Vbai-1.1 Dementia (11178564 parametre)
"Vbai-1.1 Dementia" modeli, bir önceki modele göre daha fazla veriyle eğitilmiş olup üzerinde ince ayar yapılmış versiyonudur.
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Vbai-1.1 Dementia (11178564 parameters)
The "Vbai-1.1 Dementia" model is a fine-tuned version of the previous model, trained with more data.
Kullanım / Usage
import torch
import torch.nn as nn
from torchvision import transforms, models
from PIL import Image
import matplotlib.pyplot as plt
import os
from torchsummary import summary
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.resnet18(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 4)
model.load_state_dict(torch.load('Vbai-1.1 Dementia/path'))
model = model.to(device)
model.eval()
summary(model, (3, 224, 224))
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
class_names = ['No Dementia', 'Mild Dementia', 'Avarage Dementia', 'Very Mild Dementia']
def predict(image_path, model, transform):
image = Image.open(image_path).convert('RGB')
image = transform(image).unsqueeze(0).to(device)
model.eval()
with torch.no_grad():
outputs = model(image)
probs = torch.nn.functional.softmax(outputs, dim=1)
_, preds = torch.max(outputs, 1)
return preds.item(), probs[0][preds.item()].item()
def show_image_with_prediction(image_path, prediction, confidence, class_names):
image = Image.open(image_path)
plt.imshow(image)
plt.title(f"Prediction: {class_names[prediction]} (%{confidence * 100:.2f})")
plt.axis('off')
plt.show()
test_image_path = 'image-path'
prediction, confidence = predict(test_image_path, model, transform)
print(f'Prediction: {class_names[prediction]} (%{confidence * 100})')
show_image_with_prediction(test_image_path, prediction, confidence, class_names)
Uygulama / As App
import sys
import torch
import torch.nn as nn
from torchvision import transforms, models
from PIL import Image
import matplotlib.pyplot as plt
from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QLabel, QFileDialog, QVBoxLayout, QMessageBox
from PyQt5.QtGui import QPixmap, QIcon
from PyQt5.QtCore import Qt
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
class_names = ['No Dementia', 'Mild Dementia', 'Avarage Dementia', 'Very Mild Dementia']
class DementiaApp(QWidget):
def __init__(self):
super().__init__()
self.initUI()
self.model = None
self.image_path = None
def initUI(self):
self.setWindowTitle('Prediction App by Neurazum')
self.setWindowIcon(QIcon('C:/Users/eyupi/PycharmProjects/Neurazum/NeurAI/Assets/neurazumicon.ico'))
self.setGeometry(2500, 300, 400, 200)
self.loadModelButton = QPushButton('Upload Model', self)
self.loadModelButton.clicked.connect(self.loadModel)
self.loadImageButton = QPushButton('Upload Image', self)
self.loadImageButton.clicked.connect(self.loadImage)
self.predictButton = QPushButton('Make a Prediction', self)
self.predictButton.clicked.connect(self.predict)
self.predictButton.setEnabled(False)
self.resultLabel = QLabel('', self)
self.resultLabel.setAlignment(Qt.AlignCenter)
self.imageLabel = QLabel('', self)
self.imageLabel.setAlignment(Qt.AlignCenter)
layout = QVBoxLayout()
layout.addWidget(self.loadModelButton)
layout.addWidget(self.loadImageButton)
layout.addWidget(self.imageLabel)
layout.addWidget(self.predictButton)
layout.addWidget(self.resultLabel)
self.setLayout(layout)
def loadModel(self):
options = QFileDialog.Options()
fileName, _ = QFileDialog.getOpenFileName(self, "Choose Model Path", "",
"PyTorch Model Files (*.pt);;All Files (*)", options=options)
if fileName:
self.model = models.resnet18(pretrained=False)
num_ftrs = self.model.fc.in_features
self.model.fc = nn.Linear(num_ftrs, 4)
self.model.load_state_dict(torch.load(fileName, map_location=device))
self.model = self.model.to(device)
self.model.eval()
self.predictButton.setEnabled(True)
QMessageBox.information(self, "Model Uploaded", "Model successfully uploaded!")
def loadImage(self):
options = QFileDialog.Options()
fileName, _ = QFileDialog.getOpenFileName(self, "Choose Image File", "",
"Image Files (*.jpg *.jpeg *.png);;All Files (*)", options=options)
if fileName:
self.image_path = fileName
pixmap = QPixmap(self.image_path)
self.imageLabel.setPixmap(pixmap.scaled(224, 224, Qt.KeepAspectRatio))
def predict(self):
if self.model and self.image_path:
prediction, confidence = self.predictImage(self.image_path, self.model, transform)
self.resultLabel.setText(f'Prediction: {class_names[prediction]} (%{confidence * 100:.2f})')
else:
QMessageBox.warning(self, "Missing Information", "Model and picture must be uploaded.")
def predictImage(self, image_path, model, transform):
image = Image.open(image_path).convert('RGB')
image = transform(image).unsqueeze(0).to(device)
model.eval()
with torch.no_grad():
outputs = model(image)
probs = torch.nn.functional.softmax(outputs, dim=1)
_, preds = torch.max(outputs, 1)
return preds.item(), probs[0][preds.item()].item()
if __name__ == '__main__':
app = QApplication(sys.argv)
ex = DementiaApp()
ex.show()
sys.exit(app.exec_())
Python Sürümü / Python Version
3.9 <=> 3.13
Modüller / Modules
matplotlib==3.8.0
Pillow==10.0.1
torch==2.3.1
torchsummary==1.5.1
torchvision==0.18.1
