metadata

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

train_checkpoints2

This model is a fine-tuned version of 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