| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: dennisjooo/Birds-Classifier-EfficientNetB2 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - f1 |
| - precision |
| - recall |
| - accuracy |
| model-index: |
| - name: train_checkpoints2 |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: validation |
| args: default |
| metrics: |
| - name: F1 |
| type: f1 |
| value: 0.8685894687564659 |
| - name: Precision |
| type: precision |
| value: 0.8781544844044844 |
| - name: Recall |
| type: recall |
| value: 0.8634882138558609 |
| - name: Accuracy |
| type: accuracy |
| value: 0.8686131386861314 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # train_checkpoints2 |
| |
| This model is a fine-tuned version of [dennisjooo/Birds-Classifier-EfficientNetB2](https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4826 |
| - F1: 0.8686 |
| - Precision: 0.8782 |
| - Recall: 0.8635 |
| - Accuracy: 0.8686 |
| |
| ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
| | 0.1145 | 1.0 | 15 | 0.5836 | 0.8608 | 0.8776 | 0.8520 | 0.8613 | |
| | 0.129 | 2.0 | 30 | 0.8019 | 0.8322 | 0.8634 | 0.8192 | 0.8358 | |
| | 0.2085 | 3.0 | 45 | 0.7550 | 0.8083 | 0.8355 | 0.8042 | 0.8212 | |
| | 0.1722 | 4.0 | 60 | 0.7524 | 0.8298 | 0.8422 | 0.8357 | 0.8394 | |
| | 0.19 | 5.0 | 75 | 0.5542 | 0.8743 | 0.8910 | 0.8679 | 0.8723 | |
| | 0.1612 | 6.0 | 90 | 0.8325 | 0.8114 | 0.8410 | 0.8063 | 0.8066 | |
| | 0.2009 | 7.0 | 105 | 0.4425 | 0.8900 | 0.8904 | 0.8911 | 0.8942 | |
| | 0.209 | 8.0 | 120 | 0.6705 | 0.8126 | 0.8482 | 0.8074 | 0.8358 | |
| | 0.2188 | 9.0 | 135 | 0.5906 | 0.8387 | 0.8551 | 0.8350 | 0.8467 | |
| | 0.1962 | 10.0 | 150 | 0.4826 | 0.8686 | 0.8782 | 0.8635 | 0.8686 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
