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
File size: 1,000 Bytes
91c1e9e 5e66a57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Installation
The initial implementation targets Python 3.10, PyTorch 1.13.1 with CUDA 11.7, and Transformers 4.42.3. Inference uses BF16. This environment has been used for development; a fresh installation remains to be validated.
```bash
conda create -n prima python=3.10 -y
conda activate prima
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 \
--extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
```
COCO caption metrics require a Java runtime. Training writes progress and scalar losses to standard output.
MMCV and MMDetection are not required. Run commands from the project root. Placeholders must be replaced before loading a model or dataset.
Run the release interface checks with:
```bash
python -m unittest discover -s tests -v
```
These tests cover checkpoint loading, grounding-mask alignment, and evaluation behavior without downloading model weights or datasets. Test caches and generated outputs are excluded by `.gitignore`.
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