##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| English short name lower case | Alpha-2 code | Alpha-3 code | Numeric code | ISO 3166-2 | |
|---|---|---|---|---|---|
| Afghanistan | AF | AFG | 004 | ISO 3166-2:AF | |
| Åland Islands | AX | ALA | 248 | ISO 3166-2:AX | |
| Albania | AL | ALB | 008 | ISO 3166-2:AL | |
| Algeria | DZ | DZA | 012 | ISO 3166-2:DZ | |
| American Samoa | AS | ASM | 016 | ISO 3166-2:AS | |
| Andorra | AD | AND | 020 | ISO 3166-2:AD | |
| Angola | AO | AGO | 024 | ISO 3166-2:AO | |
| Anguilla | AI | AIA | 660 | ISO 3166-2:AI | |
| Antarctica | AQ | ATA | 010 | ISO 3166-2:AQ |
| Vivek Gupta Sep 2nd, 2020 at 10:02 AM | |
| I am new to sparknlp. I am writing a custom transformer which will remove tokens from text whose length is <=2. Transformer is working and doing its job. But it is not giving proper structure as an output. Instead it is returning only Array of String. I am struggling to get output in following structure - | |
| ArrayType( | |
| StructType([ | |
| StructField("annotatorType", StringType(), False), | |
| StructField("begin", IntegerType(), False), | |
| StructField("end", IntegerType(), False), | |
| StructField("result", StringType(), False), | |
| StructField("metadata", MapType(StringType(), StringType()), True) | |
| ]) |
| from transformers import ViTFeatureExtractor, ViTForImageClassification | |
| from PIL import Image | |
| import requests | |
| url = 'http://images.cocodataset.org/val2017/000000039769.jpg' | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') | |
| model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') |
| from transformers import ViTFeatureExtractor, ViTForImageClassification | |
| from transformers import pipeline | |
| feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') | |
| model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
| pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor, device=-1) |
| from transformers import pipeline | |
| pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor, device=-1) | |
| for batch_size in [1, 8, 32, 64, 128]: | |
| print("-" * 30) | |
| print(f"Streaming batch_size={batch_size}") | |
| for out in tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)): | |
| pass |
| from transformers import ViTFeatureExtractor, ViTForImageClassification | |
| from transformers import pipeline | |
| import torch | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| print(device) | |
| feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') | |
| model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
| model = model.to(device) |
| from sparknlp.annotator import * | |
| from sparknlp.base import * | |
| from pyspark.ml import Pipeline | |
| imageAssembler = ImageAssembler() \ | |
| .setInputCol("image") \ | |
| .setOutputCol("image_assembler") | |
| imageClassifier = ViTForImageClassification \ |
| [ | |
| { | |
| "tourBlurb" : "Big Sur is big country. The Big Sur Retreat takes you to the most majestic part of the Pacific Coast and show you the secret trails.", | |
| "tourName" : "Big Sur Retreat", | |
| "tourPackage" : "Backpack Cal", | |
| "tourBullets" : "\"Accommodations at the historic Big Sur River Inn, Privately guided hikes through any of the 5 surrounding national parks, Picnic lunches prepared by the River Inn kitchen, Complimentary country breakfast, Admission to the Henry Miller Library and the Point Reyes Lighthouse \"", | |
| "tourRegion" : "Central Coast", | |
| "tourDifficulty" : "Medium", | |
| "tourLength" : 3, | |
| "tourPrice" : 750, |
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