Instructions to use jamiewjm/sam-tp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use jamiewjm/sam-tp with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(jamiewjm/sam-tp) 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(jamiewjm/sam-tp) 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
update zip name
Browse files
README.md
CHANGED
|
@@ -24,7 +24,8 @@ size_categories:
|
|
| 24 |
# SAM‑TP Traversability Dataset
|
| 25 |
|
| 26 |
This repository contains pixel‑wise **traversability masks** paired with egocentric RGB images, prepared in a **flat, filename‑aligned** layout that is convenient for training SAM‑2 / SAM‑TP‑style segmentation models.
|
| 27 |
-
|
|
|
|
| 28 |
|
| 29 |
> **Folder layout**
|
| 30 |
```
|
|
|
|
| 24 |
# SAM‑TP Traversability Dataset
|
| 25 |
|
| 26 |
This repository contains pixel‑wise **traversability masks** paired with egocentric RGB images, prepared in a **flat, filename‑aligned** layout that is convenient for training SAM‑2 / SAM‑TP‑style segmentation models.
|
| 27 |
+
|
| 28 |
+
To use the dataset, simply download the sam2_flat_fold57.zip file and unzip it.
|
| 29 |
|
| 30 |
> **Folder layout**
|
| 31 |
```
|