Cassava Leaf Disease Classifier (EfficientNet-B0) — v2

Fine-tuned EfficientNet-B0 classifying cassava leaf photos into 5 categories, trained on the Cassava Leaf Disease Classification dataset (~21,400 real field images from Uganda, Makerere AI Lab).

First step toward offline, edge-deployable computer vision for physical-world problems in Africa.

Classes

0: Cassava Bacterial Blight (CBB)
1: Cassava Brown Streak Disease (CBSD)
2: Cassava Green Mottle (CGM)
3: Cassava Mosaic Disease (CMD)
4: Healthy

Results (held-out 20% stratified validation)

Accuracy: 0.800
Macro F1: 0.684
Best class: Mosaic (F1 0.911). Hardest: Bacterial Blight (F1 0.531).

Training

Base: EfficientNet-B0 (ImageNet pretrained, timm)
224x224, ImageNet normalization; augmentation: flips, rotation, color jitter
Class-weighted CrossEntropy (inverse-frequency) for heavy imbalance (~61% Mosaic)
AdamW, lr 1e-4, CosineAnnealing over 12 epochs, best-epoch by macro-F1
Hardware: Kaggle Tesla T4

Known limitation (important)

The model sometimes classifies mild or early-stage diseased leaves as Healthy — the highest-stakes error for real deployment, since it misses infections. This is worst for early Bacterial Blight. Faint disease signal on mostly-green leaves is the model's core remaining weakness and is not fully solved by this version.

Usage

import timm, torch
model = timm.create_model("efficientnet_b0", pretrained=False, num_classes=5)
model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu"))
model.eval()

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