![nonude top 100 models nonude top 100 models](https://static01.nyt.com/images/2021/09/09/fashion/07MODELS-MARIE-COMBO/04MODELS-MARIE-COMBO-superJumbo.jpg)
- #Nonude top 100 models how to#
- #Nonude top 100 models full#
- #Nonude top 100 models code#
- #Nonude top 100 models download#
Single Stage Detector: real-time CNN for object detection that detects 80 different classes.Ī variant of MobileNet that uses the Single Shot Detector (SSD) model framework.
#Nonude top 100 models full#
A smaller version of the more complex full YOLOv2 network. Model ClassĪ real-time CNN for object detection that detects 20 different classes. Semantic segmentation models partition an input image by labeling each pixel into a set of pre-defined categories. Object detection models detect the presence of multiple objects in an image and segment out areas of the image where the objects are detected. Model Classĭeep CNN model for handwritten digit identification This subset of models classify images for specific domains and datasets. This network architecture design considers direct metric such as speed, instead of indirect metric like FLOP.ĭeep CNN model (up to 8 layers) that increased the number of features that the network is capable of detecting that helps to pick image features at a finer level of resolution.ĬNN model with an order of magnitude of few computations and parameters, while still acheiving state-of-the-art accuracy and better efficiency than previous ConvNets. Compared to MobileNet, ShuffleNet achieves superior performance by a significant margin due to it's efficient structure.Įxtremely computation efficient CNN model that is designed specifically for mobile devices. This model greatly reduces the computational cost and provides a ~13x speedup over AlexNet on ARM-based mobile devices. This model has reduced computational cost and improved image resolution compared to Inception v1.Įxtremely computation efficient CNN model that is designed specifically for mobile devices. This model is same as GoogLeNet, implemented through Caffe2 that has improved utilization of the computing resources inside the network and helps with the vanishing gradient problem.ĭeep CNN model for Image Classification as an adaptation to Inception v1 with batch normalization. Model that has every layer connected to every other layer and passes on its own feature providing strong gradient flow and more diversified features. This model uses localization of regions to classify and extract features from images. Pure Caffe implementation of R-CNN for image classification. Comparatively smaller and faster than VGG and more accurate in detailing than AlexNet.ĭeep CNN variation of AlexNet for Image Classification in Caffe where the max pooling precedes the local response normalization (LRN) so that the LRN takes less compute and memory. Similar to AlexNet but uses multiple smaller kernel-sized filters that provides more accuracy when classifying images.Ī Deep CNN model (up to 8 layers) where the input is an image and the output is a vector of 1000 numbers.ĭeep CNN model(up to 22 layers). Uses shortcut connections to achieve higher accuracy when classifying images.Ī light-weight CNN model providing AlexNet level accuracy with 50x fewer parameters.ĭeep CNN model(up to 19 layers). Light-weight deep neural network best suited for mobile and embedded vision applications.Ī CNN model (up to 152 layers). This collection of models take images as input, then classifies the major objects in the images into 1000 object categories such as keyboard, mouse, pencil, and many animals.
#Nonude top 100 models how to#
INT8 models are generated by Intel® Neural Compressor, read the Introduction to know how to use it to quantize ONNX model.
#Nonude top 100 models code#
npz), downloading multiple ONNX models through Git LFS command line, and starter Python code for validating your ONNX model using test data. Models Read the Usage section below for more details on the file formats in the ONNX Model Zoo (.onnx.
#Nonude top 100 models download#
To download an ONNX model, navigate to the appropriate Github page and click the Download button on the top right. We have standardized on Git LFS (Large File Storage) to store ONNX model files. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The ONNX Model Zoo is a collection of pre-trained, state-of-the-art models in the ONNX format contributed by community members like you. ONNX is supported by a community of partners who have implemented it in many frameworks and tools. Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models.