| | --- |
| | license: mit |
| | task_categories: |
| | - tabular-classification |
| | language: |
| | - en |
| | tags: |
| | - artificial-intelligence |
| | - machine-learning |
| | - model-benchmark |
| | - deep-learning |
| | size_categories: |
| | - n<1K |
| | --- |
| | |
| | # AI Model Performance Benchmark Dataset |
| |
|
| | ## Description |
| | This dataset contains structured benchmark data of artificial intelligence models across multiple domains including computer vision, natural language processing, audio classification, multimodal systems, and edge AI. |
| |
|
| | It provides model specifications such as parameter size, training dataset scale, accuracy, F1 score, inference latency, memory usage, and estimated power consumption. |
| |
|
| | ## Columns |
| | - model_name: Name of the AI model |
| | - model_type: Category of AI system |
| | - parameters_million: Number of model parameters (in millions) |
| | - training_dataset_size_million: Training dataset size (in millions of samples) |
| | - accuracy_percent: Model accuracy percentage |
| | - f1_score: F1 performance score |
| | - inference_latency_ms: Inference speed in milliseconds |
| | - memory_usage_mb: Runtime memory usage in MB |
| | - power_consumption_watt: Estimated power consumption |
| |
|
| | ## Purpose |
| | This dataset is designed for: |
| | - AI performance benchmarking |
| | - Machine learning research simulation |
| | - Educational purposes |
| | - Model efficiency comparison studies |
| |
|
| | ## Format |
| | CSV structured dataset |
