Video-Text-to-Text
Transformers
Safetensors
sam2
English
vica_qwen
text-generation
multimodal
vision-language
video understanding
visuospatial cognition
spatial reasoning
vlm
llava
qwen
siglip
hiera
dual-encoder
Instructions to use nkkbr/ViCA2-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nkkbr/ViCA2-init with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("nkkbr/ViCA2-init", device_map="auto") - sam2
How to use nkkbr/ViCA2-init with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(nkkbr/ViCA2-init) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(nkkbr/ViCA2-init) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - multimodal | |
| - vision-language | |
| - video understanding | |
| - visuospatial cognition | |
| - spatial reasoning | |
| - vlm | |
| - llava | |
| - qwen | |
| - siglip | |
| - hiera | |
| - sam2 | |
| - dual-encoder | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: video-text-to-text | |
| model_name: ViCA2-7B-Init | |
| ## Usage and Full Documentation | |
| For detailed model description, training setup, datasets, evaluation results, and inference code, **please refer to the following links**: | |
| [](https://github.com/nkkbr/ViCA) | |
| [](https://huggingface.co/nkkbr/ViCA2) |