Object Detection with JAX/Equinox on VOC 2007

This project is a challenge to implement an object detection pipeline for the PASCAL VOC 2007 dataset using JAX and Equinox. You'll be adapting key components from the YOLOv10 model (originally in PyTorch) to a JAX-based framework.

This project uses JAX, a high-performance numerical computation library, and Equinox, a library for building neural networks in JAX.

Primary Goal: train an object detection model on the VOC 2007 dataset by implementing the prediction head and loss function inspired by YOLOv10 within the provided JAX/Equinox boilerplate.

Table of Contents

1. Prerequisites
2. Project Structure
3. Understanding the VOC 2007 Dataset
4. Your Task: Implementing Object Detection
   • The Challenge: Porting YOLOv10 Components
   • Key Files to Modify
   • Guidance on Porting
5. Crash Course: JAX and Equinox for PyTorch Users
   • Core Principles
   • PRNG Keys: Explicit Randomness
   • jax.jit: Just-In-Time Compilation
   • jax.vmap: Automatic Vectorization (Batching)
   • Equinox Modules: eqx.Module
   • Gradients: jax.grad & jax.value_and_grad
   • PyTrees
   • Immutability and Updates
6. Data Loading and Preprocessing
7. Configuration
8. Evaluation (for Object Detection)
9. Important Considerations & Debugging Tips
10. Resources

Prerequisites

• Basic understanding of machine learning and deep learning concepts.
• Experience with Python.
• Familiarity with other DL frameworks like PyTorch is highly beneficial,
• No prior JAX/Equinox experience is strictly necessary,
• Access to a machine with a GPU/TPU is recommended for faster training, though not strictly required.

Project Structure

The project is organized as follows:

.
├── hackathon/
│   ├── init.py
│   ├── augmentations.py       # Image augmentation functions (TensorFlow based)
│   ├── classification.py      # WORKING EXAMPLE: Image classification task
│   ├── config.py              # Configuration loading
│   ├── dataloader.py          # TensorFlow Datasets (TFDS) loading logic
│   ├── metrics.py             # (Potentially) Metrics like Dice coefficient
│   ├── objectdetection.py     # BOILERPLATE: Your primary workspace for object detection
│   ├── preprocessing.py       # Image preprocessing functions (TensorFlow based)
│   ├── tasks.py               # Task-specific processing function dispatcher
│   └── utils.py               # Utility functions
├── classification.py          # Main script to run training/evaluation for a classification task
├── objectdetection.py         # The main script to update to run the object detection pipeline
├── .config                    # Example configuration file (rename from .config.example if provided)
└── README.md                  # This file

• hackathon/classification.py: This file contains a fully working example of an image classification task.
• hackathon/objectdetection.py: This is where you'll spend most of your time. It contains boilerplate code for an object detection task, with several NotImplementedError sections that you need to fill in.
• augmentations.py & preprocessing.py: These files use TensorFlow for image operations. Note that the data pipeline (loading, preprocessing, augmentation) is TensorFlow-based, and the resulting NumPy arrays are then fed into JAX for model training.
• config.py: Manages project configurations. See the .config file for available options.

The base neural network used for feature extraction is a custom Convolutional Neural Network (CNN). Its definition is another open source project, Equimo, a project implementing multiple computer vision models using Equinox.

Understanding the VOC 2007 Dataset

The PASCAL VOC 2007 dataset is a standard benchmark for object detection. Key characteristics:

• Images: Contains thousands of color images of various scenes.
• Object Classes: 20 object classes (e.g., person, car, cat, dog, chair).
• Annotations: Each image is annotated with:
  • Bounding boxes for every object instance.
  • The class label for each bounding box.
• Challenges:
  • Multiple objects per image.
  • Objects of varying sizes and scales.
  • Occlusion and truncation of objects.

Your model will need to predict the class and bounding box coordinates (typically (x_{min}, y_{min}, x_{max}, y_{max})) for each object in an image.

The Task: Implementing Object Detection

Your primary task is to complete the hackathon/objectdetection.py script. This involves:

1. Understanding the Data:

   • Familiarize yourself with how bounding boxes and labels are provided by the dataloader.py for the VOC dataset. The preprocessing function in objectdetection.py receives sample[objects_key]["bbox"] and sample[objects_key]["label"]. You'll need to process these appropriately.
   • The augmentations.py file contains a create_global_crops function that has a NotImplementedError section for handling bounding boxes during augmentations. You will need to implement this to ensure bounding boxes are correctly transformed along with the images. The random_resized_crop function already has bbox transformation logic you can adapt.

2. Implementing the YOLOv10-inspired Prediction Head:

   • In objectdetection.py, the loss_fn calls model(...). This model (your custom CNN + prediction head) will output predictions. You need to design and implement a prediction head that takes features from the base CNN and outputs predictions in a format suitable for object detection following the Yolov10 network.
   • Refer to the YOLOv10 architecture for inspiration on how the prediction head is structured. You'll need to implement these layers using Equinox modules (eqx.nn.Conv2d, eqx.nn.Linear, etc.). You can check out the Equimo repository to have many examples of advanced types of layers.

3. Implementing the YOLOv10-inspired Loss Function:

   • The loss_fn in objectdetection.py currently has a NotImplementedError. You need to implement the loss calculation. This will likely involve several components:
     • Classification Loss: For the class of detected objects.
     • Regression Loss: For the bounding box coordinates.
     • Objectness Loss: To determine if an anchor/grid cell contains an object.
   • You'll need to match ground truth bounding boxes with predicted boxes to calculate these losses. This is a critical part of object detection.

4. Completing preprocessing and augmentations:

   • The preprocessing function in objectdetection.py needs to be completed to handle bounding box data alongside images. This might involve converting bounding box formats or preparing them for augmentation.
   • The augmentations function in objectdetection.py also needs completion. Crucially, the create_global_crops call within it needs to correctly handle bounding box transformations. The bboxes argument to create_global_crops (in augmentations.py) is where you'll pass your bounding box data, and you'll need to implement the logic within create_global_crops to transform these bboxes according to the image augmentations.

5. Implementing Evaluation:

   • The evaluation loop in evaluate within objectdetection.py has a NotImplementedError. You'll need to implement a suitable evaluation metric for object detection, such as mean Average Precision (mAP). This will involve processing model predictions on the validation set and comparing them against ground truth.

The Challenge: Porting YOLOv10 Components

You are not expected to port the entire YOLOv10 model. The focus is on the prediction heads and the loss computation.

• YOLOv10 Repository: https://github.com/THU-MIG/yolov10
• Study the PyTorch implementation of the heads and loss functions in the YOLOv10 repository. Your goal is to translate the logic and mathematical operations into JAX/Equinox.

Key Files to Modify

• hackathon/objectdetection.py:
  • preprocessing(): Implement logic to handle image and object (bounding box, label) data.
  • augmentations(): Ensure bounding boxes are correctly passed to and processed by create_global_crops.
  • loss_fn(): Implement the object detection loss.
  • evaluate(): Implement the evaluation loop and mAP calculation.
  • You may also need to define new Equinox modules for the prediction head(s) here or in a separate file.
• hackathon/augmentations.py:
  • create_global_crops(): Implement the bounding box transformation logic when bboxes are provided.

Guidance on Porting

• Start Simple: Begin by understanding the structure of a single detection head layer.
• Equinox Equivalents:
  • PyTorch nn.Conv2d -> eqx.nn.Conv2d
  • PyTorch nn.Linear -> eqx.nn.Linear
  • Activation functions (ReLU, Sigmoid, etc.) are available in jax.nn.
• Shape Management: Pay very close attention to tensor shapes at each step. JAX is explicit about this, i.e., it will not explicitely broadcast shapes like PyTorch.
• Loss Components: Break down the YOLO loss into its constituent parts and implement them one by one.

Crash Course: JAX and Equinox for PyTorch Users

JAX and Equinox have some fundamental differences from PyTorch. Understanding these is crucial.

Core Principles

• Functional Programming: JAX encourages a functional programming style. Functions should ideally be pure (no side effects; output depends only on input).
• Immutability: JAX arrays (and Equinox model parameters) are immutable. Operations that "modify" an array actually return a new array.

PRNG Keys: Explicit Randomness

Unlike PyTorch's global random seed, JAX requires explicit handling of pseudo-random number generator (PRNG) keys.

• Create a key: key = jax.random.PRNGKey(seed)
• Split keys for different operations: key, subkey = jax.random.split(key)
• Pass keys to functions that require randomness (e.g., initializers, dropout, sampling).
• Why? This makes random operations reproducible and easier to reason about, especially with transformations like jax.vmap and jax.pmap.

Example:

import jax
import jax.random as jr

key = jr.PRNGKey(0)
key_dropout, key_init, key_data_aug = jr.split(key, 3)

# Use key_dropout for a dropout layer
# Use key_init for weight initialization
# Use key_data_aug for a random data augmentation

In our codebase, you'll see key being passed around, especially in train_step and compute_cls_accuracy.

jax.jit: Just-In-Time Compilation

JAX can compile your Python functions into highly optimized XLA (Accelerated Linear Algebra) code using jax.jit.

• Usage: Decorate your function with @jax.jit (or @eqx.filter_jit for Equinox modules).
• Benefits: Significant speedups, especially for numerical computations.
• Gotchas:
  • The function is traced on its first call with specific input shapes and types. Subsequent calls with different shapes/types will trigger a re-compilation.
  • Control flow (if/else, loops) based on tensor values can be tricky. Try to make control flow depend on static values or use JAX control flow primitives like jax.lax.cond or jax.lax.scan.
  • Side effects (like printing or modifying external variables) inside JITted functions behave unexpectedly or are ignored. Use jax.debug.print for debugging.

jax.vmap: Automatic Vectorization (Batching)

jax.vmap is a powerful transformation that maps a function designed for single data points to work over batches of data.

• PyTorch: Batching is often implicit in layers (e.g., nn.Conv2d expects a batch dimension).
• JAX/Equinox: You typically write functions/modules for a single data sample. jax.vmap then handles the batching.
  • jax.vmap(model)(batch_of_images)
  • in_axes argument specifies which arguments to map over (e.g., in_axes=(0, None) means map over the first dimension of the first argument, and broadcast the second argument).

Example from classification.py:

# model is defined to process a single image
logits = jax.vmap(model, in_axes=(0, None))(images, key)
# Here, `images` is a batch of images (e.g., shape [B, H, W, C])
# `key` is a single PRNG key, broadcasted for each image in the batch.
# `model` is applied to each image in `images`.

You must use jax.vmap (or ensure your model inherently handles batches, which is less common in Equinox) when processing batches of data.

Equinox Modules: eqx.Module

Equinox provides a simple way to create neural network modules, similar to torch.nn.Module.

• Structure: Parameters (weights, biases) are attributes of the class.
• eqx.filter: Equinox modules are "PyTrees." eqx.filter helps separate learnable parameters from static configuration or non-learnable components.
  • params, static = eqx.filter(model, eqx.is_array) separates array attributes (learnable parameters) from everything else.
  • model = eqx.combine(params, static) reconstructs the model. This is crucial for optimizers, which only need to update the learnable parameters.

Example from classification.py:

# In train_step:
model = eqx.combine(params, static) # Reconstruct model
# ...
loss, grads = jax.value_and_grad(loss_for_step)(params) # Get grads only for params
# params = optax.apply_updates(params, updates) # Update only params

Gradients: jax.grad & jax.value_and_grad

JAX provides functions for automatic differentiation.

• jax.grad(fun): Returns a function that computes the gradient of fun.
• jax.value_and_grad(fun): Returns a function that computes both the value of fun and its gradient. This is often more efficient.

The loss_fn in the provided code is designed to take params (the learnable parts of the model) as its first argument, which is what jax.grad or jax.value_and_grad will differentiate with respect to.

PyTrees

JAX and Equinox extensively use the concept of "PyTrees." A PyTree is a container of leaf elements, like lists, tuples, and dictionaries. JAX transformations (jit, vmap, grad) can operate on entire PyTrees. Equinox modules are PyTrees. This allows you to handle complex nested structures of parameters and data seamlessly.

Immutability and Updates

Since JAX arrays are immutable, you don't update them in-place.

# PyTorch (in-place)
params[0] += update_val

# JAX (functional update)
params = params.at[0].add(update_val)
# Or, for entire arrays, often just:
params = new_params

Optax optimizers handle this correctly when applying updates: params = optax.apply_updates(params, updates).

Data Loading and Preprocessing

• hackathon/dataloader.py: Uses tensorflow_datasets (TFDS) to load data.
  • The load_tfds function handles dataset loading, preprocessing, augmentation, batching, and conversion to NumPy arrays.
• hackathon/preprocessing.py: Contains TensorFlow-based image preprocessing functions like prepare_image (resizing) and normalize.
• hackathon/augmentations.py: Contains TensorFlow-based image augmentation functions like random_resized_crop, random_horizontal_flip, color_jitter, etc.
  • Crucially for object detection: The random_resized_crop function has logic to transform bounding boxes. You will need to ensure this logic is correctly used and potentially adapted when you implement bounding box handling in create_global_crops and the augmentations function in objectdetection.py.
• hackathon/tasks.py: The get_processing_functions utility dynamically selects the correct preprocessing, augmentations, and postprocessing functions based on the dataset and task type.

The pipeline is: TFDS -> TensorFlow preprocessing/augmentations -> NumPy arrays -> JAX model.

Configuration

Project configurations are managed by hackathon/config.py using the everett library, which reads from environment variables or a .config file.

An example .config file might look like:

seed=42
# Object Detection - VOC
num_classes_voc=20 # Number of classes in VOC
image_size_voc=416 # Example input size for YOLO-like models
batch_size_voc=16
learning_rate_voc=1e-4
warmup_steps_voc=100
num_epochs_voc=50
# ... other VOC specific params ...
# image params
patch_size=16 # If your base CNN is ViT-like
normalization_mean=0.485,0.456,0.406
normalization_std=0.229,0.224,0.225
global_crops_size=224
# ...

Make sure your .config file has the necessary parameters for voc (e.g., num_classes_voc, image_size_voc, batch_size_voc_scratch, etc.). The provided config.py lists all expected configurations.

Evaluation (for Object Detection)

For object detection, a common evaluation metric is mean Average Precision (mAP).

• You will need to implement mAP calculation in the evaluate function of objectdetection.py.
• This involves:
  1. Getting predictions from your model on the validation set (bounding boxes, class scores, objectness scores).
  2. Applying Non-Maximum Suppression (NMS) to filter redundant boxes.
  3. Comparing predicted boxes to ground truth boxes using Intersection over Union (IoU).
  4. Calculating precision and recall for each class at various confidence thresholds.
  5. Computing the Average Precision (AP) for each class (area under the precision-recall curve).
  6. Averaging APs across all classes to get mAP.

This is a non-trivial task, so plan your time accordingly. You might find existing JAX implementations of mAP or its components helpful as a reference.

Important Considerations & Debugging Tips

• Start with classification.py: Understand it thoroughly. It's your reference for JAX/Equinox patterns.
• Incremental Development: Implement and test small pieces at a time.
  • Can you get the data shapes right for the head?
  • Can you compute one component of the loss?
• Debugging JITted Code:
  • jax.debug.print("message: {x}", x=array): Prints values inside JITted functions. It does not print during tracing, only during execution.
  • jax.debug.breakpoint(): If you run your code under a Python debugger.
  • Temporarily disable @jax.jit or @eqx.filter_jit for easier debugging with standard Python print statements, but remember performance will be much slower.
• Shape Mismatches: This will be a common source of errors. Print shapes frequently!
  • print(array.shape) (outside JIT)
  • jax.debug.print("array shape: {s}", s=array.shape) (inside JIT)
• PRNG Key Management: Ensure keys are split and threaded through your functions correctly. Reusing keys will lead to correlated "random" numbers.
• Bounding Box Formats: Be consistent with your bounding box coordinate format (e.g., [xmin, ymin, xmax, ymax] vs. [x_center, y_center, width, height], normalized vs. absolute). VOC typically uses [ymin, xmin, ymax, xmax] (absolute). TFDS might provide them normalized. Check dataloader.py and preprocessing.py.
• Numerical Stability: Watch out for NaNs in your loss. This can be due to log(0), division by zero, or large gradients. Use jax.numpy.clip or add small epsilon values (1e-7 or jnp.finfo(dtype).eps) where appropriate.
• TensorFlow vs. JAX operations: Remember that the data loading pipeline uses TensorFlow ops. Your model and loss function will use JAX ops (jax.numpy which is often aliased as jnp).

Resources

• JAX Documentation: https://jax.readthedocs.io/
• Equinox Documentation: https://docs.kidger.site/equinox/
• Optax (Optimizer library) Documentation: https://optax.readthedocs.io/
• YOLOv10 Paper & Repository: (Linked above)
• PASCAL VOC Dataset: http://host.robots.ox.ac.uk/pascal/VOC/
• TFDS VOC2007: https://www.tensorflow.org/datasets/catalog/voc#voc2007 (Details on how TFDS structures the data)