vGiacomov/image-classifier-beans

ResNet18 Fine-tuned on Beans Dataset

This model was trained in Google Colab using a T4 GPU and tracked with MLflow.

Model Details

Dataset: Beans

Classes:

• —Healthy
• —Bean Rust
• —Angular Leaf Spot

Validation Accuracy: 0.9398

Training Configuration

Overfitting Prevention Techniques:

• —Data augmentation (rotation, flip, crop, color jitter)
• —Dropout (30%)
• —L2 regularization (weight decay: 1e-4)
• —Learning rate scheduling (ReduceLROnPlateau)
• —Best model selection based on validation accuracy

Hyperparameters:

• —Learning Rate: 5e-05
• —Epochs: 10
• —Batch Size: 32
• —Weight Decay: 0.0001
• —Dropout: 0.3
• —Optimizer: Adam

Artifacts

• —resnet18_beans.pth - PyTorch model weights
• —per_class_metrics.csv - Detailed per-class metrics
• —confusion_matrix.png - Confusion matrix visualization

Usage

Download and load the model:

from huggingfacehub import hfhub_download import torch from torchvision import models from torch import nn

modelpath = hfhubdownload( repoid="vGiacomov/image-classifier-beans", filename="resnet18_beans.pth" )

model = models.resnet18() model.fc = nn.Sequential( nn.Dropout(0.3), nn.Linear(model.fc.infeatures, 3) ) model.loadstatedict(torch.load(modelpath, map_location="cpu")) model.eval()

Apache 2.0