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krismp
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emotion_recognition

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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emotion_recognition / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: emotion_recognition
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.175

emotion_recognition

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3469
  • Accuracy: 0.175

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 10 2.0721 0.125
No log 2.0 20 2.0633 0.125
No log 3.0 30 2.0038 0.125
No log 4.0 40 1.9097 0.125
No log 5.0 50 1.7412 0.125
No log 6.0 60 1.6189 0.05
No log 7.0 70 1.5343 0.0375
No log 8.0 80 1.4746 0.0688
No log 9.0 90 1.4330 0.0938
No log 10.0 100 1.4130 0.15
No log 11.0 110 1.3735 0.1062
No log 12.0 120 1.3516 0.1062
No log 13.0 130 1.2838 0.1375
No log 14.0 140 1.3058 0.1187
No log 15.0 150 1.3116 0.1
No log 16.0 160 1.3269 0.1313
No log 17.0 170 1.2624 0.1062
No log 18.0 180 1.3285 0.1187
No log 19.0 190 1.3490 0.1437
No log 20.0 200 1.2592 0.1375
No log 21.0 210 1.3600 0.0938
No log 22.0 220 1.2835 0.1313
No log 23.0 230 1.2842 0.1375
No log 24.0 240 1.2840 0.1
No log 25.0 250 1.2456 0.1313
No log 26.0 260 1.2960 0.1562
No log 27.0 270 1.3208 0.1375
No log 28.0 280 1.3207 0.1375
No log 29.0 290 1.2892 0.175
No log 30.0 300 1.2837 0.1812
No log 31.0 310 1.3548 0.1562
No log 32.0 320 1.4371 0.1437
No log 33.0 330 1.4219 0.1562
No log 34.0 340 1.4033 0.1875
No log 35.0 350 1.4505 0.1437
No log 36.0 360 1.2975 0.1562
No log 37.0 370 1.3906 0.1562
No log 38.0 380 1.3547 0.1688
No log 39.0 390 1.4706 0.1938
No log 40.0 400 1.3595 0.1625
No log 41.0 410 1.4236 0.1625
No log 42.0 420 1.4180 0.1812
No log 43.0 430 1.3993 0.1562
No log 44.0 440 1.4066 0.1625
No log 45.0 450 1.3760 0.175
No log 46.0 460 1.4221 0.1812
No log 47.0 470 1.3772 0.1625
No log 48.0 480 1.4265 0.2
No log 49.0 490 1.4716 0.1625
0.6962 50.0 500 1.3917 0.1625

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.13.3