The original weights of some Caffe models, ported to PyTorch.

Last update: Nov 04, 2022

Related tags

Overview

pytorch-caffe-models

This repo contains the original weights of some Caffe models, ported to PyTorch. Currently there are:

GoogLeNet (Going Deeper with Convolutions):

The GoogLeNet model in torchvision was trained from scratch by the PyTorch team with very different data preprocessing and has very differently scaled internal activations, which can be important when using the model as a feature extractor.

There is also a tool (dump_caffe_model.py) to dump Caffe model weights to a more portable format (pickles of NumPy arrays), which requires Caffe and its Python 3 bindings to be installed. A script to compute validation loss and accuracy (validate.py) is also included (the ImageNet validation set can be obtained from Academic Torrents).

Usage

Basic usage

This outputs logits for 1000 ImageNet classes for a black (zero) input image:

import pytorch_caffe_models

model, transform = pytorch_caffe_models.googlenet_bvlc()

model(transform(torch.zeros([1, 3, 224, 224])))

The original models were trained with BGR input data in the range 0-255, which had then been scaled to zero mean but not unit standard deviation. The model-specific transform returned by the pretrained model creation function expects RGB input data in the range 0-1 and it will differentiably rescale the input and convert from RGB to BGR.

Feature extraction

Using the new torchvision feature extraction utility:

from torchvision.models import feature_extraction

layer_names = feature_extraction.get_graph_node_names(model)[1]

Then pick your favorite layer (we can use inception_4c.conv_5x5)

model.eval().requires_grad_(False)
extractor = feature_extraction.create_feature_extractor(model, {'inception_4c.conv_5x5': 'out'})

input_image = torch.randn([1, 3, 224, 224]) / 50 + 0.5
input_image.requires_grad_()

features = extractor(transform(input_image))['out']
loss = -torch.sum(features**2) / 2
loss.backward()

input_image now has its .grad attribute populated and you can normalize and descend this gradient for DeepDream or other feature visualization methods. (The BVLC GoogLeNet model was the most popular model used for DeepDream.)

You might also like...

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F

2.8k Dec 29, 2022

A python code to convert Keras pre-trained weights to Pytorch version

Weights_Keras_2_Pytorch 最近想在Pytorch项目里使用一下谷歌的NIMA，但是发现没有预训练好的pytorch权重，于是整理了一下将Keras预训练权重转为Pytorch的代码，目前是支持Keras的Conv2D, Dense, DepthwiseConv2D, Batch

2 Dec 16, 2021

DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto

145 Dec 30, 2022

Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

165 Nov 22, 2022

Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

vanilla-rw-protonets-project Vanilla Prototypical Networks and PNs with Random Weights for image classification on Omniglot and mini-ImageNet. Made wi

8 Aug 31, 2022

The original weights of some Caffe models, ported to PyTorch.

Related tags

Overview

pytorch-caffe-models

Usage

Basic usage

Feature extraction

You might also like...

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

A python code to convert Keras pre-trained weights to Pytorch version

DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

Voice of Pajlada with model and weights.

High level network definitions with pre-trained weights in TensorFlow

A program that can analyze videos according to the weights you select

Inflated i3d network with inception backbone, weights transfered from tensorflow

Releases(models-2)

models-2(Jan 17, 2022)

Owner

Katherine Crowson

Learning Synthetic Environments and Reward Networks for Reinforcement Learning

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

Location-Sensitive Visual Recognition with Cross-IOU Loss

The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

HyperDict - Self linked dictionary in Python

PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

This repository contains part of the code used to make the images visible in the article "How does an AI Imagine the Universe?" published on Towards Data Science.

Dynamic Capacity Networks using Tensorflow

LegoDNN: a block-grained scaling tool for mobile vision systems

A non-linear, non-parametric Machine Learning method capable of modeling complex datasets

Code for CVPR 2018 paper --- Texture Mapping for 3D Reconstruction with RGB-D Sensor

This is the code for the paper "Contrastive Clustering" (AAAI 2021)

Self-Supervised Image Denoising via Iterative Data Refinement

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Disentangled Lifespan Face Synthesis

Training Very Deep Neural Networks Without Skip-Connections

Byzantine-robust decentralized learning via self-centered clipping

CPPE - 5 (Medical Personal Protective Equipment) is a new challenging object detection dataset