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MAE-CT-CPC-Dicotomized-v8-n0-m1

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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MAE-CT-CPC-Dicotomized-v8-n0-m1 / README.md
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: MAE-CT-CPC-Dicotomized-v8-n0-m1
    results: []

MAE-CT-CPC-Dicotomized-v8-n0-m1

This model is a fine-tuned version of MCG-NJU/videomae-large-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4681
  • Accuracy: 0.7907

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 3500

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6769 0.02 70 0.6814 0.5938
0.7223 1.02 140 0.6953 0.5938
0.6628 2.02 210 0.6335 0.625
0.5096 3.02 280 0.6514 0.625
0.4739 4.02 350 0.6358 0.6562
0.4554 5.02 420 0.6272 0.6562
0.4818 6.02 490 0.7727 0.5938
0.4129 7.02 560 0.8222 0.6875
0.6301 8.02 630 0.8041 0.625
0.3809 9.02 700 0.8721 0.625
0.8071 10.02 770 1.1092 0.625
0.1888 11.02 840 1.1556 0.625
0.3762 12.02 910 1.3499 0.625
0.3502 13.02 980 1.5333 0.6562
0.1027 14.02 1050 1.6249 0.6562
0.177 15.02 1120 1.3758 0.6562
0.0998 16.02 1190 1.9514 0.6562
0.1749 17.02 1260 1.9120 0.6562
0.0145 18.02 1330 2.1036 0.625
0.0038 19.02 1400 2.0288 0.625
0.1262 20.02 1470 2.0193 0.6875
0.0203 21.02 1540 2.1937 0.6562
0.0002 22.02 1610 2.2922 0.625
0.0017 23.02 1680 2.1569 0.6562
0.0049 24.02 1750 2.2573 0.625
0.0231 25.02 1820 2.1460 0.6875
0.0001 26.02 1890 2.3566 0.6562
0.0001 27.02 1960 2.3822 0.5938
0.0001 28.02 2030 2.3178 0.6562
0.0004 29.02 2100 2.5492 0.625
0.0003 30.02 2170 2.7648 0.625
0.0001 31.02 2240 2.3949 0.625
0.0001 32.02 2310 2.4107 0.6562
0.0001 33.02 2380 2.6099 0.5938
0.0001 34.02 2450 2.8574 0.5625
0.0001 35.02 2520 2.5808 0.5938
0.0001 36.02 2590 2.6246 0.5938
0.0001 37.02 2660 2.7051 0.5938
0.0001 38.02 2730 2.5046 0.5938
0.0001 39.02 2800 2.5003 0.5938
0.0 40.02 2870 2.5460 0.625
0.0 41.02 2940 2.5397 0.625
0.0 42.02 3010 2.5384 0.625
0.0 43.02 3080 2.4849 0.625
0.0 44.02 3150 2.5847 0.6562
0.0 45.02 3220 2.5829 0.6562
0.0 46.02 3290 2.5809 0.6562
0.0 47.02 3360 2.5756 0.625
0.0001 48.02 3430 2.4744 0.6562
0.0 49.02 3500 2.4720 0.6562

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.0.1+cu117
  • Datasets 3.0.1
  • Tokenizers 0.20.0