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Hong Jing (Jingles) jinglescode

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@jinglescode
jinglescode / unets-compare-unets-metrics.csv
Last active December 6, 2019 12:35
Binary cross-entropy Dice coefficient Intersection over Union # parameters Time per epoch
U-Net 0.3319 0.8367 0.8421 34.5 M 14s
UNet++ 0.2650 0.8104 0.8580 36.6 M 32s
Attention U-Net 0.2967 0.8247 0.8541 - 13s
@jinglescode
jinglescode / unet++-architecture.py
Created November 18, 2019 06:06
import torch
import torch.nn as nn
class conv_block_nested(nn.Module):
def __init__(self, in_ch, mid_ch, out_ch):
super(conv_block_nested, self).__init__()
self.activation = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True)
self.bn1 = nn.BatchNorm2d(mid_ch)
@jinglescode
jinglescode / moviepredict-modelearningsresults.csv
Last active November 15, 2019 12:08
Accuracy (best val. loss) Time taken per epoch
ResNet18 0.6809 8s
ResNet18 + Bag of Tricks 0.6968 (+2.3%) 35s
ResNet18 + Mish 0.7021 (+3.1%) 13s
ResNet18 + Bag of Tricks + Mish 0.6915 (+1.6%) 40s
Deep and Wide ResNet 0.6879 (+1.0%) 47s
ResNet18 + Data augmentation 0.7037 (+3.3%) 8s
@jinglescode
jinglescode / moviepredict-downloadimage.py
Last active November 15, 2019 05:37
import requests
import time
import re
import os
def download_poster(downloaded_image_dir, title, label, poster_path):
if not os.path.exists(downloaded_image_dir):
os.makedirs(downloaded_image_dir)
@jinglescode
jinglescode / moviepredict-sampledata.csv
Last active November 15, 2019 05:27
id title genre poster_path popularity budget revenue revenue_budget_ratio
862 toy-story animation /rhIRbceoE9lR4veEXuwCC2wARtG.jpg 21.946943 30000000.0 373554033.0 12.45
8844 jumanji adventure /vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg 17.015539 65000000.0 262797249.0 4.04
31357 waiting-to-exhale comedy /16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg 3.859495 16000000.0 81452156.0 5.09
949 heat action /zMyfPUelumio3tiDKPffaUpsQTD.jpg 17.924927 60000000.0 187436818.0 3.12
9091 sudden-death action /eoWvKD60lT95Ss1MYNgVExpo5iU.jpg 5.231580 35000000.0 64350171.0 1.84
@jinglescode
jinglescode / unet-architecture.py
Created November 7, 2019 10:40
import torch
import torch.nn as nn
class conv_block(nn.Module):
def __init__(self, in_ch, out_ch):
super(conv_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),
nn.BatchNorm2d(out_ch),
@jinglescode
jinglescode / seattle_airbnb_open_data-corr-minnightsrevenue-remove1000nights.csv
Created August 10, 2019 03:43
minimum_nights estimated_revenue
minimum_nights 1.000000 0.199189
estimated_revenue 0.199189 1.000000
@jinglescode
jinglescode / seattle_airbnb_open_data-pd_listings-toplistings-le4days.csv
Created August 10, 2019 03:38
listing_id number_of_reviews minimum_nights accommodates bedrooms beds estimated_revenued
2 3308979 20 4 11 5.0 7.0 78000.0
1592 9460 240 3 2 1.0 1.0 71280.0
3165 3040278 156 2 4 2.0 2.0 67704.0
3216 481220 164 2 4 1.0 3.0 63960.0
2922 699596 136 3 2 1.0 1.0 61200.0
@jinglescode
jinglescode / seattle_airbnb_open_data-minnights-countlistings.csv
Created August 10, 2019 03:31
minimum_nights '# of listings'
1 1610
2 1423
3 480
4 122
5 67
6 16
7 50
8 1
10 5
@jinglescode
jinglescode / seattle_airbnb_open_data-amenities.csv
Created July 10, 2019 06:06
amenities # of listings provide
Wireless Internet 3667.0
Heating 3627.0
Kitchen 3423.0
Smoke Detector 3281.0
Essentials 3237.0
Dryer 2997.0
Washer 2992.0
Internet 2811.0