initial commit

definition.py

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+import torch.nn as nn
+
+class FlowersImagesDetectionModel(nn.Module):
+    def __init__(self, num_classes):
+        super(FlowersImagesDetectionModel, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(128 * 28 * 28, 512)  # Adjust the input size according to your image size after resizing
+        self.fc2 = nn.Linear(512, num_classes)
+        self.relu = nn.ReLU()
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = self.pool(self.relu(self.conv3(x)))
+        x = x.view(-1, 128 * 28 * 28)  # Adjust this according to the output size of the convolutional layers
+        x = self.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2945bcacceb16ec477c7f9f127ddb1131150583f7abba6af412bada17e83dcf6
+size 206109068

train.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from definition import FlowersImagesDetectionModel
+from torch.utils.data import DataLoader
+from datasets import load_dataset
+from torchvision.transforms import ToTensor, Resize
+from torch.utils.data.dataset import TensorDataset
+
+flowerTypesNumber = 102
+
+model = FlowersImagesDetectionModel(flowerTypesNumber)
+
+# Funzioni di ottimizzazione e di perdita
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+criterion = nn.CrossEntropyLoss()
+
+# Caricamento del dataset
+originalDataset = load_dataset("nelorth/oxford-flowers", split="train")
+
+tensorImages = []
+tensorLabels = []
+
+# Trasforma le immagini in tensori PyTorch e ridimensionale
+for imageData, label in zip(originalDataset['image'], originalDataset['label']):
+    tensorImage = ToTensor()(Resize((224, 224))(imageData))  # Ridimensiona le immagini
+    tensorImages.append(tensorImage)
+    tensorLabels.append(label)
+
+# Trasforma le liste di tensori in un singolo tensore
+imagesTensor = torch.stack(tensorImages)
+labelsTensor = torch.tensor(tensorLabels)
+
+# Crea un dataset
+dataset = TensorDataset(imagesTensor, labelsTensor)
+
+# Crea un DataLoader
+dataLoader = DataLoader(dataset, batch_size=64, shuffle=True)
+
+# Addestramento
+model.train()
+for epoch in range(2):
+    running_loss = 0.0
+
+    for i, (inputs, labels) in enumerate(dataLoader, 0):
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item()
+
+        if i % 100 == 99:
+            print('[%d, %5d] loss: %.3f' %
+                  (epoch + 1, i + 1, running_loss / 100))
+            running_loss = 0.0
+
+torch.save(model.state_dict(), 'pytorch_model.bin')