metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: blurred_landmarks
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: landmarks
          split: validation
          args: landmarks
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9645365168539326

blurred_landmarks

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1152
Accuracy: 0.9645

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6588	1.0	357	0.6460	0.7707
0.3752	2.0	714	0.2969	0.8933
0.3275	3.0	1071	0.1912	0.9319
0.2183	4.0	1429	0.1794	0.9305
0.2133	5.0	1786	0.1638	0.9414
0.1984	6.0	2143	0.1322	0.9484
0.1409	7.0	2500	0.1304	0.9529
0.1864	8.0	2858	0.1212	0.9572
0.1778	9.0	3215	0.1216	0.9540
0.1734	10.0	3572	0.1129	0.9593
0.1349	11.0	3929	0.1127	0.9614
0.1057	12.0	4287	0.1177	0.9582
0.1434	13.0	4644	0.1153	0.9603
0.0832	14.0	5001	0.1264	0.9593
0.0963	15.0	5358	0.1146	0.9607
0.0642	16.0	5716	0.1135	0.9635
0.0763	17.0	6073	0.1210	0.9614
0.0432	18.0	6430	0.1162	0.9645
0.0618	19.0	6787	0.1269	0.9600
0.049	19.99	7140	0.1152	0.9645

Framework versions

Transformers 4.30.0.dev0
Pytorch 1.13.0
Datasets 2.10.1
Tokenizers 0.11.0