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Vision Model Benchmark Template

Overview

This is an essential template to benckmark Vision Models based on Visual Recognition tasks capability and efficiency.

1. Model Overview

  • Model Name:
  • Model Version:
  • Model Source: (e.g., Hugging Face, custom, pre-trained)
  • Total Parameters:
  • Architecture Type: (e.g., CNN, Transformer-based, hybrid)
  • Input Size: (e.g., 224x224, 512x512)
  • Pre-training Details:
  • Dataset: (e.g., ImageNet, proprietary dataset)
  • Transfer Learning: (Yes/No, mention layers fine-tuned)
  • Framework Used: (e.g., PyTorch, TensorFlow, ONNX)
  • License: (e.g., Apache 2.0, MIT)

2. Benchmark Tasks

Visual Recognition Tasks:

Image Classification

  • Dataset: (e.g., ImageNet, CIFAR-10)
  • Accuracy (%):
  • Top-5 Accuracy (%):
  • Inference Time: (ms/image)
  • Throughput: (Images/sec)

Object Detection

  • Dataset: (e.g., COCO, VOC)
  • mAP (%):
  • Average Precision (for different IoU thresholds):
  • FPS (Frames per Second):
  • Latency (ms/frame):

Image Segmentation

  • Dataset: (e.g., PASCAL VOC, Cityscapes)
  • mIoU (%):
  • Pixel Accuracy (%):
  • Latency (ms/image):
  • FPS:

Pose Estimation

  • Dataset:
  • Average Precision (%):
  • Latency (ms):
  • Keypoint Localization Error: (if applicable)

3. Efficiency Metrics

  • Inference Speed: (ms/image or FPS, across batch sizes)
  • Throughput: (images/sec or frames/sec, across batch sizes)
  • Memory Footprint: (MB/GB, during inference)
  • Model Size: (MB/GB on disk)
  • Hardware Used:
  • CPU/GPU Type: (e.g., Intel i9, NVIDIA RTX 3090)
  • RAM: (GB)
  • Compute Environment: (e.g., local, cloud: AWS, GCP, Colab)
  • Power Consumption: (Watt, if available)
  • Quantization/Pruning:
  • Was the model quantized? (Yes/No)
  • Quantization Details: (e.g., INT8, dynamic, etc.)
  • Impact on Accuracy and Speed: (before vs. after quantization)

4. Training Information (If relevant)

  • Training Dataset: (size, classes)
  • Training Time: (hours or days)
  • Hardware Environment for Training:
  • GPUs Used: (model, count)
  • Distributed Training: (Yes/No)
  • Training Batch Size:
  • Learning Rate:
  • Optimizer: (e.g., Adam, SGD)

5. Resource Utilization

  • Batch Size Tested: (for inference)
  • Latency per Batch: (ms)
  • GPU Utilization: (%)
  • RAM Utilization: (GB)
  • Disk I/O: (if applicable)
  • Model Scalability: (performance across varying input sizes, batch sizes)

6. Model Robustness

  • Adversarial Testing: (Yes/No)
  • If tested, provide accuracy drop against adversarial attacks.
  • Robustness to Noise:
  • Performance in noisy environments (image perturbations, occlusions).
  • Generalization:
  • How well does the model perform on out-of-distribution data?

7. Leaderboard Positioning

  • Rank your model in comparison with others based on:
  • Accuracy
  • Efficiency
  • Throughput
  • Memory Usage
  • Power Efficiency
  • Performance-to-Cost Ratio

Key Metrics for Leaderboard:

  • Accuracy (for various tasks like classification, segmentation, detection).
  • Inference Speed (time taken for single inference, ms).
  • Throughput (images/sec or FPS).
  • Resource Utilization (GPU/CPU, RAM usage).
  • Power Efficiency (if available).
  • Cost Performance Ratio: (efficiency in context of hardware cost).

8. Comparison of Methods

  • Compare model types: (e.g., ResNet vs. Vision Transformer, YOLO vs. Faster R-CNN).
  • Comparative performance on different datasets.
  • Trade-offs between accuracy, speed, and memory consumption.

9. Limitations and Challenges

  • Dataset Bias: (mention if dataset limitations affect the model).
  • Edge Cases:
  • Performance in rare or difficult scenarios (e.g., extreme lighting conditions).
  • Scalability:
  • How does the model perform when scaled for larger datasets or bigger inputs?

10. Output: Vision Leaderboard (HF space)