Hibernates-MEA-R2-V0
An advanced AI system for visual sequence processing, extending the capabilities of MCG-NJU/videomae-large-finetuned-kinetics.
Key Performance Indicators:
Optimal Loss: 0.4894
Peak Accuracy: 80.43%
System Overview
Advanced AI architecture optimized for visual sequence understanding:
Core: Deep learning transformer system
Data Handling: Sequential frame processing
Main Function: Visual content categorization
Learning Cycles: 50 complete epochs
Results Summary:
Maximum Precision: 80.43% (epoch 7)
Consistent Performance: 75%+ maintained
Applications & Requirements
Core Applications
Visual sequence interpretation
Dynamic content analysis
Environmental context recognition
Time-series visual processing
Technical Considerations
Task-specific optimization
Computing needs: High-performance GPU
Memory constraints: 4-sample batching
Data format: Standardized input required
Development Data
Implementation Details:
Cycle Structure: 65 iterations per epoch
Development Span: 3250 total iterations
Assessment Methods: Dual metric system (loss/accuracy)
Progress Metrics:
Starting Point: 54% accuracy
Final Result: 73.91%
Best-case Loss: 0.4894
Implementation Specifications
Core Parameters
Implementation utilized the following configuration:
Learning Rate: 1e-05
Training Units: 4 per batch
Validation Units: 4 per batch
Random Seed: 42
Optimization: Advanced weight management with adamw_torch
Beta values: (0.9,0.999)
Epsilon: 1e-08
Rate Control: Linear adjustment
Warmup Ratio: 0.1
Total Iterations: 3250
Development Progress
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System Versions
Transformers 4.46.2
Pytorch 2.0.1+cu117
Datasets 3.0.1
Tokenizers 0.20.0
Last updated