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---
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
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