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

Cycle Loss	Epoch	Step	Validation Loss	Success Rate
0.6186	0.02	65	0.7367	0.5435
0.5974	1.02	130	0.8185	0.5435
0.5491	2.02	195	0.8372	0.5435
0.6156	3.02	260	0.6620	0.5870
0.6255	4.02	325	0.6835	0.5435
0.438	5.02	390	1.2116	0.5435
0.4653	6.02	455	0.6002	0.5652
0.5876	7.02	520	0.4894	0.8043
0.3801	8.02	585	0.8324	0.5435
0.4474	9.02	650	1.1581	0.5652
0.694	10.02	715	0.5354	0.7174
0.4773	11.02	780	0.6181	0.6957
0.6208	12.02	845	0.5677	0.7609
0.344	13.02	910	0.7452	0.6087
0.254	14.02	975	0.6362	0.7391
0.4578	15.02	1040	0.8304	0.6957
0.3954	16.02	1105	0.6049	0.7609
0.248	17.02	1170	0.9506	0.6739
0.1334	18.02	1235	1.1876	0.6739
0.534	19.02	1300	0.6296	0.7391
0.3556	20.02	1365	1.3007	0.6957
0.5439	21.02	1430	1.5066	0.6739
0.4107	22.02	1495	0.9273	0.8043
0.61	23.02	1560	1.0008	0.7174
0.6482	24.02	1625	0.7548	0.7609
0.199	25.02	1690	0.7917	0.7826
0.1185	26.02	1755	0.7529	0.7826
0.3886	27.02	1820	0.8627	0.7609
0.0123	28.02	1885	1.3886	0.7174
0.5328	29.02	1950	1.2803	0.6957
0.2961	30.02	2015	1.4397	0.7174
0.1192	31.02	2080	2.2563	0.6304
0.145	32.02	2145	1.0465	0.7609
0.0924	33.02	2210	0.9859	0.7826
0.1016	34.02	2275	1.0758	0.7826
0.1894	35.02	2340	1.2088	0.7609
0.2657	36.02	2405	1.5409	0.7391
0.1235	37.02	2470	1.2736	0.7609
0.1539	38.02	2535	1.2608	0.7609
0.03	39.02	2600	1.2058	0.7609
0.1447	40.02	2665	1.1072	0.7609
0.0888	41.02	2730	1.1454	0.7826
0.0016	42.02	2795	1.1194	0.7826
0.1489	43.02	2860	1.2170	0.7609
0.0004	44.02	2925	1.1894	0.7609
0.0004	45.02	2990	1.3329	0.7391
0.0014	46.02	3055	1.1887	0.7609
0.1675	47.02	3120	1.2652	0.7391
0.012	48.02	3185	1.3228	0.7391
0.0475	49.02	3250	1.3507	0.7391

System Versions

• Transformers 4.46.2
• Pytorch 2.0.1+cu117
• Datasets 3.0.1
• Tokenizers 0.20.0