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These are benchmarks results for Guided-Object Inference Slicing evaluation. F1-Det aplied model directly on dataset and saved prediction results in COCO.json format. GOIS-Det applied innovative framework with model as slicing inference and saved prediction results in COCO.json format. TOD improved

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MIT License - All rights reserved to the author. This project may be used for study and educational purposes, but redistribution, redevelopment, or use of the code for personal or commercial purposes is strictly prohibited without the author's written consent.

Enhancing Tiny Object Detection by applying Guided Object Inference Slicing(GOIS) Complete Benchmarks Evaluated results

Open In Colab

Testing Code Steps for Section 1,2,3

1. Download Required Files

  • Ground Truth (GT): Download the COCO.json file containing the ground truth annotations.
  • FI-Det COCO.json: Download the Full Inference Detection results in COCO.json format.
  • OGIS-Det COCO.json: Download the Object Guided Inference Slicing Detection results in COCO.json format.
  • Upload the files to your preferred storage location (e.g., Google Drive).
  • Follow step 6,7 in

Section 1: Without Fine Tuning 15% Dataset Subset(970 Images) Inference Results VisDrone2019Train Dataset

Comparative Results for FI-Det and GOIS-Det

This table presents the Average Precision (AP) and Average Recall (AR) metrics for seven models. Each model includes rows for FI-Det, GOIS-Det, and the percentage improvement achieved by GOIS over FI-Det. Downloadable links for FI-Det and GOIS-Det results are included. Ground Truth COCO for this evaluation is available at | 15% Train Dataset GT

Model AP-Small AR-Small AP-Medium AP-Large AR@1 AR@10 AR@100 AR-Medium AR-Large [email protected] [email protected] [email protected]
YOLO11 FI-Det, GOIS-Det 0.002 / 0.01 0.004 / 0.033 0.023 / 0.057 0.057 / 0.096 0.012 / 0.027 0.027 / 0.068 0.029 / 0.087 0.049 / 0.14 0.109 / 0.193 0.012 / 0.033 0.018 / 0.051 0.013 / 0.034
% Improve ↑ 388.07% ↑ 718.09% ↑ 152.67% ↑ 69.75% ↑ 128.48% ↑ 154.67% ↑ 194.56% ↑ 188.51% ↑ 77.16% ↑ 164.89% ↑ 183.25% ↑ 160.64%
RT-DETR-L FI-Det, GOIS-Det 0.011 / 0.022 0.044 / 0.103 0.067 / 0.095 0.134 / 0.149 0.032 / 0.046 0.081 / 0.116 0.101 / 0.171 0.144 / 0.225 0.245 / 0.273 0.043 / 0.061 0.067 / 0.094 0.044 / 0.063
% Improve ↑ 103.75% ↑ 135.04% ↑ 42.48% ↑ 10.79% ↑ 40.91% ↑ 42.27% ↑ 68.99% ↑ 55.97% ↑ 11.44% ↑ 41.08% ↑ 39.79% ↑ 43.49%
YOLOv10 FI-Det, GOIS-Det 0.002 / 0.008 0.002 / 0.027 0.018 / 0.056 0.063 / 0.093 0.013 / 0.026 0.025 / 0.061 0.027 / 0.076 0.038 / 0.125 0.118 / 0.185 0.012 / 0.031 0.017 / 0.048 0.013 / 0.033
% Improve ↑ 445.53% ↑ 1052.31% ↑ 202.98% ↑ 49.03% ↑ 101.74% ↑ 141.36% ↑ 182.78% ↑ 231.55% ↑ 56.42% ↑ 155.67% ↑ 181.62% ↑ 158.41%
YOLOv8n FI-Det, GOIS-Det 0.003 / 0.013 0.004 / 0.039 0.024 / 0.053 0.054 / 0.097 0.015 / 0.028 0.029 / 0.067 0.032 / 0.084 0.05 / 0.134 0.122 / 0.193 0.014 / 0.03 0.02 / 0.047 0.014 / 0.032
% Improve ↑ 360.46% ↑ 805.84% ↑ 121.22% ↑ 79.27% ↑ 80.94% ↑ 128.38% ↑ 159.81% ↑ 167.54% ↑ 58.05% ↑ 120.03% ↑ 138.30% ↑ 133.27%
YOLOv8s-WorldV2 FI-Det, GOIS-Det 0.004 / 0.016 0.011 / 0.048 0.042 / 0.068 0.090 / 0.101 0.021 / 0.036 0.042 / 0.084 0.046 / 0.103 0.075 / 0.159 0.179 / 0.197 0.023 / 0.040 0.034 / 0.060 0.023 / 0.043
% Improve ↑ 287.28% ↑ 325.65% ↑ 62.42% ↑ 11.68% ↑ 69.48% ↑ 100.75% ↑ 125.29% ↑ 112.30% ↑ 10.25% ↑ 77.07% ↑ 73.88% ↑ 87.13%

Section 2: Fine Tuning Models with 10 epoches Visdrone Traning and then Inference results on Full Dataset(6,471 Images) VisDrone2019Train

Comparative Results for FI-Det and GOIS-Det

This table presents the Average Precision (AP) and Average Recall (AR) metrics for five models (YOLO11, YOLOv10, YOLOv9, YOLOv8, YOLOv5). Each model includes three rows: FI-Det results, GOIS-Det results, and % improvement achieved by GOIS. Downloadable links for FI-Det and GOIS-Det results are included in the first column next to the model name. Ground Truth COCO for this evaluation available at | FullTraineDatasetGT

Model AP-Small AR-Small AP-Medium AP-Large AR@1 AR@10 AR@100 AR-Medium AR-Large F1 Score [email protected] [email protected] [email protected]
YOLO11 FI-Det 0.024 0.035 0.159 0.283 0.045 0.112 0.137 0.208 0.349 0.170 0.120 0.171 0.119
YOLO11 - GOIS-Det 0.071 0.133 0.164 0.151 0.053 0.152 0.207 0.273 0.227 0.470 0.134 0.192 0.132
% Improvement ↑ 195.83% ↑ 278.66% ↑ 3.14% ↓ 46.64% ↑ 18.81% ↑ 35.46% ↑ 51.17% ↑ 31.44% ↓ 34.90% ↑ 176.47% ↑ 11.67% ↑ 12.87% ↑ 10.92%
YOLOv10 FI-Det 0.022 0.029 0.133 0.222 0.041 0.097 0.117 0.178 0.278 0.17 0.091 0.140 0.100
YOLOv10 - GOIS-Det 0.061 0.110 0.130 0.101 0.047 0.127 0.171 0.218 0.159 0.44 0.099 0.156 0.107
% Improvement ↑ 177.27% ↑ 279.22% ↓ 2.26% ↓ 54.95% ↑ 14.18% ↑ 31.01% ↑ 46.09% ↑ 22.50% ↓ 42.82% ↑ 158.82% ↑ 8.79% ↑ 11.43% ↑ 7.00%
YOLOv9 FI-Det 0.079 0.051 0.320 0.472 0.027 0.060 0.069 0.116 0.225 0.051 0.039 0.054 0.043
YOLOv9 - GOIS-Det 0.130 0.074 0.242 0.171 0.036 0.086 0.111 0.177 0.233 0.075 0.051 0.074 0.056
% Improvement ↑ 64.56% ↑ 35.76% ↓ 24.38% ↓ 63.77% ↑ 32.61% ↑ 41.88% ↑ 59.89% ↑ 52.01% ↑ 3.89% ↑ 59.89% ↓ 11.79% ↓ 8.39% ↓ 14.22%
YOLOv8 FI-Det 0.025 0.032 0.158 0.290 0.046 0.113 0.136 0.209 0.365 0.17 0.108 0.168 0.118
YOLOv8 - GOIS-Det 0.070 0.044 0.163 0.149 0.056 0.158 0.211 0.281 0.220 0.082 0.121 0.193 0.130
% Improvement ↑ 180.00% ↑ 140.15% ↑ 3.16% ↓ 48.62% ↑ 22.33% ↑ 40.05% ↑ 55.92% ↑ 34.65% ↓ 39.72% ↑ 168.36% ↑ 12.04% ↑ 14.88% ↑ 10.17%
YOLOv5 FI-Det 0.019 0.026 0.138 0.270 0.040 0.098 0.119 0.178 0.278 0.17 0.096 0.150 0.104
YOLOv5 - GOIS-Det 0.059 0.040 0.150 0.134 0.050 0.139 0.188 0.254 0.205 0.070 0.109 0.174 0.116
% Improvement ↑ 210.53% ↑ 188.07% ↑ 8.70% ↓ 50.37% ↑ 26.22% ↑ 42.71% ↑ 58.12% ↑ 40.05% ↓ 37.62% ↑ 193.98% ↑ 13.54% ↑ 16.00% ↑ 11.54%

Section 3: NO Fine Tuning Five Models Inference results on Full Dataset(6,471 Images) VisDrone2019Train

his table presents the Average Precision (AP) and Average Recall (AR) metrics for five models (YOLO11, YOLOv10, YOLOv9, YOLOv8, YOLOv5). Each model includes three rows: FI-Det results, GOIS-Det results, and % improvement achieved by GOIS. Downloadable links for FI-Det and GOIS-Det results are included in the first column next to the model name. Ground Truth COCO for this evaluation available at | FullTraineDatasetGT

Model AP-Small AR-Small AP-Medium AP-Large AR@1 AR@10 AR@100 AR-Medium AR-Large F1 Score [email protected] [email protected] [email protected]
YOLO11 FI-Det, 0.024 0.035 0.159 0.283 0.045 0.112 0.137 0.208 0.349 0.170 0.120 0.171 0.119
YOLO11 -GOIS-Det 0.071 0.133 0.164 0.151 0.053 0.152 0.207 0.273 0.227 0.470 0.134 0.192 0.132
% Improvement ↑ 196.90% ↑ 278.66% ↑ 2.94% ↓ 46.71% ↑ 18.81% ↑ 35.46% ↑ 51.17% ↑ 31.44% ↓ 34.90% ↑ 176.47% ↑ 12.01% ↑ 12.38% ↑ 11.26%
YOLOv10 FI-Det, 0.022 0.029 0.133 0.222 0.041 0.097 0.117 0.178 0.278 0.17 0.091 0.140 0.100
YOLOv10 - GOIS-Det 0.061 0.110 0.130 0.101 0.047 0.127 0.171 0.218 0.159 0.44 0.099 0.156 0.107
% Improvement ↑ 176.54% ↑ 279.22% ↓ 2.30% ↓ 54.85% ↑ 14.18% ↑ 31.01% ↑ 46.09% ↑ 22.50% ↓ 42.82% ↑ 158.82% ↑ 8.88% ↑ 11.40% ↑ 7.08%
YOLOv9 FI-Det, 0.039 0.051 0.070 0.139 0.027 0.060 0.069 0.116 0.225 0.051 0.039 0.054 0.043
YOLOv9 -GOIS-Det 0.051 0.074 0.089 0.125 0.036 0.086 0.111 0.177 0.233 0.075 0.051 0.074 0.056
% Improvement ↑ 30.25% ↑ 35.76% ↑ 26.16% ↓ 10.20% ↑ 32.61% ↑ 41.88% ↑ 59.89% ↑ 52.01% ↑ 3.89% ↑ 59.89% ↑ 30.25% ↑ 35.76% ↑ 31.27%
YOLOv8 FI-Det, GOIS-Det 0.012 0.029 0.022 0.061 0.013 0.028 0.030 0.048 0.124 0.029 0.012 0.018 0.012
YOLOv8 - GOIS-Det 0.029 0.044 0.051 0.092 0.026 0.065 0.082 0.134 0.191 0.082 0.029 0.044 0.030
% Improvement ↑ 130.33% ↑ 140.15% ↑ 131.56% ↑ 50.25% ↑ 100.68% ↑ 132.76% ↑ 168.36% ↑ 175.62% ↑ 53.47% ↑ 168.36% ↑ 130.33% ↑ 140.15% ↑ 142.12%
YOLOv5 FI-Det, 0.010 0.026 0.019 0.052 0.011 0.022 0.024 0.037 0.115 0.026 0.010 0.014 0.010
YOLOv5 -GOIS-Det 0.026 0.040 0.049 0.086 0.024 0.055 0.070 0.121 0.180 0.070 0.026 0.040 0.027
% Improvement ↑ 166.92% ↑ 188.07% ↑ 164.97% ↑ 66.55% ↑ 115.96% ↑ 149.03% ↑ 193.98% ↑ 226.48% ↑ 55.92% ↑ 193.98% ↑ 166.92% ↑ 188.07% ↑ 171.82%

Notes:

  • ↑ represents percentage improvement achieved by GOIS-Det over FI-Det.
  • ↓ represents performance degradation in GOIS-Det compared to FI-Det.

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These are benchmarks results for Guided-Object Inference Slicing evaluation. F1-Det aplied model directly on dataset and saved prediction results in COCO.json format. GOIS-Det applied innovative framework with model as slicing inference and saved prediction results in COCO.json format. TOD improved

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