Hugging Face's logo

beingbatman
/
MAE-CT-CPC-Dicotomized-v6-tricot

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
Model card Files Community
MAE-CT-CPC-Dicotomized-v6-tricot / README.md
beingbatman's picture
beingbatman
Model save
f922e0e verified
preview code
|
raw
history blame contribute delete
4.67 kB
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-v6-tricot
    results: []

MAE-CT-CPC-Dicotomized-v6-tricot

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: 2.5595
  • Accuracy: 0.2564

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: 3950

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.1005 0.0203 80 1.1025 0.3191
1.1073 1.0203 160 1.1015 0.4043
1.117 2.0203 240 1.1486 0.2340
1.0481 3.0203 320 1.1603 0.1702
1.0614 4.0203 400 1.2154 0.2979
0.9155 5.0203 480 1.1238 0.3830
0.9109 6.0203 560 1.1734 0.4468
0.6969 7.0203 640 1.2588 0.4468
0.6381 8.0203 720 1.2711 0.4255
0.5455 9.0203 800 1.3974 0.3830
0.4878 10.0203 880 1.2367 0.4468
0.3125 11.0203 960 1.5500 0.4468
0.5886 12.0203 1040 1.7877 0.3191
0.1826 13.0203 1120 1.8124 0.3404
0.3447 14.0203 1200 1.9852 0.4681
0.2065 15.0203 1280 2.3935 0.4043
0.3104 16.0203 1360 2.9981 0.3191
0.3517 17.0203 1440 2.5522 0.3830
0.0988 18.0203 1520 3.1463 0.4468
0.0532 19.0203 1600 2.8538 0.4468
0.1791 20.0203 1680 3.0306 0.4468
0.1584 21.0203 1760 3.4847 0.3830
0.016 22.0203 1840 3.4121 0.3191
0.0012 23.0203 1920 3.8550 0.3617
0.0005 24.0203 2000 3.9055 0.4043
0.0023 25.0203 2080 4.0501 0.4255
0.0009 26.0203 2160 4.2001 0.3404
0.0004 27.0203 2240 4.0130 0.3830
0.0162 28.0203 2320 4.0468 0.4043
0.0073 29.0203 2400 4.1919 0.4255
0.0012 30.0203 2480 4.0004 0.4255
0.0125 31.0203 2560 4.1151 0.3830
0.0005 32.0203 2640 4.3282 0.3830
0.0098 33.0203 2720 4.4689 0.3830
0.0036 34.0203 2800 4.3354 0.4043
0.0002 35.0203 2880 4.4605 0.3617
0.0002 36.0203 2960 4.1586 0.4255
0.0002 37.0203 3040 4.2574 0.4043
0.0002 38.0203 3120 4.6391 0.3830
0.0008 39.0203 3200 4.5526 0.3617
0.0001 40.0203 3280 4.5658 0.3830
0.0001 41.0203 3360 4.5712 0.3830
0.0001 42.0203 3440 4.6019 0.3830
0.0002 43.0203 3520 4.5915 0.4043
0.0001 44.0203 3600 4.6868 0.3830
0.0001 45.0203 3680 4.6619 0.3830
0.0002 46.0203 3760 4.7142 0.3617
0.0002 47.0203 3840 4.6525 0.3617
0.0001 48.0203 3920 4.6684 0.3617
0.0001 49.0076 3950 4.6668 0.3617

Framework versions

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