Instructions to use dl1wjd2/Logistics_AX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dl1wjd2/Logistics_AX with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("dl1wjd2/Logistics_AX", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,037 Bytes
a3f0a64 | 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 | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch._functorch.deprecated import (
combine_state_for_ensemble,
functionalize,
grad,
grad_and_value,
hessian,
jacfwd,
jacrev,
jvp,
make_functional,
make_functional_with_buffers,
vjp,
vmap,
)
# utilities. Maybe these should go in their own namespace in the future?
from torch._functorch.make_functional import (
FunctionalModule,
FunctionalModuleWithBuffers,
)
# Was never documented
from torch._functorch.python_key import make_fx
# Top-level APIs. Please think carefully before adding something to the
# top-level namespace:
# - private helper functions should go into torch._functorch
# - very experimental things should go into functorch.experimental
# - compilation related things should go into functorch.compile
__version__ = torch.__version__
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