CarQualityClassifier

Classifiers used in MeshFleet to generate the MeshFleet Dataset. The Dataset and its generation is described in MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling.

MeshFleet Dataset Implementation and Car Quality Classification

🤗 Huggingface Dataset: MeshFleet

This repository contains the implementation for the generation of the MeshFleet dataset, a curated collection of 3D car models derived from Objaverse-XL, using a car quality classification pipeline.

Dataset Overview:

The MeshFleet dataset provides metadata for 3D car models, including their SHA256 from Objaverse-XL, vehicle category, and size. The core dataset is available as a CSV file: meshfleet_with_vehicle_categories_df.csv. You can easily load it using pandas:

import pandas as pd

meshfleet_df = pd.read_csv('./data/meshfleet_with_vehicle_categories_df.csv')
print(meshfleet_df.head())

The actual 3D models can be downloaded from Objaverse-XL using their corresponding SHA256 hashes. Pre-rendered images of the MeshFleet models are also available within the Hugging Face repository in the renders directory, organized as renders/{sha256}/00X.png.

Project Structure and Functionality:

This repository provides code for the following:

1. Data Preprocessing: Preparing the labeled objects (subset from Objaverse) for quality classification.
2. Car Quality Classifier Training: Training a model to predict the quality of 3D car models based on rendered images.
3. Objaverse-XL Processing: Downloading, rendering, and classifying objects from the larger Objaverse-XL dataset.
4. Classification of Rendered Objects: Applying the trained classifier to new renderings.

Please Note: You don't have to clone or install the repository if you just want to use the extracted data. You can download the preprocessed data from the Hugging Face repository: https://huggingface.co/datasets/DamianBoborzi/MeshFleet

Data Preprocessing

The preprocessing pipeline involves several key steps:

1. Loading Labels: Load the car_quality_dataset_votes.csv file from CarQualityDataset. This file contains quality labels (votes) for a subset of Objaverse objects, which serves as our training data.
2. Loading/Generating Renderings: Obtain rendered images of the labeled objects. You can either use the pre-rendered images from the same CarQualityDataset repository or generate your own using the Objaverse-XL library.
3. Embedding Generation: Create image embeddings using DINOv2 and SigLIP models. These embeddings capture visual features crucial for quality assessment. We provide a notebook (scripts/objaverse_generate_sequence_embeddings.ipynb) to guide you through this process. Pre-generated embeddings will also be made available for download.
4. Classifier Training We use the generated embeddings and labels to train a classifier to predict the quality of 3D car models. The training script is sequence_classifier_training.py.
5. Objaverse-XL Processing: You can load, render, and classify 3D car models from the larger Objaverse-XL dataset, using the oxl_processing/objaverse_xl_batched_renderer.py script

Installation

Before you begin, install the necessary dependencies:

pip install -r requirements.txt
pip install .

Classifier Training

This section details the steps to train the car quality classifier.

1. Load and Render Labeled Objects: Begin by loading the labeled objects from data/car_quality_dataset_votes.csv. Render the objects(if you haven't already downloaded pre-rendered images.
2. Generate Embeddings: Use the scripts/objaverse_generate_sequence_embeddings.ipynb notebook to generate DINOv2 and SigLIP embeddings for the rendered images. Place the generated embeddings in the data directory. We will also provide a download link for pre-computed embeddings soon.
3. Train the Classifier: Train a classifier using the quality_classifier/sequence_classifier_training.py script. This script takes the embeddings as input and trains a model to predict object quality. Make sure to adjust the paths within the script if you're using your own generated embeddings. A pre-trained model will also be made available for download.

Objaverse-XL Processing (Downloading, Rendering, and Classification)

To process objects from the full Objaverse-XL dataset:

1. Use the oxl_processing/objaverse_xl_batched_renderer.py script: This script handles downloading, rendering, and (optionally) classifying objects from Objaverse-XL.
2. Refer to the oxl_processing directory: This directory contains all necessary files and a dedicated README with more detailed instructions. Note that the full Objaverse-XL dataset is substantial, so consider this when planning storage and processing. You can also use the renders of the objects from the Objaverse_processed repository (The renders are around 500 GB).

Classification of Rendered Objects

To classify new renderings using the trained model:

1. Download Pre-trained Models: Download the trained classifier and the PCA model (used for dimensionality reduction of DINOv2 embeddings) from Hugging Face and place them in the car_quality_models directory.

2. Prepare a CSV: Create a CSV file containing the SHA256 hash and image path (sha256,img_path) for each rendered image you want to classify.

3. Run reclassify_oxl_data.py: Use the following command to classify the images:

   python reclassify_oxl_data.py --num_objects <number_of_objects> --gpu_batch_size <batch_size> [--use_combined_embeddings]
   

   • <number_of_objects>: The number of objects to classify.
   • <batch_size>: The batch size for GPU processing.
   • --use_combined_embeddings: (Optional) Use both DINOv2 and SigLIP embeddings for classification.