Instructions to use zeromodels/sam2_hiera_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use zeromodels/sam2_hiera_tiny with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use zeromodels/sam2_hiera_tiny with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" 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
File size: 1,037 Bytes
826a766 711289f 826a766 711289f a757f87 826a766 711289f 826a766 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"library_name": "kerasformers",
"kerasformers_version": "1.2.1",
"model_module": "kerasformers.models.sam2",
"model_class": "SAM2PromptableSegment",
"variant": "sam2_hiera_tiny",
"weights": "model.weights.h5",
"schema_version": 2,
"weight_dtype": "float32",
"model_type": "sam2",
"vision_config": {
"hidden_dim": 96,
"blocks_per_stage": [
1,
2,
7,
2
],
"embed_dim_per_stage": [
96,
192,
384,
768
],
"num_attention_heads_per_stage": [
1,
2,
4,
8
],
"window_size_per_stage": [
8,
4,
14,
7
],
"global_attention_blocks": [
5,
7,
9
],
"backbone_channel_list": [
768,
384,
192,
96
],
"window_pos_embed_bg_size": null,
"num_multimask_outputs": 3,
"include_box_input": false,
"include_mask_input": false,
"multimask_output": true,
"image_size": 1024
}
} |