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sapbot/
resnet20-cifar10-272k

Name: resnet20-cifar10-272k
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resnet20-cifar10-272k

Model description

This model is a ResNet20 trained from scratch on the CIFAR-10 dataset. The architecture follows the original CIFAR‑ResNet design (3×3 convolutions, basic residual blocks, 272474 parameters). Training was performed on a consumer AMD Radeon RX 6600 GPU using PyTorch with ROCm support.

Author: sapbot from Romarchive

Intended uses

Image classification on 10 everyday object categories.
Educational purposes – shows that small models can achieve competitive accuracy without large‑scale pre‑training.

Training procedure

Optimizer: SGD with momentum 0.9 and weight decay 1e‑4.
Learning rate: 0.1, dropped by factor 0.1 at epochs 80 and 120 (MultiStepLR).
Batch size: 128.
Epochs: up to 160 (early stopping with patience 10).
Data augmentation: random horizontal flip, random crop with 4‑pixel padding.
Input normalization: mean (0.4914, 0.4822, 0.4465), std (0.2023, 0.1994, 0.2010).

Evaluation results

Metric	Value
Test accuracy	86.29%
Test loss	0.4451
Full CIFAR-10 accuracy	90.17%

Per‑class performance

Class	Precision	Recall	F1-score	Support
0 (airplane)	0.7959	0.9320	0.8586	1000
1 (automobile)	0.9023	0.9510	0.9260	1000
2 (bird)	0.8176	0.8250	0.8213	1000
3 (cat)	0.7883	0.7000	0.7415	1000
4 (deer)	0.8010	0.9180	0.8555	1000
5 (dog)	0.8423	0.7850	0.8126	1000
6 (frog)	0.9310	0.8630	0.8957	1000
7 (horse)	0.9201	0.8750	0.8970	1000
8 (ship)	0.9622	0.8650	0.9110	1000
9 (truck)	0.8944	0.9150	0.9046	1000
accuracy			0.8629	10000
macro avg	0.8655	0.8629	0.8624	10000
weighted avg	0.8655	0.8629	0.8624	10000

Confusion matrix (row = true label, col = predicted label)

True \ Pred	Pred 0	Pred 1	Pred 2	Pred 3	Pred 4	Pred 5	Pred 6	Pred 7	Pred 8	Pred 9
True 0	932	6	18	6	3	1	0	3	14	17
True 1	14	951	0	1	0	0	1	0	2	31
True 2	51	2	825	27	44	15	16	13	3	4
True 3	30	6	45	700	54	102	29	17	5	12
True 4	10	2	23	18	918	4	12	11	1	1
True 5	14	3	31	82	47	785	4	25	4	5
True 6	16	2	53	35	17	7	863	5	1	1
True 7	16	3	10	9	62	16	1	875	0	8
True 8	71	18	4	7	1	2	1	2	865	29
True 9	17	61	0	3	0	0	0	0	4	915

Model size

272,474 trainable parameters.

How to use

python

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image

# --------------------------------------------------
# ResNet20 for CIFAR-10 (exact same architecture)
# --------------------------------------------------
def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)

class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 16
        self.conv1 = conv3x3(3, 16)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64 * block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

def ResNet20():
    return ResNet(BasicBlock, [3, 3, 3])

# --------------------------------------------------
# Load the model weights
# --------------------------------------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet20().to(device)
model.load_state_dict(torch.load("best_resnet20_cifar10.pth", map_location=device))
model.eval()

# --------------------------------------------------
# Preprocess an image (must be 32x32 RGB)
# --------------------------------------------------
transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])

def predict(image_path):
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        output = model(input_tensor)
        _, predicted = output.max(1)
    classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
    return classes[predicted.item()]

# Example usage:
# print(predict("my_cat.jpg"))

Acknowledgements

Original CIFAR‑10 dataset by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
PyTorch team for the framework and ROCm support.

sapbot/resnet20-cifar10-272k