Computer Vision Model Evaluation Metrics for Validation and Test Sets

Quantitative evaluation results for computer vision models on an original dataset. The 9.5 KB Excel file contains metrics for bounding box and keypoint detection expressed as percentages, alongside inference time, best epoch, and training time. Authored by Avinash Bansal and last updated on April 21, 2026, it is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Benchmarking model generalization based on separate validation and test set metrics
• Comparing computational efficiency across models based on reported inference and training times
• Analyzing object detection performance based on bounding box accuracy percentages
• Evaluating pose estimation models based on keypoint detection results

Strengths

• Metrics are reported separately for validation and test sets, allowing assessment of generalization
• Includes computational efficiency metrics like inference time, best epoch, and training time
• Shared under a permissive CC-BY-4.0 license
• File size is 9.5 KB, indicating a concise and focused summary

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• Description metadata is limited; actual data quality requires manual inspection after download

Provenance

Source

figshare

Collection Method

Likely generated from model training and evaluation experiments.

Time Range

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Freshness

Last updated 2026-04-21 17:44:21; freshness should be verified

Geography

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