Datasets:

abhijitbetigeri
/

dc-ops-dataset

text stringlengths 5 290
server rack
server
cable
LED light
label
network port
power supply
patch panel
6 0.060156 0.293056 0.057031 0.748611 0.490625 0.637500 0.501563 0.356944
5 0.457813 0.566667 0.489844 0.700000 0.498437 0.586111 0.485938 0.573611 0.488281 0.679167 0.471875 0.586111
8 0.585938 0.984722 0.440625 0.734722 0.009375 0.819444 0.052344 0.997222
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5 0.379688 0.625000 0.319531 0.738889 0.332031 0.736111 0.385156 0.665278 0.388281 0.643056
0 0.059375 0.295833 0.056250 0.747222 0.490625 0.637500 0.501563 0.356944
1 0.061719 0.280556 0.055469 0.750000 0.471094 0.648611 0.450000 0.565278 0.504687 0.523611 0.502344 0.297222
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10 0.513723 0.556633 0.356205 0.558673 0.401551 0.733673 0.435859 0.714796 0.433771 0.614286 0.459427 0.715306 0.478819 0.604082 0.480310 0.706633 0.504773 0.730102
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7 0.545000 0.278333 0.538000 0.285000 0.529000 0.308333 0.531000 0.346667 0.544000 0.366667 0.548000 0.363333 0.548000 0.281667
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5 0.030000 0.210000 0.044000 0.336667 0.223000 0.491667 0.217000 0.336667 0.249000 0.313333 0.150000 0.278333 0.255000 0.256667
5 0.644000 0.563333 0.578000 0.653333 0.564000 0.805000 0.593000 0.918333 0.663000 0.970000 0.726000 0.893333 0.745000 0.753333 0.708000 0.601667
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6 0.626000 0.286667 0.914000 0.466667 0.623000 0.520000 0.895000 0.688333 0.749000 0.793333 0.999000 0.986667 0.999000 0.350000
13 0.626000 0.286667 0.914000 0.466667 0.623000 0.520000 0.896000 0.688333 0.749000 0.793333 0.999000 0.986667 0.999000 0.350000
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3 0.299369 0.843074 0.293057 0.966450 0.315600 0.970779 0.322813 0.996753 0.333634 0.970779 0.381425 0.968615 0.383228 0.850649
8 0.770063 0.237013 0.762849 0.179654 0.589720 0.189394 0.657349 0.248918
5 0.430117 0.364719 0.397656 0.547619 0.220920 0.588745 0.000000 0.800866 0.000000 0.913420 0.367899 0.663420
0 0.990081 0.231602 0.430117 0.309524 0.665464 0.423160 0.438233 0.535714 0.357980 0.265152 0.633904 0.053030 0.000000 0.532468 0.379621 0.337662 0.352570 0.677489
7 0.313796 0.361472 0.314698 0.384199 0.325518 0.396104 0.402164 0.419913 0.412083 0.374459 0.393147 0.357143 0.343553 0.344156
8 0.664563 0.115801 0.659152 0.163420 0.763751 0.178571 0.770063 0.199134 0.780884 0.198052 0.786294 0.149351
0 0.294860 0.655844 0.237151 0.620130 0.129847 0.733766 0.000000 0.798701 0.000000 0.931818 0.295762 0.730519
8 0.701533 0.082251 0.696123 0.117965 0.853922 0.168831 0.857529 0.121212
1 0.000000 0.312771 0.080252 0.229437 0.385032 0.146104 0.408476 0.082251 0.525699 0.096320 0.536519 0.025974 0.000000 0.007576
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15 0.770063 0.237013 0.762849 0.179654 0.589720 0.189394 0.660956 0.250000
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15 0.664563 0.115801 0.659152 0.163420 0.763751 0.178571 0.770063 0.199134 0.780884 0.198052 0.786294 0.149351
15 0.701533 0.082251 0.696123 0.117965 0.853922 0.168831 0.857529 0.121212
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5 0.116923 0.277197 0.407692 0.486402 0.953077 0.501046
1 0.115385 0.571130 0.220000 0.707113 0.946154 0.696653 0.919231 0.619247 0.987692 0.608787
6 0.000000 0.000000 0.000000 0.848326 0.999231 0.902720 0.999231 0.000000
4 0.677692 0.007322 0.798462 0.086820 0.750769 0.105649 0.750769 0.184100 0.839231 0.183054 0.943077 0.241632 0.950000 0.087866 0.823077 0.002092
1 0.513333 0.007347 0.999333 0.876735 0.903333 0.495510
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1 0.800000 0.858594 0.275000 0.858984 0.263542 0.963281 0.825000 0.989062
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8 0.820312 0.286328 0.748958 0.296094 0.748958 0.320312 0.818229 0.309375
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8 0.477006 0.672078 0.483318 0.688312 0.596032 0.706710 0.601443 0.658009 0.517583 0.636364
8 0.588819 0.758658 0.578900 0.701299 0.412083 0.734848 0.477908 0.785714
15 0.770965 0.543290 0.630298 0.542208 0.594229 0.570346 0.591524 0.588745 0.770965 0.594156
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15 0.522994 0.629870 0.663661 0.655844 0.670875 0.611472 0.570784 0.590909
15 0.477006 0.672078 0.483318 0.688312 0.596032 0.706710 0.601443 0.658009 0.517583 0.636364
15 0.588819 0.758658 0.578900 0.701299 0.412083 0.734848 0.477908 0.785714
0 0.190000 0.174000 0.341250 0.416000 0.180000 0.812000 0.807500 0.808000 0.826250 0.468000 0.475000 0.486000 0.827500 0.264000 0.333750 0.312000 0.821250 0.102000
3 0.817500 0.528000 0.760000 0.530000 0.758750 0.596000 0.790000 0.612000 0.817500 0.612000
6 0.191250 0.158000 0.180000 0.814000 0.808750 0.808000 0.837500 0.444000 0.577500 0.452000 0.846250 0.240000 0.333750 0.316000 0.823750 0.102000

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DC-Ops: Data Center Components Dataset

On-device data center operations assistant dataset for the Qualcomm x Meta ExecuTorch Hackathon.

Overview

319 images of data center infrastructure (server racks, NVIDIA NVL72, cables, ports, etc.)
3,045 polygon annotations in YOLO-seg format
16 component classes auto-labeled with Grounding DINO + SAM, for fine-tuning YOLOv8n-seg

Classes

ID	Class	Count
0	server rack	rack enclosures
1	compute tray	GPU/CPU trays (NVL72: 18 per rack)
2	NVLink switch tray	NVLink switches
3	network switch	TOR / Ethernet / InfiniBand
4	power shelf	PSU banks
5	cable	copper, fiber, power
6	network port	OSFP, QSFP, Ethernet
7	LED indicator	status LEDs
8	label	serial numbers, asset tags
9	fan	cooling fans
10	cooling manifold	liquid cooling pipes
11	cable cartridge	NVLink cable cartridge
12	power connector	power plugs, bus bar
13	drive bay	NVMe drive slots
14	management port	BMC RJ45, serial console
15	DPU	BlueField DPU / ConnectX NIC

Trained Model

models/dc_ops_yolov8n_seg.pt — YOLOv8n-seg fine-tuned on this dataset (6.5MB)

Box mAP50: 0.232
Top classes: compute tray (0.57), network port (0.51), cable (0.27)
Target device: Samsung Galaxy S25 Ultra (Snapdragon 8 Elite, SM8750)

Label Format

YOLO-seg format: class_id x1 y1 x2 y2 ... xn yn (normalized polygon coordinates)

Pipeline

Brightdata scraping → Grounding DINO + SAM auto-labeling → YOLOv8n-seg fine-tuning → ExecuTorch .pte export → Snapdragon NPU

Usage

from ultralytics import YOLO
model = YOLO("models/dc_ops_yolov8n_seg.pt")
results = model("path/to/server_rack_image.jpg")

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