Image Segmentation
Transformers
Safetensors
mlrs
feature-extraction
remote-sensing
reasoning-segmentation
earth-observation
custom_code
Instructions to use INSAIT-Institute/MLRS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INSAIT-Institute/MLRS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="INSAIT-Institute/MLRS", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("INSAIT-Institute/MLRS", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from INSAIT-Institute/MLRS: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/INSAIT-Institute/MLRS/resolve/main/config.json
- Command line
-
hf download hf://INSAIT-Institute/MLRS/config.json
-
curl -L -o config.json https://huggingface.co/INSAIT-Institute/MLRS/resolve/main/config.json
1.81 kB
| { | |
| "architectures": [ | |
| "MLRS" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_mlrs.MLRSConfig", | |
| "AutoModel": "modeling_mlrs.MLRS", | |
| "AutoProcessor": "processing_mlrs.MLRSProcessor" | |
| }, | |
| "bbox_filtering": true, | |
| "image_first": true, | |
| "instruction_prompt": "You are solving visual reasoning tasks.\n\nYou may respond in one of two final formats:\n\n1. If the task requires segmenting/localizing objects in the image, call the segmentation tool by outputting:\n<segmentation>{\"noun phrase\": \"...\", \"objects\": [{\"bbox\": [x1, y1, x2, y2]}]}</segmentation>\n\nThe segmentation JSON must contain:\n- \"noun phrase\": exactly one referential noun phrase identifying the target.\n- \"objects\": a list of objects, each with one \"bbox\" field.\n- Coordinates should use the 0-999 image coordinate grid.\n\nIf the task includes several images, segmentation tool uses the last rgb image.\n\n2. If the task asks for a textual answer and does not require segmentation, output:\n<answer>your textual answer</answer>\n\nUse <think>...</think> before the final tag when reasoning is useful.\nDo not use <answer> for segmentation. Do not use <segmentation> for non-segmentation answers.\n", | |
| "language_adapter_subdir": "language_adapter", | |
| "language_base_model": "OpenGVLab/InternVL3_5-8B-HF", | |
| "language_base_revision": "741a7d03020411e666c6109218ab71e08151ef86", | |
| "mask_threshold": 0.5, | |
| "max_image_longest_side": 4030, | |
| "max_new_tokens": 1024, | |
| "model_type": "mlrs", | |
| "multi_image_downsample_min_images": 3, | |
| "multi_image_max_longest_side": 448, | |
| "pad_to_multiple_of": 8, | |
| "padding_side": "left", | |
| "sam_base_model": "facebook/sam3", | |
| "sam_base_revision": "3c879f39826c281e95690f02c7821c4de09afae7", | |
| "sam_delta_file": "sam3_delta.safetensors", | |
| "transformers_version": "5.18.0" | |
| } | |