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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- pytorch |
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- resnet |
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- beans |
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- agriculture |
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- plant-disease |
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datasets: |
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- beans |
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library_name: pytorch |
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pipeline_tag: image-classification |
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metrics: |
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- accuracy |
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--- |
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# ResNet18 Fine-tuned on Beans Dataset |
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This model was trained in Google Colab using a T4 GPU and tracked with MLflow. |
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## Model Details |
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**Dataset:** [Beans](https://huggingface.co/datasets/AI-Lab-Makerere/beans) |
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**Classes:** |
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- Healthy |
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- Bean Rust |
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- Angular Leaf Spot |
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**Validation Accuracy:** 0.9398 |
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## Training Configuration |
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**Overfitting Prevention Techniques:** |
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- Data augmentation (rotation, flip, crop, color jitter) |
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- Dropout (30%) |
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- L2 regularization (weight decay: 1e-4) |
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- Learning rate scheduling (ReduceLROnPlateau) |
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- Best model selection based on validation accuracy |
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**Hyperparameters:** |
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- Learning Rate: 5e-05 |
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- Epochs: 10 |
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- Batch Size: 32 |
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- Weight Decay: 0.0001 |
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- Dropout: 0.3 |
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- Optimizer: Adam |
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## Artifacts |
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- `resnet18_beans.pth` - PyTorch model weights |
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- `per_class_metrics.csv` - Detailed per-class metrics |
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- `confusion_matrix.png` - Confusion matrix visualization |
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## Usage |
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Download and load the model: |
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from huggingface_hub import hf_hub_download |
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import torch |
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from torchvision import models |
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from torch import nn |
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model_path = hf_hub_download( |
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repo_id="vGiacomov/image-classifier-beans", |
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filename="resnet18_beans.pth" |
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) |
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model = models.resnet18() |
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model.fc = nn.Sequential( |
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nn.Dropout(0.3), |
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nn.Linear(model.fc.in_features, 3) |
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) |
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model.load_state_dict(torch.load(model_path, map_location="cpu")) |
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model.eval() |
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Apache 2.0 |
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