metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: efficientnet-b5-Brain_Tumors_Image_Classification
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: 0.8020304568527918
efficientnet-b5-Brain_Tumors_Image_Classification
This model is a fine-tuned version of google/efficientnet-b5 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9410
- Accuracy: 0.8020
- Weighted f1: 0.7736
- Micro f1: 0.8020
- Macro f1: 0.7802
- Weighted recall: 0.8020
- Micro recall: 0.8020
- Macro recall: 0.7977
- Weighted precision: 0.8535
- Micro precision: 0.8020
- Macro precision: 0.8682
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.3872 | 1.0 | 180 | 1.0601 | 0.6853 | 0.6485 | 0.6853 | 0.6550 | 0.6853 | 0.6853 | 0.6802 | 0.8177 | 0.6853 | 0.8330 |
| 1.3872 | 2.0 | 360 | 0.9533 | 0.7843 | 0.7483 | 0.7843 | 0.7548 | 0.7843 | 0.7843 | 0.7819 | 0.8354 | 0.7843 | 0.8471 |
| 0.8186 | 3.0 | 540 | 0.9410 | 0.8020 | 0.7736 | 0.8020 | 0.7802 | 0.8020 | 0.8020 | 0.7977 | 0.8535 | 0.8020 | 0.8682 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3