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 The new website is simpler; we removed tangential or outdated functions to focus on the core use case—enabling users to download the data, including the full ImageNet dataset and the ImageNet Large Scale Visual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ORIGINAL ANSWER (LINKS NO LONGER VALID):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/
Related
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.
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
Is there a way to extend Wagtail's documents to show file previews? There's a service ( I have not tested ), which looks cool, but the free plan to Pro plan is a huge leap in cost. I am hoping someone has figured this out already and can point me to the solution. Thank you.
FilePreviews.io's 100 documents a month on the free plan seems pretty generous to me. You could try to build something similar, e.g. using ImageMagick to create PDF thumbnails:
http://duncanlock.net/blog/2013/11/18/how-to-create-thumbnails-for-pdfs-with-imagemagick-on-linux/
and use a service like Aspose to convert common file formats to PDFs:
https://www.aspose.com
Or you could do it manually, by adding a thumbnail field to your document model, and telling users to provide their own thumbnails.
But if you or your client are uploading more than 100 documents a month, and you want reliable thumbnail generation for multiple document types, $49 a month may be better value than working it all out yourself.
at the bottom of:
https://developer.pearson.com/apis/ft-education-api/#!/ftarticles/listArticles_get_0
i am trying to get a list of articles using the set parameters.
i have registered for, and received an API key.
the issue is that i am unable to generate a list of articles, say on '2014-05-01'
note: financial times set up the API to allow students to get plain text articles 30 days after the original article was published.
done extremely small amount of research and understand i may need to use python to receive the actual content...have python installed (familiar with absolute basics)...trying to get nothing more than plain text documents of articles.
thanks.
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.