metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-omar
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: 0.9143576826196473
resnet-50-finetuned-omar
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: 0.2645
- Accuracy: 0.9144
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0695 | 1.0 | 111 | 1.0576 | 0.5315 |
| 0.971 | 2.0 | 223 | 0.9366 | 0.5416 |
| 0.8121 | 3.0 | 334 | 0.7493 | 0.7103 |
| 0.6861 | 4.0 | 446 | 0.5625 | 0.8363 |
| 0.606 | 5.0 | 557 | 0.4239 | 0.8816 |
| 0.5001 | 6.0 | 669 | 0.3159 | 0.9219 |
| 0.4704 | 7.0 | 780 | 0.3254 | 0.9118 |
| 0.4332 | 8.0 | 892 | 0.2808 | 0.9194 |
| 0.4432 | 9.0 | 1003 | 0.2854 | 0.9219 |
| 0.4768 | 10.0 | 1115 | 0.2782 | 0.9219 |
| 0.4432 | 11.0 | 1226 | 0.2768 | 0.9320 |
| 0.4752 | 12.0 | 1338 | 0.2744 | 0.9219 |
| 0.489 | 13.0 | 1449 | 0.2693 | 0.9194 |
| 0.3743 | 14.0 | 1561 | 0.2715 | 0.9270 |
| 0.417 | 14.93 | 1665 | 0.2645 | 0.9144 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3