I’ve been fascinated by how transfer learning makes advanced computer vision accessible. Just last week, I saw a farmer struggling to identify crop diseases - that sparked this project. We’ll build a plant disease classifier using PyTorch, going from raw data to production deployment. Stick with me, and you’ll have practical skills you can apply to your own projects. Let’s get started.

First, ensure you have Python 3.8+ and a decent GPU. Create a virtual environment and install these packages:

pip install torch torchvision torchaudio pillow matplotlib pandas
pip install timm albumentations fastapi onnx onnxruntime

Transfer learning leverages patterns learned from massive datasets. Why train from scratch when we can build on existing knowledge? We’ll use EfficientNet as our foundation - it’s small but powerful. Here’s how we initialize our model:

import timm
import torch.nn as nn

def create_model(num_classes):
    model = timm.create_model('efficientnet_b0', pretrained=True, num_classes=0)
    classifier = nn.Sequential(
        nn.Linear(1280, 512),
        nn.ReLU(),
        nn.Dropout(0.3),
        nn.Linear(512, num_classes)
    )
    return nn.Sequential(model, classifier)

For our plant disease project, I collected 15,000 images across 10 disease categories from public datasets. The real magic happens in data preparation. How do we make our model robust to real-world variations? Through strategic augmentation:

from albumentations import *
train_transform = Compose([
    RandomResizedCrop(224, 224),
    HorizontalFlip(p=0.5),
    RandomBrightnessContrast(p=0.2),
    ShiftScaleRotate(rotate_limit=15),
    Normalize()
])

Customizing the training loop gives us control. Notice how we freeze layers initially then gradually unfreeze:

# Freeze backbone first
for param in model[0].parameters():
    param.requires_grad = False

# Train only classifier
optimizer = torch.optim.Adam(model[1].parameters(), lr=0.001)

# After 5 epochs, unfreeze later layers
for name, param in model[0].named_parameters():
    if 'blocks.6' in name or 'blocks.7' in name:
        param.requires_grad = True

What separates good models from great ones? Clever training techniques. I implement learning rate warmup and cosine annealing:

scheduler = torch.optim.lr_scheduler.OneCycleLR(
    optimizer, 
    max_lr=0.01,
    steps_per_epoch=len(train_loader),
    epochs=20
)

Evaluation goes beyond accuracy. We use confusion matrices and Grad-CAM for interpretation:

from pytorch_grad_cam import GradCAM
cam = GradCAM(model=model, target_layer=model[0].blocks[-1])
heatmap = cam(input_tensor)

Before deployment, we optimize. Quantization shrinks our model by 4x with minimal accuracy loss:

quantized_model = torch.quantization.quantize_dynamic(
    model, {nn.Linear}, dtype=torch.qint8
)
torch.onnx.export(quantized_model, dummy_input, "model_quant.onnx")

For deployment, FastAPI provides a robust web interface:

from fastapi import FastAPI, File
app = FastAPI()

@app.post("/predict")
async def predict(image: bytes = File(...)):
    img = preprocess_image(image)
    prediction = model(img)
    return {"class": CLASS_NAMES[prediction.argmax()]}

Wrap it in Docker for consistency across environments. Remember to log predictions and monitor performance drift. I set up automated alerts when accuracy drops below 95% of training performance.

This entire project took me three weeks from concept to deployment. The farmer I mentioned? He’s now testing our prototype. What problem could you solve with these techniques? Try modifying the code for your own dataset. If you found this helpful, share it with someone who’d benefit. I’d love to hear about your implementations in the comments!