Image-Text-to-Text
sam2
English
agriculture
multimodal
vision-language
visual-grounding
image-segmentation
grounded-caption-generation
referring-expression-segmentation
bioclip
dinov3
Instructions to use boudiafA/AgriScope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use boudiafA/AgriScope with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(boudiafA/AgriScope) 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(boudiafA/AgriScope) 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
File size: 727 Bytes
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message: "If you use AgriScope in your research, please cite the accompanying paper."
title: "AgriScope: Pixel-Grounded Multimodal Understanding for Agriculture Images"
type: software
authors:
- family-names: Boudiaf
given-names: Abderrahmene
- family-names: Alanssari
given-names: Mohamad
- family-names: Hussain
given-names: Irfan
- family-names: Javed
given-names: Sajid
repository-code: "https://github.com/boudiafA/AgriScope"
url: "https://github.com/boudiafA/AgriScope"
license: Apache-2.0
date-released: 2026-07-17
abstract: >-
AgriScope is a unified pixel-grounded multimodal framework for image-level,
region-level, and pixel-level understanding of agricultural imagery.
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