Object Localization with PyTorch and EfficientNet-B0

A deep learning project for single-object localization: given an image, the model predicts a tight bounding box around the target object using supervised regression on bounding-box coordinates.

This implementation follows the object-localization workflow from the notebook and is centered on:

• a custom PyTorch dataset for image + bounding-box pairs,
• Albumentations-based geometric augmentation with bounding-box awareness,
• a pretrained timm backbone,
• a training loop that optimizes bounding-box regression with mean squared error,
• validation-based checkpointing and inference visualizations.

Object localization asks the model to identify where the object is in an image rather than classify what the image contains. Instead of returning a class label, the network regresses four coordinates describing a bounding box:

• xmin
• ymin
• xmax
• ymax

The training data in this project consists of images paired with bounding-box annotations in Pascal VOC format. The dataset contains object categories such as eggplant, cucumber, and mushroom, with image dimensions stored alongside the coordinates.

The pipeline begins by reading the CSV annotation file and visualizing raw image/bounding-box pairs to verify the annotation format. The data is then split into training and validation subsets using an 80/20 split. Next, the images are resized to a fixed resolution and augmented with random flips and rotation while preserving bounding-box consistency.

A custom torch.utils.data.Dataset implementation loads each image, applies synchronized augmentations to both the image and its bounding box, converts the result to a tensor, and returns the pair to the dataloader. The model uses a pretrained EfficientNet-B0 backbone from timm and is configured to output four values corresponding to the bounding-box coordinates. Training minimizes MSE between predicted and ground-truth boxes, and the best checkpoint is saved whenever validation loss improves.

For inference, the saved checkpoint is restored and predictions are compared against ground truth using visualization utilities.

• Analyze the object localization dataset and bounding-box annotation format
• Develop a custom PyTorch dataset for image and bounding-box regression
• Apply Albumentations for geometry-aware data augmentation with bounding-box support
• Utilize a pretrained convolutional neural network from timm for regression
• Implement training and evaluation pipelines for bounding-box prediction
• Perform inference and visualize predicted bounding boxes against ground truth

The notebook uses a dataset cloned from a GitHub repository and read from a CSV file containing:

• img_path
• xmin, ymin, xmax, ymax
• width, height
• label

The notebook preview shows 186 annotated samples. After the train/validation split, the dataset contains:

• 148 training examples
• 38 validation examples

Each annotation represents one object instance per image. The bounding boxes are stored in absolute pixel coordinates.

Before training, the notebook visualizes sample annotations directly on top of the original RGB images using OpenCV and Matplotlib. This step confirms that:

• the CSV coordinates align with the image content,
• the bounding box format is correct,
• the target object occupies a meaningful portion of the image,
• the localization task is suitable for bounding-box regression.

This inspection is repeated on both original and augmented samples to verify that transformations preserve label consistency.

Albumentations is used with bbox_params so that image transforms and bounding boxes remain synchronized.

• Resize to 140 × 140
• Random horizontal flip (p=0.5)
• Random vertical flip (p=0.5)
• Random rotation

• Resize to 140 × 140

Bounding boxes are declared in pascal_voc format and the augmentation pipeline is configured with label_fields=['class_labels'] so that the library can transform the coordinates correctly.

The ObjLocDataset class extends torch.utils.data.Dataset and encapsulates the full sample-loading logic.

1. Read a row from the annotation dataframe.
2. Extract the four bounding-box coordinates.
3. Load the corresponding image from disk.
4. Convert the image from BGR to RGB.
5. Apply the augmentation pipeline, if provided.
6. Convert the image to a float tensor with channel-first layout.
7. Convert the bounding box to a tensor.

Each dataset item returns:

• an image tensor of shape [3, 140, 140]
• a bounding-box tensor of shape [4]

The implementation keeps the dataset focused and reusable across both training and validation pipelines.

The dataset is wrapped by standard PyTorch dataloaders.

Configuration used in the notebook:

• batch size: 16
• training loader: shuffled
• validation loader: sequential

The first batch in the notebook has shape:

• images: [16, 3, 140, 140]
• bounding boxes: [16, 4]

This matches the expected input/output contract for the regression model.

The model is built with timm.create_model using EfficientNet-B0 as a pretrained backbone.

• pretrained=True to leverage ImageNet features
• num_classes=4 so the model outputs four regression values
• a minimal forward pass that returns:
  • predictions only during inference
  • predictions plus loss during training when ground-truth boxes are available

The model uses Mean Squared Error (MSE) between the predicted and target bounding-box coordinates. This makes the task a direct regression problem rather than a detection pipeline with anchors, objectness scores, or class logits.

Two dedicated functions handle the optimization workflow.

• switches the model to training mode
• iterates over training batches
• moves images and targets to the selected device
• computes predictions and MSE loss
• performs backpropagation
• updates the optimizer
• returns the average training loss

• switches the model to evaluation mode
• disables gradient computation
• computes validation loss across the validation dataloader
• returns the average validation loss

The notebook uses Adam with learning rate 0.001.

The best-performing model is saved to best_model.pt whenever validation loss improves. This keeps the strongest checkpoint from the full training run.

The notebook uses the following core hyperparameters:

• image size: 140
• batch size: 16
• learning rate: 0.001
• epochs: 40
• backbone: efficientnet_b0
• device: cuda
• output dimensions: 4

This setup balances a lightweight input resolution with a strong pretrained backbone for coordinate regression.

1. Load annotations from the CSV file and inspect the bounding-box schema.
2. Visualize sample images with boxes overlaid to validate the data.
3. Split the dataset into training and validation subsets.
4. Define augmentations that resize images and transform boxes consistently.
5. Implement a custom dataset that returns image tensors and bounding-box tensors.
6. Create dataloaders for batched training and evaluation.
7. Build a pretrained regression model on top of EfficientNet-B0.
8. Define training and validation loops with MSE loss.
9. Train for multiple epochs while saving the best checkpoint by validation loss.
10. Restore the best model and run inference on validation images.
11. Compare predicted boxes against ground truth visually.

For inference, the notebook:

• loads the saved checkpoint from best_model.pt,
• switches the model to evaluation mode,
• selects a validation image,
• runs a forward pass to predict the bounding box,
• visualizes predicted and ground-truth boxes for comparison.

The notebook repeats this inference step on multiple validation samples to show how the model generalizes across examples.

During the recorded training run, validation loss steadily decreased early in training and the best checkpoint was achieved at epoch 38 with a validation loss of approximately 33.71. The final epoch still remained in the same loss range, which indicates that the model had learned a stable regression mapping for bounding-box prediction.

• Python
• PyTorch
• timm
• Albumentations
• OpenCV
• NumPy
• Pandas
• Matplotlib
• scikit-learn
• tqdm

• Bounding boxes are handled in absolute pixel coordinates and converted through Albumentations using Pascal VOC semantics.
• The dataset pipeline is designed for a single bounding box per image.
• The forward pass is intentionally simple to keep the focus on localization rather than full object detection.
• Visual verification is an important part of the workflow because bounding-box regression is sensitive to incorrect coordinate handling.