VLMPed CoT
A LoRA fine-tuned version of Qwen2.5-VL-3B-Instruct for pedestrian crossing intention prediction, trained with Chain-of-Thought supervision.
Modellquelle
- Art
- LoRA
- Dateigröße
- 142 MB
- Basismodell
- Qwen/Qwen2.5-VL-3B-Instruct
- Version
- vadapter_model
- Aktualisiert
- 3. Okt. 2026
- Verleger
- @chiawen0104
- Downloads
- 4
- Quelle
- Hugging Face
Quellenbeschreibung
VLMPed-CoT
A LoRA fine-tuned version of Qwen2.5-VL-3B-Instruct for pedestrian crossing intention prediction, trained with Chain-of-Thought supervision.
This model is part of the ECE 228 final project at UCSD (Spring 2026): "How Do Vision Language Models Utilize Multi-Frame Temporal Information for Pedestrian Intention Prediction?"
Project repository: ece228_VLMPed-CoT
Model Details
- Developed by: chiawen0104
- Model type: Vision-Language Model (LoRA fine-tuned)
- Finetuned from: Qwen/Qwen2.5-VL-3B-Instruct
- Task: Pedestrian crossing intention prediction (binary: cross / not cross)
- Training datasets: JAAD, PIE
- Framework: PEFT 0.15.1
How to Get Started
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from peft import PeftModel
base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-3B-Instruct"
)
model = PeftModel.from_pretrained(base_model, "chiawen0104/VLMPed-CoT")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
Training Details
- Base model: Qwen2.5-VL-3B-Instruct
- Fine-tuning method: LoRA (via PEFT)
- Training regime: bf16 mixed precision
- Training data: JAAD and PIE pedestrian crossing intention datasets
- CoT supervision: ✅ Chain-of-Thought reasoning generated via Gemini API
Intended Use
This model takes multi-frame pedestrian images as input and predicts whether a pedestrian intends to cross the street. The CoT supervision encourages the model to reason step-by-step before making a prediction. It is intended for research purposes in autonomous driving and pedestrian behavior analysis.
Differences from VLMPed-wo-CoT
| VLMPed-CoT | VLMPed-wo-CoT | |
|---|---|---|
| CoT supervision | ✅ | ❌ |
| Direct prediction | ✅ | ✅ |
Reference
- Original Paper: VLMPed-CoT: A large vision-language model with a chain-of-thought mechanism for pedestrian crossing intention prediction
- Original implementation: lyc2121/VLMPed-CoT-for-Pedestrian-Crossing-Intention-Prediction
- Companion model: chiawen0104/VLMPed-wo-CoT
Framework versions
- PEFT 0.15.1
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 3. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 Artefaktadapter_model.safetensors
safetensors · 142 MB · SHA-256 2ada072ccdbc…22ac · Hugging Face
HerunterladenQuellenauszüge
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Quellenauszüge
3 Auszüge--- base_model: Qwen/Qwen2.5-VL-3B-Instruct library_name: peft --- # VLMPed-CoT A LoRA fine-tuned version of [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) for pedestrian crossing intention prediction, trained **with** Chain-of-Thought supervision. This model is part of the ECE 228 final project at UCSD (Spring 2026): **"How Do Vision Language Models Utilize Multi-Frame Temporal Information for Pedestrian Intention Prediction?"** Project repository: [ece228_VLMPed-CoT](https://github.com/chiawen0104/ece228_VLMPed-CoT) --- ## Model Details - **Developed by:** chiawen0104 - **Model type:** Vision-Language Model (LoRA fine-tuned) - **Finetuned from:** [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) - **Task:** Pedestrian crossing intention prediction (binary: cross / not cross) - **Training datasets:** JAAD, PIE - **Framework:** PEFT 0.15.1 --- ## How to Get Started ```python from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration from peft import PeftModel base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-3B-Instruct" ) model = PeftModel.from_pretrained(base_model, "chiawen0104/VLMPed-CoT") processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") ``` --- ## Training Details - **Base model:** Qwen2.5-VL-3B-Instruct - **Fine-tuning method:** LoRA (via PEFT) - **Training regime:** bf16 mixed precision - **Training data:** JAAD and PIE pedestrian crossing intention datasets - **CoT supervision:** ✅ Chain-of-Thought reasoning generated via Gemini API --- ## Intended Use This model takes multi-frame pedestrian images as input and predicts whether a pedestrian intends to cross the street. The CoT supervision encourages the model to reason step-by-step before making a prediction. It is intended for research purposes in autonomous driving and pedestrian behavior analysis. --- ## Differences from VLMPed-wo-CoT | | VLMPed-CoT | VLMPed-wo-CoT | |---|---|---| | CoT supervision | ✅ | ❌ | | Direct prediction | ✅ | ✅ | --- ## Reference - Original Paper: [VLMPed-CoT: A large vision-language model with a chain-of-thought mechanism for pedestrian crossing intention prediction](https://www.sciopen.com/article/10.26599/COMMTR.2026.9640009) - Original implementation: [lyc2121/VLMPed-CoT-for-Pedestrian-Crossing-Intention-Predic...
Source context: 4 downloads · 0 likes · Library peft · Repo chiawen0104/VLMPed-CoT
Source context: 2 downloads · 0 likes · Library peft · Repo chiawen0104/VLMPed-CoT
