Downloads · 30 days
2
6% of all-time downloads
fine-tuned-model
Rageshhf/fine-tuned-model
fine-tuned-model is a image classification model from Rageshhf. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
36
Public
Parameters
85.8M
343 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors343 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Apr 28, 2024
- Updated
- Apr 28, 2024
- SHA
- d42ece73
Base models
Spaces
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
fine-tuned-model
This model is a fine-tuned version of google/vit-base-patch16-224 on the Falah/Alzheimer_MRI dataset. It achieves the following results on the evaluation set:
- Loss: 0.8720
- Accuracy: 0.5742
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: 0.0003
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9696 | 1.0 | 256 | 0.8925 | 0.5781 |
| 0.9141 | 2.0 | 512 | 0.8447 | 0.5938 |
| 0.8669 | 3.0 | 768 | 0.8378 | 0.6035 |
| 0.8356 | 4.0 | 1024 | 0.8236 | 0.5938 |
| 0.8529 | 5.0 | 1280 | 0.8206 | 0.6074 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Apr 28, 2024
- Updated
- Apr 28, 2024