Video-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
pedestrianqa
qwen2.5-vl
vision-language
video-question-answering
autonomous-driving
pedestrian-intention-prediction
pedestrian-trajectory-prediction
rationale-generation
text-generation-inference
Instructions to use namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct") model = AutoModelForMultimodalLM.from_pretrained("namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct") - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
c9d330c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"min_pixels": 3136,
"max_pixels": 12845056,
"patch_size": 14,
"temporal_patch_size": 2,
"merge_size": 2,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"image_processor_type": "Qwen2VLImageProcessor",
"processor_class": "Qwen2_5_VLProcessor"
}
|