Image Segmentation
Transformers
Safetensors
English
prima
text-generation
computer-vision
multi-image-reasoning
pixel-grounding
vision-language
custom-code
custom_code
Instructions to use kanguyen-vn/PRIMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanguyen-vn/PRIMA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="kanguyen-vn/PRIMA", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("kanguyen-vn/PRIMA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Include clean PRIMA code and evaluator; document metrics, initialization, and MIT contributions
91c1e9e verified Download code/configs/evaluate.yaml from kanguyen-vn/PRIMA: direct link, hf CLI and curl.
- Browser
- Download file 237 Bytes
-
https://huggingface.co/kanguyen-vn/PRIMA/resolve/main/code/configs/evaluate.yaml
- Command line
-
hf download hf://kanguyen-vn/PRIMA/code/configs/evaluate.yaml
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curl -L -o evaluate.yaml https://huggingface.co/kanguyen-vn/PRIMA/resolve/main/code/configs/evaluate.yaml
237 Bytes
| merged_ckpt_path: PLAN-Lab/PRIMA | |
| dataset_dir: /path/to/datasets | |
| m4seg_annpath: /path/to/datasets/m4seg/annotations/m4seg_test.json.gz | |
| precision: bf16 | |
| val_batch_size: 1 | |
| max_new_tokens: 1024 | |
| workers: 0 | |
| output_save_path: outputs/evaluation | |