157 research outputs found
ON THE INFLUENCE OF COGNITIVE IMPAIRMENT FACTORS ON THE CLASSIFICATION OF HORTICULTURAL HEALING LANDSCAPE SYSTEM AND ELEMENT ATTRIBUTES
ON THE INFLUENCE OF COGNITIVE IMPAIRMENT FACTORS ON THE CLASSIFICATION OF HORTICULTURAL HEALING LANDSCAPE SYSTEM AND ELEMENT ATTRIBUTES
Soil Image Segmentation Based on Mask R-CNN
The complex background in the soil image collected in the field natural
environment will affect the subsequent soil image recognition based on machine
vision. Segmenting the soil center area from the soil image can eliminate the
influence of the complex background, which is an important preprocessing work
for subsequent soil image recognition. For the first time, the deep learning
method was applied to soil image segmentation, and the Mask R-CNN model was
selected to complete the positioning and segmentation of soil images. Construct
a soil image dataset based on the collected soil images, use the EISeg
annotation tool to mark the soil area as soil, and save the annotation
information; train the Mask R-CNN soil image instance segmentation model. The
trained model can obtain accurate segmentation results for soil images, and can
show good performance on soil images collected in different environments; the
trained instance segmentation model has a loss value of 0.1999 in the training
set, and the mAP of the validation set segmentation (IoU=0.5) is 0.8804, and it
takes only 0.06s to complete image segmentation based on GPU acceleration,
which can meet the real-time segmentation and detection of soil images in the
field under natural conditions. You can get our code in the Conclusions. The
homepage is https://github.com/YidaMyth.Comment: 4 pages, 5 figures, Published in 2023 3rd International Conference on
Consumer Electronics and Computer Engineerin
Initializing Models with Larger Ones
Weight initialization plays an important role in neural network training.
Widely used initialization methods are proposed and evaluated for networks that
are trained from scratch. However, the growing number of pretrained models now
offers new opportunities for tackling this classical problem of weight
initialization. In this work, we introduce weight selection, a method for
initializing smaller models by selecting a subset of weights from a pretrained
larger model. This enables the transfer of knowledge from pretrained weights to
smaller models. Our experiments demonstrate that weight selection can
significantly enhance the performance of small models and reduce their training
time. Notably, it can also be used together with knowledge distillation. Weight
selection offers a new approach to leverage the power of pretrained models in
resource-constrained settings, and we hope it can be a useful tool for training
small models in the large-model era. Code is available at
https://github.com/OscarXZQ/weight-selection
Sequential Manipulation Planning on Scene Graph
We devise a 3D scene graph representation, contact graph+ (cg+), for
efficient sequential task planning. Augmented with predicate-like attributes,
this contact graph-based representation abstracts scene layouts with succinct
geometric information and valid robot-scene interactions. Goal configurations,
naturally specified on contact graphs, can be produced by a genetic algorithm
with a stochastic optimization method. A task plan is then initialized by
computing the Graph Editing Distance (GED) between the initial contact graphs
and the goal configurations, which generates graph edit operations
corresponding to possible robot actions. We finalize the task plan by imposing
constraints to regulate the temporal feasibility of graph edit operations,
ensuring valid task and motion correspondences. In a series of simulations and
experiments, robots successfully complete complex sequential object
rearrangement tasks that are difficult to specify using conventional planning
language like Planning Domain Definition Language (PDDL), demonstrating the
high feasibility and potential of robot sequential task planning on contact
graph.Comment: 8 pages, 6 figures. Accepted by IROS 202
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
As deep learning models nowadays are widely adopted by both cloud services
and edge devices, reducing the latency of deep learning model inferences
becomes crucial to provide efficient model serving. However, it is challenging
to develop efficient tensor programs for deep learning operators due to the
high complexity of modern accelerators and the rapidly growing number of
operators. Deep learning compilers, such as Apache TVM, adopt declarative
scheduling primitives to lower the bar of developing tensor programs. However,
we show that this approach is insufficient to cover state-of-the-art tensor
program optimizations. In this paper, we propose to embed the scheduling
process into tensor programs and use dedicated mappings, called task mappings,
to define the computation assignment and ordering. This new approach greatly
enriches the expressible optimizations by allowing developers to manipulate
tensor programs at a much finer granularity. We call the proposed method the
task-mapping programming paradigm. In addition, we propose a new
post-scheduling fusion optimization that allows developers to focus on
scheduling every single operator and automates the fusion after scheduling. It
greatly reduces the engineering efforts for operator fusion. Our proposed
paradigm also constructs an efficient hardware-centric schedule space, which is
agnostic to the program input size and greatly reduces the tuning time. With
the proposed paradigm, we implement a deep learning compiler Hidet. Extensive
experiments on modern convolution and transformer models show that Hidet
outperforms state-of-the-art DNN inference framework, ONNX Runtime, and
compiler, TVM equipped with scheduler AutoTVM and Ansor, by up to 1.48x (1.22x
on average). It also reduces the tuning time by 20x and 11x compared with
AutoTVM and Ansor, respectively. We open-sourced hidet at
https://www.github.com/hidet-org/hidet.Comment: 15 pages, 22 figures, 1 tabl
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