charlesreid1

#!/bin/bash
# 
# Download the Large-scale CelebFaces Attributes (CelebA) Dataset
# from their Google Drive link.
# 
# CelebA: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
# 
# Google Drive: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8

python3 get_drive_file.py 0B7EVK8r0v71pZjFTYXZWM3FlRnM celebA.zip

lirnli

mehdidc

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

tarlen5

"""
author: Timothy C. Arlen
date: 28 Feb 2018

Calculate Mean Average Precision (mAP) for a set of bounding boxes corresponding to specific
image Ids. Usage:

> python calculate_mean_ap.py

Will display a plot of precision vs recall curves at 10 distinct IoU thresholds as well as output

madhawav

import cv2
import time

person_cascade = cv2.CascadeClassifier(
    os.path.join('/path/to/haarcascade_fullbody.xml'))
cap = cv2.VideoCapture("/path/to/test/video")
while True:
    r, frame = cap.read()
    if r:
        start_time = time.time()

ctmakro

# if input image is in range 0..1, please first multiply img by 255
# assume image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4
def imshow(img):
    import cv2
    import IPython
    _,ret = cv2.imencode('.jpg', img) 
    i = IPython.display.Image(data=ret)
    IPython.display.display(i)

hbredin

bfarzin

wangg12

import os
import torch
import argparse
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format


def removekey(d, listofkeys):
    r = dict(d)
    for key in listofkeys:

alper111

import torch
import torchvision

class VGGPerceptualLoss(torch.nn.Module):
    def __init__(self, resize=True):
        super(VGGPerceptualLoss, self).__init__()
        blocks = []
        blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())