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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/1.jpg filter=lfs diff=lfs merge=lfs -text
+examples/2.jpg filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from model import resnet18  # Ensure this matches your model definition file
+
+# Load CIFAR-100 class names
+with open("cifar100_classes.txt") as f:
+    CIFAR100_CLASSES = [line.strip() for line in f.readlines()]
+
+# Load trained model
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = resnet18(num_classes=100)
+checkpoint=torch.load("resnet18_cifar100_best.pth", map_location=DEVICE)
+model.load_state_dict(checkpoint["model_state_dict"])
+
+model.eval()
+model.to(DEVICE)
+
+# Define preprocessing
+transform = T.Compose([
+    T.Resize((32, 32)),
+    T.ToTensor(),
+    T.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
+])
+
+def predict(image):
+    img = Image.fromarray(image).convert("RGB")
+    img = transform(img).unsqueeze(0).to(DEVICE)
+    with torch.no_grad():
+        outputs = model(img)
+        probs = torch.softmax(outputs, dim=1)
+        conf, pred = torch.max(probs, dim=1)
+        class_name = CIFAR100_CLASSES[pred.item()]
+        confidence = conf.item()   # Normalize to 0-100%
+    return {f"{class_name}": round(confidence, 2)}
+
+# Gradio UI
+title = "CIFAR-100 Image Classifier"
+description = "Upload an image (32x32 or larger). The model will predict the top class with confidence score."
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Label(num_top_classes=1, label="Prediction"),
+    title=title,
+    description=description,
+    examples=[["examples/1.jpg"], ["examples/2.jpg"]],
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()

cifar100_classes.txt

@@ -0,0 +1,100 @@
+apple
+aquarium_fish
+baby
+bear
+beaver
+bed
+bee
+beetle
+bicycle
+bottle
+bowl
+boy
+bridge
+bus
+butterfly
+camel
+can
+castle
+caterpillar
+cattle
+chair
+chimpanzee
+clock
+cloud
+cockroach
+couch
+crab
+crocodile
+cup
+dinosaur
+dolphin
+elephant
+flatfish
+forest
+fox
+girl
+hamster
+house
+kangaroo
+keyboard
+lamp
+lawn_mower
+leopard
+lion
+lizard
+lobster
+man
+maple_tree
+motorcycle
+mountain
+mouse
+mushroom
+oak_tree
+orange
+orchid
+otter
+palm_tree
+pear
+pickup_truck
+pine_tree
+plain
+plate
+poppy
+porcupine
+possum
+rabbit
+raccoon
+ray
+road
+rocket
+rose
+sea
+seal
+shark
+shrew
+skunk
+skyscraper
+snail
+snake
+spider
+squirrel
+streetcar
+sunflower
+sweet_pepper
+table
+tank
+telephone
+television
+tiger
+tractor
+train
+trout
+tulip
+turtle
+wardrobe
+whale
+willow_tree
+wolf
+woman
+worm

model.py

@@ -0,0 +1,151 @@
+import torch
+import torch.nn as nn
+from typing import Type, Union, List, Optional
+
+
+class BasicBlock(nn.Module):
+    expansion: int = 1
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        stride: int = 1,
+        downsample: Optional[nn.Module] = None,
+    ) -> None:
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(out_channels, out_channels,
+                               kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        identity = x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.downsample is not None:
+            identity = self.downsample(x)
+        out += identity
+        out = self.relu(out)
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion: int = 4
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        stride: int = 1,
+        downsample: Optional[nn.Module] = None,
+    ) -> None:
+        super().__init__()
+        width = out_channels
+        self.conv1 = nn.Conv2d(
+            in_channels, width, kernel_size=1, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = nn.Conv2d(width, width, kernel_size=3,
+                               stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(
+            width, out_channels * self.expansion, kernel_size=1, stride=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        identity = x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+        out = self.conv3(out)
+        out = self.bn3(out)
+        if self.downsample is not None:
+            identity = self.downsample(x)
+        out += identity
+        out = self.relu(out)
+        return out
+
+
+class ResNet(nn.Module):
+    def __init__(
+        self,
+        block: Type[Union[BasicBlock, Bottleneck]],
+        layers: List[int],
+        num_classes: int = 100,
+    ) -> None:
+        super().__init__()
+        self.in_channels = 64
+        # Modified for CIFAR-100 (32x32 images)
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+    def _make_layer(
+        self,
+        block: Type[Union[BasicBlock, Bottleneck]],
+        out_channels: int,
+        blocks: int,
+        stride: int = 1,
+    ) -> nn.Sequential:
+        downsample = None
+        if stride != 1 or self.in_channels != out_channels * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.in_channels, out_channels * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_channels * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(self.in_channels, out_channels, stride, downsample))
+        self.in_channels = out_channels * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.in_channels, out_channels))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x)
+        return x
+
+
+def resnet18(num_classes: int = 100) -> ResNet:
+    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)
+
+
+def resnet34(num_classes: int = 100) -> ResNet:
+    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)
+
+
+def resnet50(num_classes: int = 100) -> ResNet:
+    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)

requirements.txt

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+gradio
+torch
+torchvision
+Pillow

resnet18_cifar100_best.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:23c3eac02b21c620cdfeeafff569534c716f9c9c7c61984ec5220c81475f4edd
+size 89862129