CIFAR-10 ResNet18 Classification Model
This model is a ResNet-18 trained on the CIFAR-10 subset dataset.
Model Details
- Model Type: ResNet-18
- Dataset: CIFAR-10 Subset
- Classes: 10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
- Best Validation Accuracy: 80.80%
- Framework: PyTorch
Training Details
- Epochs: 10
- Batch Size: 64
- Learning Rate: 0.001
- Optimizer: Adam
Usage
import torch
from torchvision import models
from huggingface_hub import hf_hub_download
# Load model
model = models.resnet18(pretrained=False)
model.fc = torch.nn.Linear(model.fc.in_features, 10)
# Download weights
model_path = hf_hub_download(repo_id="priyadip/cifar10-resnet18-m25csa023", filename="best_model.pth")
checkpoint = torch.load(model_path, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
Student Information
- Roll No: M25CSA021
- Exam: ML-DLOps Minor Exam - Part 2