Golang Sample | Python Sample | Node.js Sample |
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We're classifying images into NSFW and SFW categories.
Following are the image categories we classify into NSFW categories.
- Porn
- Explicit Nudity
- Animated Porn
- Suggestive Nudity
- Gore
You can query our pretrained model which should work for most use cases. If you want to build your own custom model tailored to your use case, skip ahead to the next section.
Few integration code samples are provided below.
- Get your API key by signing up on app.nanonets.com
curl --request POST --url 'https://app.nanonets.com/api/v2/ImageCategorization/LabelUrls/' --header 'accept: application/x-www-form-urlencoded' -d 'modelId=7390a500-9fe1-483b-8123-750b96fc660c&urls=https://goo.gl/ICoiHc' -u '-REPLCAE_YOUR_API_KEY:'
#REPLACE YOUR API KEY
import requests
url = 'https://app.nanonets.com/api/v2/ImageCategorization/LabelUrls/'
headers = {
'accept': 'application/x-www-form-urlencoded'
}
data = {'modelId': '7390a500-9fe1-483b-8123-750b96fc660c', 'urls' : ['https://goo.gl/ICoiHc']}
response = requests.request('POST', url, headers=headers, auth=requests.auth.HTTPBasicAuth('REPLACE_YOUR_API_KEY', ''), data=data)
print(response.text)
var request = require("request");
var options = { method: 'POST',
url: 'http://app.nanonets.com/api/v2/ImageCategorization/LabelUrls/',
headers:
{ 'cache-control': 'no-cache',
Authorization: 'Basic ' + new Buffer('REPLACE YOUR API KEY' + ":" + '').toString("base64"),
'Content-Type': 'application/x-www-form-urlencoded' },
form:
{ urls: 'https://goo.gl/ICoiHc',
modelId: '7390a500-9fe1-483b-8123-750b96fc660c' } };
request(options, function (error, response,body) {
if (error) throw new Error(error);
console.log(body);
});
git clone https://github.com/NanoNets/nsfw-api
cd nsfw-api
Get your free API Key from http://app.nanonets.com/#/keys
export NANONETS_API_KEY=YOUR_API_KEY_GOES_HERE
python ./code/create_model.py
_Note: This generates a MODEL_ID that you need for the next step
export NANONETS_MODEL_ID=YOUR_MODEL_ID
_Note: you will get YOUR_MODEL_ID from the previous step
The training data is found in data
python ./code/upload_training.py
Once the Images have been uploaded, begin training the Model
python ./code/train_model.py
The model takes ~2 hours to train. You will get an email once the model is trained. In the meanwhile you check the state of the model
python ./code/model_state.py
Once the model is trained. You can make predictions using the model
python ./code/prediction.py PATH_TO_YOUR_IMAGE.jpg
Sample Usage:
python ./code/prediction.py ./data/nsfw/2795.jpg