Instructions to use zeromodels/sam2_hiera_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/sam2_hiera_tiny with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/sam2_hiera_tiny") - Keras
How to use zeromodels/sam2_hiera_tiny with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zeromodels/sam2_hiera_tiny") - sam2
How to use zeromodels/sam2_hiera_tiny with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("zeromodels/sam2_hiera_tiny") 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("zeromodels/sam2_hiera_tiny") 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
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Download README.md from zeromodels/sam2_hiera_tiny: direct link, hf CLI and curl.
- Browser
- Download file 813 Bytes
-
https://huggingface.co/zeromodels/sam2_hiera_tiny/resolve/main/README.md
- Command line
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hf download hf://zeromodels/sam2_hiera_tiny/README.md
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curl -L -o README.md https://huggingface.co/zeromodels/sam2_hiera_tiny/resolve/main/README.md
813 Bytes
| pipeline_tag: mask-generation | |
| license: apache-2.0 | |
| library_name: zeromodels | |
| tags: | |
| - keras | |
| - zeromodels | |
| - sam2 | |
| - tf | |
| - jax | |
| - pytorch | |
| # sam2_hiera_tiny (Keras 3) | |
| [](https://github.com/IMvision12/ZeroModels) [](https://imvision12.github.io/ZeroModels/sam2/) [](https://huggingface.co/collections/zeromodels/sam-v1-v2-v3-6a8eaf73a274fffa2ea83cfd) | |
| Pure-Keras 3 weights for [zeromodels](https://github.com/IMvision12/ZeroModels), mirrored from the source. License: `apache-2.0`. | |
| ```python | |
| from zeromodels.models.sam2 import SAM2PromptableSegment | |
| model = SAM2PromptableSegment.from_weights("sam2_hiera_tiny") | |
| ``` |