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ConvNextV2-large-DogBreed

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ConvNextV2-large-DogBreed / README.md
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metadata
license: apache-2.0
base_model: facebook/convnextv2-large-22k-224
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: ConvNextV2-large-DogBreed
    results: []

ConvNextV2-large-DogBreed

This model is a fine-tuned version of facebook/convnextv2-large-22k-224 on dog breed classification dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5469
  • Accuracy: 0.9139

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: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 512
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
4.8578 1.0 13 4.6940 0.0671
4.6332 1.99 26 4.4169 0.1784
4.4095 2.99 39 4.1105 0.3485
3.8841 3.98 52 3.7581 0.5198
3.5964 4.98 65 3.3647 0.6647
3.2781 5.97 78 2.9442 0.7677
2.6006 6.97 91 2.5252 0.8180
2.2638 7.96 104 2.1256 0.8467
1.9609 8.96 117 1.7626 0.8766
1.3962 9.95 130 1.4453 0.9042
1.143 10.95 143 1.1818 0.9102
0.9423 11.94 156 0.9697 0.9138
0.7674 12.94 169 0.8097 0.9174
0.5007 13.93 182 0.6922 0.9186
0.4097 14.93 195 0.5999 0.9162
0.3392 16.0 209 0.5174 0.9269
0.2285 17.0 222 0.4685 0.9257
0.184 17.99 235 0.4337 0.9210
0.1587 18.99 248 0.4058 0.9257
0.1112 19.98 261 0.3824 0.9222
0.0967 20.98 274 0.3712 0.9150
0.0838 21.97 287 0.3584 0.9186
0.0665 22.97 300 0.3468 0.9174
0.0589 23.96 313 0.3428 0.9186
0.0551 24.96 326 0.3364 0.9186
0.0512 25.95 339 0.3334 0.9162
0.0441 26.95 352 0.3278 0.9210
0.0428 27.94 365 0.3275 0.9150
0.0387 28.94 378 0.3237 0.9210
0.036 29.93 391 0.3242 0.9150
0.0337 30.93 404 0.3204 0.9186
0.0328 32.0 418 0.3176 0.9198
0.0304 33.0 431 0.3183 0.9162
0.0283 33.99 444 0.3150 0.9210
0.029 34.99 457 0.3168 0.9174
0.0264 35.98 470 0.3146 0.9174
0.0259 36.98 483 0.3162 0.9174
0.0258 37.97 496 0.3126 0.9186
0.0251 38.97 509 0.3131 0.9174
0.0239 39.96 522 0.3145 0.9186
0.0234 40.96 535 0.3120 0.9198
0.023 41.95 548 0.3102 0.9198
0.0226 42.95 561 0.3123 0.9198
0.0222 43.94 574 0.3140 0.9186
0.0225 44.94 587 0.3119 0.9186
0.0215 45.93 600 0.3106 0.9198
0.0209 46.93 613 0.3113 0.9198
0.0212 48.0 627 0.3115 0.9198
0.021 49.0 640 0.3113 0.9198
0.0212 49.76 650 0.3113 0.9198

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1