1. AGP GPU benchmarks from ixbt.com - 101 cards tested - VOGONS
Bevat niet: Handpan | Resultaten tonen met:Handpan
Article: https://www.ixbt.com/video2/over2k4.shtml
2. EPIC-KITCHENS VISOR Benchmark VIdeo Segmentations and Object ...
Abstract. We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video.
We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challen…
3. Pedestrian detection algorithm integrating large kernel attention ...
29 nov 2023 · Specifically, the detection accuracy improves by 1.1% compared to the original YOLOV5 algorithm, and the accuracy performance index reaches 73.0 ...
In the context of intelligent driving, pedestrian detection faces challenges related to low accuracy in target recognition and positioning. To address this issue, a pedestrian detection algorithm is proposed that integrates a large kernel attention mechanism with the YOLOV5 lightweight model. The algorithm aims to enhance long-term attention and dependence during image processing by fusing the large kernel attention module with the C3 module. Furthermore, it addresses the lack of long-distance relationship information in channel and spatial feature extraction and representation by introducing the Coordinate Attention mechanism. This mechanism effectively extracts local information and focused location details, thereby improving detection accuracy. To improve the positioning accuracy of obscured targets, the alpha CIOU bounding box regression loss function is employed. It helps mitigate the impact of occlusions and enhances the algorithm’s ability to precisely localize pedestrians. To evaluate the effectiveness of trained model, experiments are conducted on the BDD100K pedestrian dataset as well as the Pascal VOC dataset. Experimental results demonstrate that the improved attention fusion YOLOV5 lightweight model achieves an average accuracy of 60.3%. Specifically, the detection accuracy improves by 1.1% compared to the original YOLOV5 algorithm, and the accuracy performance index reaches 73.0%. These findings strongly indicate the proposed algorithm in significantly enhancing...
4. [PDF] Zero-shot Long Video Understanding via Informative Spatial-Temporal ...
12 nov 2024 · Our model significantly improves the state-of- the-art on three long video question-answering benchmarks: EgoSchema, NextQA, and Inten-.
5. Latest technological advances and insights into capture and removal of ...
7 mrt 2024 · Benchmarks ... As pressure increased from 5 to 30 bar, the permeability rose from 80 GPU to 140 and 120 GPU for H2S and CO2, respectively.
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6. A Lightweight Object Detection Algorithm for Remote Sensing ...
This paper proposes a lightweight object detection algorithm based on an attention mechanism and YOLOv5s.
The specific characteristics of remote sensing images, such as large directional variations, large target sizes, and dense target distributions, make target detection a challenging task. To improve the detection performance of models while ensuring real-time detection, this paper proposes a lightweight object detection algorithm based on an attention mechanism and YOLOv5s. Firstly, a depthwise-decoupled head (DD-head) module and spatial pyramid pooling cross-stage partial GSConv (SPPCSPG) module were constructed to replace the coupled head and the spatial pyramid pooling-fast (SPPF) module of YOLOv5s. A shuffle attention (SA) mechanism was introduced in the head structure to enhance spatial attention and reconstruct channel attention. A content-aware reassembly of features (CARAFE) module was introduced in the up-sampling operation to reassemble feature points with similar semantic information. In the neck structure, a GSConv module was introduced to maintain detection accuracy while reducing the number of parameters. Experimental results on remote sensing datasets, RSOD and DIOR, showed an improvement of 1.4% and 1.2% in mean average precision accuracy compared with the original YOLOv5s algorithm. Moreover, the algorithm was also tested on conventional object detection datasets, PASCAL VOC and MS COCO, which showed an improvement of 1.4% and 3.1% in mean average precision accuracy. Therefore, the experiments showed that the constructed algorithm not only outperformed the ori...
7. [PDF] Speculation-Centric Finite State Machine Parallelization on GPUs
To maximize the performance of running FSMs on GPUs, this work integrates different speculative parallelization schemes into a latency- sensitive framework, ...
8. Unleashing the power of AI in detecting metal surface defects
22 jan 2024 · The experimental setup consists of an AMD 15vCPU, RTXA5000 GPU, 24GB of memory, and the SGD optimizer for model optimization. A larger batch ...
The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP-Add module is added after the Concat module in the network’s Head section to strengthen the network’s ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model’s CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. M...
9. [PDF] Thesis F. Radmanesh - University of Twente Research Information
16 dec 2022 · (69 to 943 GPU) at 260 °C. For all gasses, the permeance follows the order ... evaluate the formation and performance of PAN-based TPE-HCCP.
