metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-brain-tumor
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: 0.9171249018067557
resnet-50-finetuned-brain-tumor
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2757
- Accuracy: 0.9171
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 1.3264 | 1.0 | 30 | 0.5035 | 1.3154 |
| 1.222 | 2.0 | 60 | 0.6473 | 1.2254 |
| 1.0584 | 3.0 | 90 | 1.0668 | 0.7510 |
| 0.8977 | 4.0 | 120 | 0.9205 | 0.8060 |
| 0.724 | 5.0 | 150 | 0.7740 | 0.8456 |
| 0.6025 | 6.0 | 180 | 0.6009 | 0.8720 |
| 0.4953 | 7.0 | 210 | 0.5039 | 0.8684 |
| 0.4252 | 8.0 | 240 | 0.4158 | 0.8904 |
| 0.3677 | 9.0 | 270 | 0.3705 | 0.9038 |
| 0.3305 | 10.0 | 300 | 0.3300 | 0.9049 |
| 0.3113 | 11.0 | 330 | 0.3053 | 0.9097 |
| 0.2835 | 12.0 | 360 | 0.2885 | 0.9116 |
| 0.2614 | 13.0 | 390 | 0.2606 | 0.9297 |
| 0.2735 | 14.0 | 420 | 0.2767 | 0.9187 |
| 0.2573 | 15.0 | 450 | 0.2757 | 0.9171 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2