# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Thamer/resnet-fine_tuned")
model = AutoModelForImageClassification.from_pretrained("Thamer/resnet-fine_tuned")Quick Links
resnet-fine_tuned
This model is a fine-tuned version of microsoft/resnet-34 on the Falah/Alzheimer_MRI dataset. It achieves the following results on the evaluation set:
- Loss: 0.1983
- Accuracy: 0.9219
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 0.9041 | 1.0 | 80 | 0.9659 | 0.5352 |
| 0.8743 | 2.0 | 160 | 0.9348 | 0.5797 |
| 0.7723 | 3.0 | 240 | 0.7793 | 0.6594 |
| 0.6864 | 4.0 | 320 | 0.6799 | 0.7031 |
| 0.5347 | 5.0 | 400 | 0.5596 | 0.7703 |
| 0.4282 | 6.0 | 480 | 0.5078 | 0.7766 |
| 0.4315 | 7.0 | 560 | 0.5455 | 0.7680 |
| 0.3747 | 8.0 | 640 | 0.4203 | 0.8266 |
| 0.2977 | 9.0 | 720 | 0.3926 | 0.8469 |
| 0.2252 | 10.0 | 800 | 0.3024 | 0.8742 |
| 0.2675 | 11.0 | 880 | 0.2731 | 0.8906 |
| 0.2136 | 12.0 | 960 | 0.3045 | 0.875 |
| 0.1998 | 13.0 | 1040 | 0.2370 | 0.9 |
| 0.2406 | 14.0 | 1120 | 0.2387 | 0.9086 |
| 0.1873 | 15.0 | 1200 | 0.1983 | 0.9219 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for Thamer/resnet-fine_tuned
Base model
microsoft/resnet-34
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thamer/resnet-fine_tuned") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")