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timesfm

Sat, 04 Apr 2026 15:53:18 +0800

google-research/timesfm

TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

Paper: A decoder-only foundation model for time-series forecasting, ICML 2024.
All checkpoints: TimesFM Hugging Face Collection.
Google Research blog.
TimesFM in BigQuery: an official Google product.

This open version is not an officially supported Google product.

Latest Model Version: TimesFM 2.5

Archived Model Versions:

1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load them.

Update - Mar. 19, 2026

Huge shoutout to @borealBytes for adding the support for AGENTS! TimesFM SKILL.md is out.

Update - Oct. 29, 2025

Added back the covariate support through XReg for TimesFM 2.5.

Update - Sept. 15, 2025

TimesFM 2.5 is out!

Comparing to TimesFM 2.0, this new 2.5 model:

uses 200M parameters, down from 500M.
supports up to 16k context length, up from 2048.
supports continuous quantile forecast up to 1k horizon via an optional 30M quantile head.
gets rid of the frequency indicator.
has a couple of new forecasting flags.

Along with the model upgrade we have also upgraded the inference API. This repo will be under construction over the next few weeks to

add support for an upcoming Flax version of the model (faster inference).
add back covariate support.
populate more docstrings, docs and notebook.

Install

Clone the repository:

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git clone https://github.com/google-research/timesfm.git
cd timesfm

Create a virtual environment and install dependencies using uv:

# Create a virtual environment
uv venv

# Activate the environment
source .venv/bin/activate

# Install the package in editable mode with torch
uv pip install -e .[torch]
# Or with flax
uv pip install -e .[flax]
# Or XReg is needed
uv pip install -e .[xreg]

[Optional] Install your preferred torch / jax backend based on your OS and accelerators (CPU, GPU, TPU or Apple Silicon).:

Install PyTorch.
Install Jax for Flax.

Code Example

import torch
import numpy as np
import timesfm

torch.set_float32_matmul_precision("high")

model = timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")

model.compile(
    timesfm.ForecastConfig(
        max_context=1024,
        max_horizon=256,
        normalize_inputs=True,
        use_continuous_quantile_head=True,
        force_flip_invariance=True,
        infer_is_positive=True,
        fix_quantile_crossing=True,
    )
)
point_forecast, quantile_forecast = model.forecast(
    horizon=12,
    inputs=[
        np.linspace(0, 1, 100),
        np.sin(np.linspace(0, 20, 67)),
    ],  # Two dummy inputs
)
point_forecast.shape  # (2, 12)
quantile_forecast.shape  # (2, 12, 10): mean, then 10th to 90th quantiles.

segment-anything

Sat, 19 Jul 2025 15:33:17 +0800

facebookresearch/segment-anything

Latest updates – SAM 2: Segment Anything in Images and Videos

Please check out our new release on Segment Anything Model 2 (SAM 2).

SAM 2 code: https://github.com/facebookresearch/segment-anything-2
SAM 2 demo: https://sam2.metademolab.com/
SAM 2 paper: https://arxiv.org/abs/2408.00714

Segment Anything Model 2 (SAM 2) is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect our SA-V dataset, the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.

Segment Anything

Meta AI Research, FAIR

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick

[Paper] [Project] [Demo] [Dataset] [Blog] [BibTeX]

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.

Installation

The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Install Segment Anything:

`1`	`pip install git+https://github.com/facebookresearch/segment-anything.git`

or clone the repository locally and install with

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git clone git@github.com:facebookresearch/segment-anything.git
cd segment-anything; pip install -e .

The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. jupyter is also required to run the example notebooks.

`1`	`pip install opencv-python pycocotools matplotlib onnxruntime onnx`

Getting Started

First download a model checkpoint. Then the model can be used in just a few lines to get masks from a given prompt:

from segment_anything import SamPredictor, sam_model_registry
sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
predictor = SamPredictor(sam)
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)

or generate masks for an entire image:

from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
mask_generator = SamAutomaticMaskGenerator(sam)
masks = mask_generator.generate(<your_image>)

Additionally, masks can be generated for images from the command line:

`1`	`python scripts/amg.py --checkpoint <path/to/checkpoint> --model-type <model_type> --input <image_or_folder> --output <path/to/output>`

See the examples notebooks on using SAM with prompts and automatically generating masks for more details.

ONNX Export

SAM’s lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the demo. Export the model with

`1`	`python scripts/export_onnx_model.py --checkpoint <path/to/checkpoint> --model-type <model_type> --output <path/to/output>`

See the example notebook for details on how to combine image preprocessing via SAM’s backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.

Web demo

The demo/ folder has a simple one page React app which shows how to run mask prediction with the exported ONNX model in a web browser with multithreading. Please see demo/README.md for more details.

Model Checkpoints

Three model versions of the model are available with different backbone sizes. These models can be instantiated by running

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from segment_anything import sam_model_registry
sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")

Click the links below to download the checkpoint for the corresponding model type.

default or vit_h: ViT-H SAM model.
vit_l: ViT-L SAM model.
vit_b: ViT-B SAM model.

Dataset

See here for an overview of the datastet. The dataset can be downloaded here. By downloading the datasets you agree that you have read and accepted the terms of the SA-1B Dataset Research License.

We save masks per image as a json file. It can be loaded as a dictionary in python in the below format.

{
    "image"                 : image_info,
    "annotations"           : [annotation],
}

image_info {
    "image_id"              : int,              # Image id
    "width"                 : int,              # Image width
    "height"                : int,              # Image height
    "file_name"             : str,              # Image filename
}

annotation {
    "id"                    : int,              # Annotation id
    "segmentation"          : dict,             # Mask saved in COCO RLE format.
    "bbox"                  : [x, y, w, h],     # The box around the mask, in XYWH format
    "area"                  : int,              # The area in pixels of the mask
    "predicted_iou"         : float,            # The model's own prediction of the mask's quality
    "stability_score"       : float,            # A measure of the mask's quality
    "crop_box"              : [x, y, w, h],     # The crop of the image used to generate the mask, in XYWH format
    "point_coords"          : [[x, y]],         # The point coordinates input to the model to generate the mask
}

Image ids can be found in sa_images_ids.txt which can be downloaded using the above link as well.

To decode a mask in COCO RLE format into binary:

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from pycocotools import mask as mask_utils
mask = mask_utils.decode(annotation["segmentation"])

See here for more instructions to manipulate masks stored in RLE format.

License

The model is licensed under the Apache 2.0 license.

Contributing

See contributing and the code of conduct.

Contributors

The Segment Anything project was made possible with the help of many contributors (alphabetical):

Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom

Citing Segment Anything

If you use SAM or SA-1B in your research, please use the following BibTeX entry.

@article{kirillov2023segany,
  title={Segment Anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}