AIが作った画像か判別するAIを4hで作ったら割と精度出たのでWebサービスとして公開した件

from autogluon.vision import ImagePredictor, ImageDataset

if __name__ == "__main__":
    # \imagesの中にはAIが生成した画像フォルダとAIが作ってない画像フォルダを用意
	dataset = ImageDataset.from_folder(r'C:\Users\admin\ml\novel\images')
    # 学習用やテスト用のデータに分割
	train, val, test = dataset.random_split(val_size=0.1, test_size=0.1)
    
    # 何時間も学習させたくないので緩めの設定で
	hyperparameters = {'batch_size': 16, 'early_stop_patience': 15}
	time_limit = 60 * 60
	predictor = ImagePredictor()
	predictor.fit(train, time_limit=time_limit, presets='medium_quality_faster_train', hyperparameters=hyperparameters)
    # テストデータでどれくらい良かったか出力する
	test_acc = predictor.evaluate(test)
	print('Top-1 test acc: %.3f' % test_acc['top1'])

    # 結果を保存する
	filename = 'my.ag'
	predictor.save(filename)

# モデルロード
filename = "my.ag"
predictor_loaded = ImagePredictor.load(filename)

#データセット読み込み分割
dataset = ImageDataset.from_folder(r'C:\Users\admin\ml\novel\images')
train, val, test = dataset.random_split(val_size=0.1, test_size=0.1)

#予想させる
result = predictor_loaded.predict(test)

# tfとか計算する@合ってるか知らんけど
tp=0
tn=0
tLen=0
fp=0
fn=0
fLen=0
for key, val in test['label'].iteritems():
    if val == 0:
        if val == result[key]:
            tp+=1
        else:
            fn+=1
        tLen+=1
    else:
        if val == result[key]:
            tn+=1
        else:
            fp+=1
        fLen+=1
print("TP:",tp/tLen)
print("FN:",fn/fLen)
print("FP:",fp/fLen)
print("TN:",tn/tLen)
print("Accuracy:",(tp+tn)/(tp+tn+fp+fn))
precision = (tp)/(tp+fp)
recall = tp/(tp+fn)
f1 = 2 * precision * recall / (precision + recall)
print("Precision:",precision)
print("Recall:",recall)
print("F-value:",f1)

TP: 0.9808917197452229
FN: 0.025423728813559324
FP: 0.01694915254237288
TN: 0.7388535031847133
Accuracy: 0.9818181818181818
Precision: 0.9871794871794872
Recall: 0.9808917197452229
F-value: 0.9840255591054313

from autogluon.vision import ImagePredictor
from pathlib import Path

from fastapi import FastAPI

labels = sorted([dir.name for dir in Path(r'C:\Users\admin\ml\novel\images').iterdir() if dir.is_dir()])
print(labels)
filename = "my.ag"
predictor_loaded = ImagePredictor.load(filename)

app = FastAPI()


@app.get("/")
def read_root(file_name: str = ""):
	if file_name == "":
		return {"Error": "file name not found"}
	return check(file_name)


def check(file: str):
	result = predictor_loaded.predict_proba(file)
	label = labels[result.idxmax(axis=1)[0]]
	proba = result.max(axis=1)[0]
	print(f'結果: {label} (確信度={proba})')

	if label == 'AI':
		return {"IsAI": True, "Percent": proba}
	else:
		return {"IsAI": False, "Percent": proba}