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import gradio as gr | |
import tensorflow as tf | |
import tensorflow_hub as hub | |
ckpt_type = '1k' | |
tf_hub_url = 'gs://cloud-tpu-checkpoints/efficientnet/v2/hub/efficientnetv2-s/classification' | |
m = hub.KerasLayer(tf_hub_url, trainable=False) | |
m.build([None, 224, 224, 3]) # Batch input shape. | |
def get_imagenet_labels(filename): | |
labels = [] | |
with open(filename, 'r') as f: | |
for line in f: | |
labels.append(line.split(' ')[1][:-1]) # split and remove line break. | |
return labels | |
classes = get_imagenet_labels("imagenet1k_labels.txt") | |
def classify(image): | |
image = tf.keras.preprocessing.image.img_to_array(image) | |
image = (image - 128.) / 128. | |
logits = m(tf.expand_dims(image, 0), False) | |
pred = tf.keras.layers.Softmax()(logits) | |
idx = tf.argsort(logits[0])[::-1][0].numpy() | |
return classes[idx] | |
title = "Interactive demo: EfficientNetV2" | |
description = "Demo for Google's EfficientNetV2. EfficientNetV2 (accepted at ICML 2021) consists of convolutional neural networks that aim for fast training speed for relatively small-scale datasets, such as ImageNet1k." | |
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.00298'>EfficientNetV2: Smaller Models and Faster Training</a> | <a href='https://github.com/google/automl/tree/master/efficientnetv2'>Github Repo</a> | <a href='https://ai.googleblog.com/2021/09/toward-fast-and-accurate-neural.html'>Blog Post</a></p>" | |
iface = gr.Interface(fn=classify, | |
inputs=gr.inputs.Image(label="image"), | |
outputs='text', | |
title=title, | |
shape=(224,224), | |
description=description, | |
enable_queue=True, | |
examples=[['panda.jpeg'], ["llamas.jpeg"], ["hot_dog.png"]], | |
article=article) | |
iface.launch() | |