Goal: Build a Vision-Language Model (VLM) from scratch that understands traffic scenes through natural language commands. The model answers questions like "Is the left lane clear?" or "Can I safely turn right?" based on visual input.
Architecture: PaliGemma-Inspired
- Vision Encoder: SigLip (Signal Lidar Image Pre-training) style encoder.
- Language Decoder: Gemma-style decoder with RoPE embeddings.
- Fusion: Linear projection + Cross-Attention mechanism.
Timeline: 10 Days (Research-Grade Implementation)
- OS: Windows / Linux / MacOS
- Python: 3.8+
- Hardware: GPU recommended (NVIDIA RTX 3000 series or better)
- Clone/Create Project: (If you haven't run the setup script yet, ensure the directory structure matches the blueprint).
- Install Dependencies:
pip install torch torchvision numpy pandas matplotlib tqdm h5py scikit-learn
- Data Setup:
- Download the BDD100K Dataset.
- Place the images in a raw data folder (configure path in
config/dataset_config.py).
traffic_vlm/
├── config/ # Hyperparameters & Paths
├── data/ # Dataset processing, tokenizers, loaders
├── model/ # Core VLM Architecture
│ ├── vision/ # SigLip Encoder components
│ ├── language/ # Gemma Decoder components
│ └── fusion/ # Cross-modal projection & attention
├── training/ # Training loops, loss, optimizers
├── evaluation/ # Metrics & Inference logic
├── visualization/ # Attention heatmaps & failure analysis
├── experiments/ # Ablation studies
├── utils/ # Logging & Checkpointing
└── notebooks/ # Analysis & Testing
-
**Task 1:
config/model_config.py** -
Define
ModelConfigdataclass (vision_dim, text_dim, heads, layers, patch_size). -
**Task 2:
config/training_config.py** -
Define
TrainingConfig(batch_size, learning_rate, epochs, device). -
**Task 3:
config/dataset_config.py** -
Define paths for BDD100K images and JSONs, and normalization constants.
-
**Task 4:
data/dataset_builder.py** -
Implement
process_bdd100k: Filter clear/daytime images, extract bounding boxes. -
**Task 5:
data/command_generator.py** -
Implement
generate_pair: Create "Question + Answer" pairs based on image metadata. -
**Task 6:
data/tokenizer.py** -
Build
SimpleTokenizer: Create vocab dict,encode(), anddecode()methods. -
**Task 7:
data/data_loader.py** -
Implement
TrafficDatasetandcollate_fnto return batched tensors.
-
**Task 8:
model/vision/siglip_encoder.py** -
Assemble the full Vision Transformer stack.
-
**Task 9:
model/vision/vision_embeddings.py** -
Implement Patch Embeddings (Conv2d) + Positional Embeddings.
-
**Task 10:
model/vision/vision_attention.py** -
Implement Multi-Head Self-Attention for vision patches.
-
**Task 11:
model/language/gemma_decoder.py** -
Build the decoder stack (Linear -> Transformer Blocks -> Output Head).
-
**Task 12:
model/language/decoder_layer.py** -
Implement
DecoderBlock: Self-Attention + Cross-Attention + FFN. -
**Task 13:
model/language/rope_embeddings.py** -
Implement Rotary Positional Embeddings (RoPE) for the query/key vectors.
-
**Task 14:
model/fusion/projection_layer.py** -
Implement Linear Projection to map Vision Dim → Language Dim.
-
**Task 15:
model/fusion/cross_attention.py** -
Implement Cross-Attention (Query=Text, Key/Value=Vision).
-
**Task 16:
model/fusion/multimodal_fusion.py** -
Implement helper logic for mask creation and token concatenation.
-
**Task 17:
model/vlm_model.py** -
Assemble the VLM: Initialize Encoder, Projector, and Decoder. Write the
forward()pass.
-
**Task 18:
training/trainer.py** -
Implement the main training loop, validation step, and GPU transfer.
-
**Task 19:
training/loss_functions.py** -
Implement CrossEntropyLoss (with optional masking/weighting).
-
**Task 20:
training/optimizer.py** -
Configure AdamW with weight decay.
-
**Task 21:
training/scheduler.py** -
Implement learning rate warmup + cosine decay.
-
**Task 22:
evaluation/metrics.py** -
Implement Accuracy, Precision, Recall, F1 Score calculations.
-
**Task 23:
evaluation/attention_metrics.py** -
Implement Attention Entropy and Grounding Score logic.
-
**Task 24:
evaluation/evaluator.py** -
Create the inference pipeline to run tests on the holdout set.
-
**Task 25:
visualization/attention_viz.py** -
Create plots for Vision and Language self-attention.
-
**Task 26:
visualization/cross_attention_viz.py** -
Critical: Overlay attention heatmaps onto original images to show reasoning.
-
**Task 27:
visualization/failure_analysis.py** -
Script to save and display worst-prediction examples.
-
**Task 28:
experiments/ablation_studies.py** -
Script to run training with components disabled (e.g., no cross-attention).
-
**Task 29:
experiments/sensitivity_analysis.py** -
Test model robustness against noise or occlusion.
-
**Task 30:
utils/checkpoint_manager.py** -
Logic for saving/loading model weights safely.
-
**Task 31:
utils/logging_utils.py** -
Wrappers for TensorBoard or simple CSV logging.
-
**Task 32:
utils/tensor_utils.py** -
Helper functions for padding sequences and creating masks.
-
**Task 33:
notebooks/data_exploration.ipynb** -
Initial dataset analysis.
-
**Task 34:
notebooks/model_testing.ipynb** -
Interactive testing sandbox.
-
**Task 35:
notebooks/results_analysis.ipynb** -
Final report generation.
- Milestone 1: Data Pipeline working (Batch of images/text loads correctly).
- Milestone 2: Vision Encoder outputs correct tensor shape.
- Milestone 3: Language Decoder generates random text (untrained).
- Milestone 4: Full Model Integration (Forward pass works without error).
- Milestone 5: Training Loop runs and loss decreases.
- Milestone 6: Evaluation metrics implemented.
- Milestone 7: Visualizations prove grounding (Model looks at the right objects).
To Train:
python -m training.trainer
To Evaluate:
python -m evaluation.evaluator
To Visualize Attention:
python -m visualization.attention_viz --image "test.jpg" --text "Is the road clear?"
This project is for educational and research purposes. Author: [Your Name] Date: December 2025