README.md

metadata

license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet-50-2024_09_13-batch-size32_epochs150_freeze
    results: []

resnet-50-2024_09_13-batch-size32_epochs150_freeze

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

• Loss: nan
• F1 Micro: 0.0002
• F1 Macro: 0.0002
• Roc Auc: 0.4995
• Accuracy: 0.0003
• Learning Rate: 0.0001

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: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 150
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Roc Auc	Accuracy	Rate
No log	1.0	273	nan	0.0	0.0	0.4995	0.0	0.001
0.0	2.0	546	nan	0.0003	0.0004	0.4993	0.0007	0.001
0.0	3.0	819	nan	0.0008	0.0010	0.4994	0.0017	0.001
0.0	4.0	1092	nan	0.0	0.0	0.4991	0.0	0.001
0.0	5.0	1365	nan	0.0005	0.0006	0.4994	0.0010	0.001
0.0	6.0	1638	nan	0.0002	0.0002	0.4993	0.0003	0.001
0.0	7.0	1911	nan	0.0	0.0	0.4993	0.0	0.0001
0.0	8.0	2184	nan	0.0002	0.0002	0.4993	0.0003	0.0001
0.0	9.0	2457	nan	0.0	0.0	0.4994	0.0	0.0001
0.0	10.0	2730	nan	0.0003	0.0004	0.4994	0.0007	0.0001
0.0	11.0	3003	nan	0.0	0.0	0.4994	0.0	0.0001

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1