---
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