README.md · keras/sam_large_sa1b at c65ee5c307ab0a5dc631871b57f81aa24e7a30f3

metadata

library_name: keras-hub

Model Overview

The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-Hub
pip install -U -q keras>=3

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Presets

The following model checkpoints are provided by the Keras team. Weights have been ported from https://dl.fbaipublicfiles.com/segment_anything/. Full code examples for each are available below.

Preset name	Parameters	Description
sam_base_sa1b	93.74M	The base SAM model trained on the SA1B dataset.
sam_large_sa1b	312.34M	The large SAM model trained on the SA1B dataset.
sam_huge_sa1b	641.09M	The huge SAM model trained on the SA1B dataset.

Example Usage

Load pretrained model using from_preset.

image_size=1024
batch_size=2
input_data = {
    "images": np.ones(
        (batch_size, image_size, image_size, 3),
        dtype="float32",
    ),
    "points": np.ones((batch_size, 1, 2), dtype="float32"),
    "labels": np.ones((batch_size, 1), dtype="float32"),
    "boxes": np.ones((batch_size, 1, 2, 2), dtype="float32"),
    "masks": np.zeros(
        (batch_size, 0, image_size, image_size, 1)
    ),
}
sam = keras_hub.models.SAMImageSegmenter.from_preset('sam_base_sa1b')
outputs = sam.predict(input_data)
masks, iou_pred = outputs["masks"], outputs["iou_pred"]

Load segment anything image segmenter with custom backbone

image_size = 128
batch_size = 2
images = np.ones(
    (batch_size, image_size, image_size, 3),
    dtype="float32",
)
image_encoder = keras_hub.models.ViTDetBackbone(
    hidden_size=16,
    num_layers=16,
    intermediate_dim=16 * 4,
    num_heads=16,
    global_attention_layer_indices=[2, 5, 8, 11],
    patch_size=16,
    num_output_channels=8,
    window_size=2,
    image_shape=(image_size, image_size, 3),
)
prompt_encoder = keras_hub.layers.SAMPromptEncoder(
    hidden_size=8,
    image_embedding_size=(8, 8),
    input_image_size=(
        image_size,
        image_size,
    ),
    mask_in_channels=16,
)
mask_decoder = keras_hub.layers.SAMMaskDecoder(
    num_layers=2,
    hidden_size=8,
    intermediate_dim=32,
    num_heads=8,
    embedding_dim=8,
    num_multimask_outputs=3,
    iou_head_depth=3,
    iou_head_hidden_dim=8,
)
backbone = keras_hub.models.SAMBackbone(
    image_encoder=image_encoder,
    prompt_encoder=prompt_encoder,
    mask_decoder=mask_decoder,
)
sam = keras_hub.models.SAMImageSegmenter(
    backbone=backbone
)

Example Usage with Hugging Face URI