annotation=model.predict_jsons(image) Jupyter notebook with the example: ...
models utils widerface_evaluate LICENSE.MIT README.md convert_to_onnx.py detect.py test_fddb.py test_widerface.py train.py README MIT license RetinaFace in PyTorch APyTorchimplementation ofRetinaFace: Single-stage Dense Face Localisation in the Wild. Model size only 1.7M, when Retinaface use ...
['pretrain']: checkpoint = paddle.load("./weights/mobilenetV1X0.25_pretrain.pdparams") backbone.set_state_dict(checkpoint) elif cfg['name'] == 'Resnet50': import paddle.vision.models as models backbone = models.resnet50(pretrained=cfg['pretrain']) self.body = backbone in_channels_...
models add resnet50 for training 5年前 utils first commit 5年前 widerface_evaluate first commit 5年前 LICENSE.MIT Add license 5年前 README.md Update README.md 5年前 convert_to_onnx.py add pytorch_to_onnx 5年前 detect.py add resnet50 for training ...
4.4. Five Facial Landmark Localisation Face detection datasets are more challenging [59] than the face alignment datasets, which are usually collected by a pre-trained face detector with a high threshold. In the fol- lowing experiments, we explore the benefits of ...
Additionally, CNNs can be trained using large-scale parallel computing, which allows for efficient training of very deep and complex models. The single-shot detector (SSD) is one of the most renowned face detection systems that employs deep learning [6]. It utilizes a convolutional neural ...
All training settings of the models can be found in the corresponding ./configs/*.yaml files. You can see the tensorboard traning logs in here. Based on the property of the training dataset, all the pre-trained models can only be used for non-commercial applications. References 🍔 Thanks ...
import torch import os import struct from models.retinaface import RetinaFace from data import cfg_mnet, cfg_re50 import torch.backends.cudnn as cudnn import argparse parser = argparse.ArgumentParser(description='Retinaface') parser.add_argument('-m', '--trained_model', default='./weights/mobile...