Downloads · 30 days
8
33% of all-time downloads
organsmnist-vit-base-finetuned
selmamalak/organsmnist-vit-base-finetuned
organsmnist-vit-base-finetuned is a machine learning model from selmamalak. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
24
Public
Repo size
24 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.4 MB · 100%
At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- May 18, 2024
- Updated
- May 18, 2024
- SHA
- 349b4e26
Base models
Datasets
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. -->
organsmnist-vit-base-finetuned
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the medmnist-v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2964
- Accuracy: 0.8993
- Precision: 0.8443
- Recall: 0.8396
- F1: 0.8394
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: 0.005
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9084 | 1.0 | 218 | 0.7151 | 0.7288 | 0.6998 | 0.6620 | 0.6412 |
| 0.89 | 2.0 | 436 | 0.3658 | 0.8540 | 0.7873 | 0.7898 | 0.7660 |
| 0.7851 | 3.0 | 654 | 0.3514 | 0.8438 | 0.8110 | 0.7674 | 0.7741 |
| 0.7144 | 4.0 | 872 | 0.3632 | 0.8670 | 0.8415 | 0.8133 | 0.7980 |
| 0.7383 | 5.0 | 1090 | 0.3680 | 0.8581 | 0.7769 | 0.8029 | 0.7786 |
| 0.6065 | 6.0 | 1308 | 0.2824 | 0.8870 | 0.8481 | 0.8328 | 0.8305 |
| 0.521 | 7.0 | 1526 | 0.2769 | 0.8940 | 0.8439 | 0.8404 | 0.8297 |
| 0.5305 | 8.0 | 1744 | 0.2611 | 0.9001 | 0.8517 | 0.8463 | 0.8447 |
| 0.4522 | 9.0 | 1962 | 0.2742 | 0.9058 | 0.8594 | 0.8517 | 0.8411 |
| 0.4445 | 10.0 | 2180 | 0.2964 | 0.8993 | 0.8443 | 0.8396 | 0.8394 |
Framework versions
- PEFT 0.11.1
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- peft
- License
- apache-2.0
- Created
- May 18, 2024
- Updated
- May 18, 2024