---
library_name: transformers
license: apache-2.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Hibernates-MEA-R2-V0
  results: []
---

# Hibernates-MEA-R2-V0

An advanced AI system for visual sequence processing, extending the capabilities of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/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