I am new to ImageNet and would like to download full sized images of one of the subsets/synsets however I have found it incredibly difficult to actually find what subsets are available and where to find the ID code so I can download this.
All previous answers (from only 7 months ago) contain links which are now all invalid. Some seem to imply there is some sort of algorithm to making up an ID as it is linked to wordnet??
Essentially I would like a dataset of plastic or plastic waste or ideally marine debris. Any help on how to get the relevant ImageNet ID or suggestions on other datasets would be much much appreciated!!
I used this repo to achieve what you're looking for. Follow the following steps:
Create an account on Imagenet website
Once you get the permission, download the list of WordNet IDs for your task
Once you've the .txt file containing the WordNet IDs, you are all set to run main.py
As per your need, you can adjust the number of images per class
By default ImageNet images are automatically resized into 224x224. To remove that resizing, or implement other types of preprocessing, simply modify the code in line #40
Source: Refer this medium article for more details.
You can find all the 1000 classes of ImageNet here.
EDIT:
Above method doesn't work post March 2021. As per this update:
The new website is simpler; we removed tangential or outdated functions to focus on the core use case—enabling users to download the data, including the full ImageNet dataset and the ImageNet Large Scale Visual Recognition Challenge (ILSVRC).
So with this, to parse and search imagenet now you may have to use nltk.
More recently, the organizers hosted a Kaggle challenge based on the original dataset with additional labels for object detection. To download the dataset you need to register a Kaggle account and join this challenge. Please note that by doing so, you agree to abide by the competition rules.
Please be aware that this file is very large (168 GB) and the download will take anywhere from minutes to days depending on your network connection.
Install the Kaggle CLI and set up credentials as per this guideline.
pip install kaggle
Then run these:
kaggle competitions download -c imagenet-object-localization-challenge
unzip imagenet-object-localization-challenge.zip -d <YOUR_FOLDER>
Additionally to understand ImageNet hierarchy refer this.
Related
I'm still very new to the world of machine learning and am looking for some guidance for how to continue a project that I've been working on. Right now I'm trying to feed in the Food-101 dataset into the Image Classification algorithm in SageMaker and later deploy this trained model onto an AWS deeplens to have food detection capabilities. Unfortunately the dataset comes with only the raw image files organized in sub folders as well as a .h5 file (not sure if I can just directly feed this file type into sageMaker?). From what I've gathered neither of these are suitable ways to feed in this dataset into SageMaker and I was wondering if anyone could help point me in the right direction of how I might be able to prepare the dataset properly for SageMaker i.e convert to a .rec or something else. Apologies if the scope of this question is very broad I am still a beginner to all of this and I'm simply stuck and do not know how to proceed so any help you guys might be able to provide would be fantastic. Thanks!
if you want to use the built-in algo for image classification, you can either use Image format or RecordIO format, re: https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html#IC-inputoutput
Image format is straightforward: just build a manifest file with the list of images. This could be an easy solution for you, since you already have images organized in folders.
RecordIO requires that you build files with the 'im2rec' tool, re: https://mxnet.incubator.apache.org/versions/master/faq/recordio.html.
Once your data set is ready, you should be able to adapt the sample notebooks available at https://github.com/awslabs/amazon-sagemaker-examples/tree/master/introduction_to_amazon_algorithms
I am trying to make a Sankey chart (alluvial diagram) in Data Studio. I have found the "Community Visualizations" page and I can see the sankey diagram is one of the examples (https://developers.google.com/datastudio/visualization/). However, when I try to look in the bucket public-community-viz-showcase-reports, despite these supposedly being public it says I don't have the appropriate permissions to view them. I want to view the code used to generate the showcase report so I can modify it for my own purposes (I need to add color coding of the flows and multiple columns). Is it possible to do this?
Some of the files were uploaded to the Community Visualizations repository. The Sankey one was marked as experimental and deleted in this commit. The reason can be due to updating it to the new version but we aware of that if using it in production. Anyway, you can still browse through the repository history to find older files containing the original code. For example:
Sankey folder
index.js
Note that it also contains instructions on how to build the visualizations with the new changes you apply to the code.
By the way, even if you don't have storage.objects.list to run $ gsutil ls gs://public-community-viz-showcase-reports/sankey you do have storage.objects.get and can retrieve individual files of course. The problem in doing it that way is that files are minified to improve performance and not really readable.
As an example, an excerpt of index.js:
$ gsutil cat gs://public-community-viz-showcase-reports/sankey/index.js | head -c 500
is the following:
!function(t){var n={};function e(r){if(n[r])return n[r].exports;var i=n[r]={i:r,l:!1,exports:{}};return t[r].call(i.exports,i,i.exports,e),i.l=!0,i.exports}e.m=t,e.c=n,e.d=function(t,n,r){e.o(t,n)||Object.defineProperty(t,n,{enumerable:!0,get:r})},e.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},e.t=function(t,n){if(1&n&&(t=e(t)),8&n)return t;if(4&n&&"object"==typeof t&&t
The files for several of the example community visualizations now lives in the experimental-visualizations repository.
I am told that the following list of "puppy" image URL's are from imagenet.
https://github.com/asharov/cute-animal-detector/blob/master/data/puppy-urls.txt
How do I download another category for e.g. "cats"?
Where can I get the entire list of imagenet categories along with their explanation in csv?
Unfortunately, ImageNet is no longer as easily accessible as it previously was. You now have to create a free account, and then request access to the database using an email address that demonstrates your status as a non-commercial researcher. Following is an excerpt of the announcement posted on March 11, 2021 (does not specifically address the requirements to obtain an account and request access permission but explains some of their reasons for changing the website generally).
We are proud to see ImageNet's wide adoption going beyond what was originally envisioned. However, the decade-old website was burdened by growing download requests. To serve the community better, we have redesigned the website and upgraded its hardware. The new website is simpler; we removed tangential or outdated functions to focus on the core use case—enabling users to download the data, including the full ImageNet dataset and the ImageNet Large Scale Visual Recognition Challenge (ILSVRC).
ORIGINAL ANSWER (LINKS NO LONGER VALID):
You can interactively explore available synsets (categories) at http://www.image-net.org/explore, each synset page has a "Downloads" tab where you can download category image URLs.
Alternatively, you can use the ImageNet API. You can download image URLs for a particular synset using the synset id or wnid. The image URL download link below uses the wnid n02121808 for domestic cat, house cat, Felis domesticus, Felis catus.
http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n02121808
You can find the wnid for a particular synset using the explore link above (the id for a selected synset will be displayed in the browser address bar).
You can retrieve a list of all available synsets (by id) from:
http://www.image-net.org/api/text/imagenet.synset.obtain_synset_list.
You can retrieve the words associated with any synset id as follows (another cat example).
http://www.image-net.org/api/text/wordnet.synset.getwords?wnid=n02121808
or you can download smaller size of imagenet, mini-imagenet:
https://github.com/yaoyao-liu/mini-imagenet-tools
2-1. https://github.com/dragen1860/LearningToCompare-Pytorch/issues/4
2-2. https://github.com/twitter/meta-learning-lstm/tree/master/data/miniImagenet
You can easily use the python package MLclf to download and transform the mini-imagenet data for the traditional image classification task or the meta-learning task. just use:
pip install MLclf
You can also see for more details:
https://pypi.org/project/MLclf/
There is a in depth tutorial that leads you through building a dashboard. I can't get started because the first thing is to connect to a cloud resource that doesn't(?) exist.
Any help would be great.
You can find the files here.
Two notes about these files:
1) These are from June and might be outdated if you are looking at newer instructions.
2) To get around GitHub's size cap, I had to break a couple of .csv files up:
AU Sales.csv, which should have no effect on following along with the instructions.
bi_salesFact.csv, which will cause you to treat the US Sales data like the international data by importing it using the folder option instead of the file option
I'm helping a professor working on a satellite image analysis project, we need 800 images stitching together for a square area at 8000x8000 resolution each image from Google Map, it is possible to download them one by one, however I believe there must be a way to write a script for batch processing.
Here I would like to ask how can I implement this using shell or python script, and how could I download images by google maps url ?
Here is an example of the url:
https://maps.google.com.au/maps/myplaces?ll=-33.071009,149.554911&spn=0.027691,0.066047&ctz=-660&t=k&z=15
However I'm not able to analyse the image direct download link from this.
Update:
Actually, I solved this problem, however due to Google's intention, I would not post the way for doing this.
Have you tried the Google static maps API?
You get 25 000 free requests, but you're limited to 640x640, so you'll need to do ~160 requests at a higher zoom level.
I suggest downloading the images as so: Downloading a picture via urllib and python
URL to start with: http://maps.googleapis.com/maps/api/staticmap?center=-33.071009,149.554911&zoom=15&size=640x640&sensor=false&maptype=satellite
It's been long time since I solved the problem, sorry for the delay.
I posted my code to github here, plz star or fork as you like :)
The idea is to use a virtual web browser at a very high resolution to load the google map page, then do the page capture. The defect is there will be google symbol all around on each image, the solution is to apply over sampling on the resolution on each of the image, then use the stiching technique to stick them all together.