Instructions to use mfranzon/sam2-tiny-tracking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use mfranzon/sam2-tiny-tracking with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(mfranzon/sam2-tiny-tracking) 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(mfranzon/sam2-tiny-tracking) 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: 878 Bytes
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license: apache-2.0
tags:
- sam2
- segment-anything
- video-segmentation
- tracking
- microscopy
pipeline_tag: mask-generation
---
# SAM2 Tiny (Cell and Bacterium Tracking)
The SAM2 tiny checkpoint used in the Model Garden **Cell and Bacterium
Tracking** demo: class-agnostic video segment-and-track where pretrained
detectors have no labels, e.g. a neutrophil chasing a bacterium.
## Method
Human-seed-then-propagate: a few clicks lock the right object, then SAM2
propagates masks across the rest of the frames autonomously. **No training
required.**
## Provenance
This is the `sam2_hiera_tiny` checkpoint from
[`facebook/sam2-hiera-tiny`](https://huggingface.co/facebook/sam2-hiera-tiny),
re-hosted here for the Model Garden showcase. All credit to Meta AI.
## Usage
See the [SAM2 repository](https://github.com/facebookresearch/sam2) for the
video predictor API.
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