resnet_50_base_aihub_model_py
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: 0.0987
- Accuracy: 0.9681
- Precision: 0.9712
- Recall: 0.9624
- F1: 0.9667
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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.5577 | 1.0 | 149 | 0.4027 | 0.8453 | 0.8514 | 0.8415 | 0.8435 |
| 0.323 | 2.0 | 299 | 0.2346 | 0.9097 | 0.9208 | 0.8962 | 0.9074 |
| 0.2467 | 3.0 | 448 | 0.1786 | 0.9303 | 0.9465 | 0.9216 | 0.9326 |
| 0.1953 | 4.0 | 598 | 0.1266 | 0.9573 | 0.9591 | 0.9483 | 0.9535 |
| 0.1456 | 4.98 | 745 | 0.0987 | 0.9681 | 0.9712 | 0.9624 | 0.9667 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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Evaluation results
- Accuracy on imagefolderself-reported0.968
- Precision on imagefolderself-reported0.971
- Recall on imagefolderself-reported0.962
- F1 on imagefolderself-reported0.967