Auto Image Classification

First, install gradsflow

pip install git+https://github.com/gradsflow/gradsflow@main

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import os
import sys
import warnings
from pathlib import Path

warnings.filterwarnings("ignore")

os.chdir("../../")

import os import sys import warnings from pathlib import Path warnings.filterwarnings("ignore") os.chdir("../../")

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import ray
from flash.core.data.utils import download_data
from gradsflow import AutoImageClassifier
from gradsflow.data.image import image_dataset_from_directory

import ray from flash.core.data.utils import download_data from gradsflow import AutoImageClassifier from gradsflow.data.image import image_dataset_from_directory

Let's use Hymenoptera dataset provided by Flash which contain images of Ants and Bees for creating Image Classication Model.

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data_dir = "/Users/aniket/personal/gradsflow/gradsflow/data/"  # replace with your filepath
# download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", data_dir)

data_dir = "/Users/aniket/personal/gradsflow/gradsflow/data/" # replace with your filepath # download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", data_dir)

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train_data = image_dataset_from_directory(f"{data_dir}/hymenoptera_data/train/", transform=True)
train_dl = train_data["dl"]

val_data = image_dataset_from_directory(f"{data_dir}/hymenoptera_data/val/", transform=True)
val_dl = val_data["dl"]

train_data = image_dataset_from_directory(f"{data_dir}/hymenoptera_data/train/", transform=True) train_dl = train_data["dl"] val_data = image_dataset_from_directory(f"{data_dir}/hymenoptera_data/val/", transform=True) val_dl = val_data["dl"]

If you want to run Gradsflow on a remote server then first setup ray cluster and initialize ray with the remote address.

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# ray.init(address="REMOTE_IP_ADDR")
# ray.init(local_mode=True)

# ray.init(address="REMOTE_IP_ADDR") # ray.init(local_mode=True)

To train an image classifier create an object of AutoImageClassifier and provide number of trials and timeout.

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model = AutoImageClassifier(
    train_dataloader=train_dl,
    val_dataloader=val_dl,
    num_classes=2,
    n_trials=1,
    optimization_metric="val_accuracy",
    timeout=50,
)

model = AutoImageClassifier( train_dataloader=train_dl, val_dataloader=val_dl, num_classes=2, n_trials=1, optimization_metric="val_accuracy", timeout=50, )

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print("AutoImageClassifier initialised!")
model.hp_tune()
print("completed!")

print("AutoImageClassifier initialised!") model.hp_tune() print("completed!")

AutoImageClassifier initialised!

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ray.shutdown()

ray.shutdown()

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Last update: September 29, 2021

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