metadata
library_name: transformers
license: apache-2.0
base_model: google/efficientnet-b0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: efficientnet-b0-finetuned-chest-xray-pneumonia
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9348659003831418
efficientnet-b0-finetuned-chest-xray-pneumonia
This model is a fine-tuned version of google/efficientnet-b0 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2240
- Accuracy: 0.9349
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4634 | 1.0 | 37 | 0.4140 | 0.8506 |
| 0.2814 | 2.0 | 74 | 0.2240 | 0.9349 |
| 0.2495 | 3.0 | 111 | 0.2450 | 0.9157 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1