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