fujicv 1.0.0

Open-source Python package for image classification and regression built on timm + torchvision

FujiCV

Open-source Python package for image classification and regression, built on timm + torchvision.

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Features

• Backbone factory — instantly load any timm or torchvision model with a single call; classifier head auto-stripped, output dimension auto-detected.
• Task heads — ClassificationHead, RegressionHead, MultiLabelHead.
• Custom layers — LinearBNDropout, GeM pooling, AttentionPool, SqueezeExcite.
• ModelBuilder — assemble backbone + custom layers + head; dummy forward pass validates shapes at build time.
• Albumentations pipelines — light / medium / heavy augmentation presets, ImageNet normalisation, deterministic val transforms.
• CSVImageDataset — reads any CSV-driven image dataset; pre-validates files, skips missing with a warning; supports classification, regression, multilabel.
• Loss functions — 13 losses across classification, regression, and multilabel tasks, all registered in LOSS_REGISTRY.
• Metrics — 16 metrics across all tasks, registered in METRIC_REGISTRY.
• Trainer — AMP, gradient clipping, early stopping, best/last checkpointing, history CSV.
• WandbLogger — W&B integration via env-var only (WANDB_API_KEY); graceful no-op when W&B is absent.
• Evaluation — confusion matrix, ROC/PR curves, t-SNE, Grad-CAM (CNN), attention rollout (ViT).
• Inference — Predictor.from_checkpoint for single image and batch inference.
• ONNX export — export and numeric verification.

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Installation

# Core (CPU)
pip install fujicv

# With W&B logging
pip install "fujicv[wandb]"

# With ONNX export
pip install "fujicv[onnx]"

# Dev / testing
pip install "fujicv[dev]"

PyTorch is not included as a transitive dependency on PyPI. Install it separately following the official instructions to pick the right CUDA version.

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Quick Start

1. Classification

from fujicv.models.builder import ModelBuilder
from fujicv.losses import get_loss
from fujicv.metrics import get_metric
from fujicv.engine.trainer import Trainer
from fujicv.data import build_splits, build_dataloaders
from fujicv.utils import load_config, set_seed

set_seed(42)
cfg = load_config("examples/configs/classification_example.yaml")

train_df, val_df, test_df = build_splits(cfg["dataset"])
train_loader, val_loader, _ = build_dataloaders(
    train_df, val_df, test_df, cfg["dataset"], cfg["augmentation"]
)

model = ModelBuilder(
    backbone_name="resnet50",
    task="classification",
    num_outputs=10,
    pretrained=True,
).build()

import torch.optim as optim

trainer = Trainer(
    model=model,
    train_loader=train_loader,
    val_loader=val_loader,
    loss_fn=get_loss("LabelSmoothingCE", {"smoothing": 0.1}),
    metrics={"Accuracy": get_metric("Accuracy"), "F1": get_metric("F1")},
    optimizer=optim.AdamW(model.parameters(), lr=3e-4),
    epochs=30,
    task="classification",
    output_dir="outputs/",
)
history = trainer.train()

2. Using the example scripts

# Train
python examples/train.py --config examples/configs/classification_example.yaml

# Evaluate (with attention maps)
python examples/evaluate.py \
    --config examples/configs/classification_example.yaml \
    --checkpoint outputs/classification/best.pt \
    --with-attention-maps

3. Inference

from fujicv.inference import Predictor
from fujicv.models.builder import ModelBuilder

model_skeleton = ModelBuilder(
    backbone_name="resnet50", task="classification", num_outputs=10, pretrained=False
).build()

predictor = Predictor.from_checkpoint("outputs/classification/best.pt", model=model_skeleton)
label, confidence = predictor.predict("path/to/image.jpg")
print(f"Predicted: {label}  ({confidence:.1%})")

4. ONNX Export

from fujicv.export import to_onnx, verify_onnx

to_onnx(model, "model.onnx")
verify_onnx(model, "model.onnx")

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Package Layout

fujicv/
  models/       backbone factory, heads, custom layers, ModelBuilder
  data/         CSVImageDataset, transforms, dataloader factory
  losses/       13 loss functions + registry
  metrics/      16 metric callables + registry
  engine/       Trainer, WandbLogger, callbacks
  eval/         plots, report, ROC/PR curves, t-SNE, attention maps
  utils/        Registry, set_seed, config loader
  inference/    Predictor
  export/       ONNX export & verification
tests/
examples/
  configs/      YAML configs for each task type
  train.py
  evaluate.py

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Supported Tasks

Task	Loss examples	Metric examples
Classification / Multiclass	CrossEntropyLoss, FocalLoss, LabelSmoothingCE	Accuracy, F1, AUROC
Regression	MSELoss, HuberLoss, QuantileLoss	MAE, RMSE, R2Score
Multi-label	BCEWithLogitsLoss, AsymmetricLoss, FocalBCELoss	HammingLoss, mAP, PerLabelAUROC

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Security

See SECURITY.md for the full policy. Key points:

• No hardcoded credentials anywhere in the codebase.
• W&B API key read from WANDB_API_KEY environment variable only.
• Loss/metric functions are pure tensor operations with no network calls.

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Contributing

1. Fork the repository and create a feature branch.
2. Install in editable mode: pip install -e ".[dev]"
3. Run the checks locally:

   ruff check fujicv/ tests/
   mypy fujicv/
   pytest tests/
   detect-secrets scan
   

4. Open a pull request — CI will run automatically.

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License

MIT — Copyright (c) 2024 FujiCV Contributors.