English image_classifier_vit_base_food101 ViTForImageClassification from nateraw

Description

Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.image_classifier_vit_base_food101 is a English model originally trained by nateraw.

Predicted Entities

grilled_cheese_sandwich, edamame, onion_rings, french_onion_soup, french_fries, creme_brulee, lobster_roll_sandwich, bruschetta, breakfast_burrito, caprese_salad, churros, omelette, club_sandwich, chocolate_mousse, nachos, bread_pudding, steak, hummus, panna_cotta, filet_mignon, sashimi, hot_and_sour_soup, cannoli, ravioli, samosa, grilled_salmon, lobster_bisque, seaweed_salad, macaroni_and_cheese, fish_and_chips, caesar_salad, dumplings, baby_back_ribs, fried_rice, oysters, peking_duck, guacamole, greek_salad, donuts, risotto, escargots, crab_cakes, waffles, carrot_cake, prime_rib, tuna_tartare, pho, chocolate_cake, bibimbap, fried_calamari, spaghetti_bolognese, gnocchi, chicken_quesadilla, frozen_yogurt, apple_pie, baklava, pulled_pork_sandwich, clam_chowder, eggs_benedict, lasagna, ceviche, paella, foie_gras, spring_rolls, falafel, miso_soup, pork_chop, ramen, pad_thai, garlic_bread, macarons, ice_cream, mussels, chicken_wings, pancakes, gyoza, poutine, croque_madame, pizza, cheese_plate, beignets, huevos_rancheros, french_toast, sushi, takoyaki, spaghetti_carbonara, beef_tartare, scallops, cup_cakes, tacos, deviled_eggs, beet_salad, tiramisu, cheesecake, strawberry_shortcake, beef_carpaccio, hamburger, red_velvet_cake, hot_dog, shrimp_and_grits, chicken_curry

Live Demo Open in Colab Download Copy S3 URI

How to use

image_assembler = ImageAssembler() \
    .setInputCol("image") \
    .setOutputCol("image_assembler")

imageClassifier = ViTForImageClassification \
    .pretrained("image_classifier_vit_base_food101", "en")\
    .setInputCols("image_assembler") \
    .setOutputCol("class")

pipeline = Pipeline(stages=[
    image_assembler,
    imageClassifier,
])

pipelineModel = pipeline.fit(imageDF)

pipelineDF = pipelineModel.transform(imageDF)

val imageAssembler = new ImageAssembler()
.setInputCol("image")
.setOutputCol("image_assembler")

val imageClassifier = ViTForImageClassification
.pretrained("image_classifier_vit_base_food101", "en")
.setInputCols("image_assembler")
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))

val pipelineModel = pipeline.fit(imageDF)

val pipelineDF = pipelineModel.transform(imageDF)

import nlu
import requests
response = requests.get('https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp/master/docs/assets/images/hen.JPEG')
with open('hen.JPEG', 'wb') as f:
    f.write(response.content)
nlu.load("en.classify_image.base_food101").predict("hen.JPEG")