Downloads · 30 days
9
35% of all-time downloads
deit-base-patch16-224-bloodmnist-fold-9
selmamalak/deit-base-patch16-224-bloodmnist-fold-9
deit-base-patch16-224-bloodmnist-fold-9 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
26
Public
Repo size
2.4 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
- Jul 21, 2025
- Updated
- Jul 21, 2025
- SHA
- 3b9ca9e3
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. -->
deit-base-patch16-224-bloodmnist-fold-9
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: 0.0853
- Accuracy: 0.9701
- Precision: 0.9687
- Recall: 0.9681
- F1: 0.9683
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4787 | 1.0 | 196 | 0.2803 | 0.9008 | 0.9067 | 0.8772 | 0.8746 |
| 0.3729 | 2.0 | 392 | 0.2156 | 0.9271 | 0.9337 | 0.9095 | 0.9193 |
| 0.3466 | 3.0 | 588 | 0.2045 | 0.9271 | 0.9290 | 0.9095 | 0.9163 |
| 0.3253 | 4.0 | 784 | 0.2027 | 0.9324 | 0.9353 | 0.9143 | 0.9221 |
| 0.3245 | 5.0 | 980 | 0.2461 | 0.9157 | 0.9369 | 0.9165 | 0.9216 |
| 0.2758 | 6.0 | 1176 | 0.1593 | 0.9447 | 0.9467 | 0.9310 | 0.9350 |
| 0.2132 | 7.0 | 1372 | 0.1155 | 0.9570 | 0.9553 | 0.9480 | 0.9510 |
| 0.212 | 8.0 | 1568 | 0.0853 | 0.9701 | 0.9687 | 0.9681 | 0.9683 |
| 0.1598 | 9.0 | 1764 | 0.0859 | 0.9666 | 0.9604 | 0.9651 | 0.9626 |
| 0.1112 | 10.0 | 1960 | 0.0791 | 0.9701 | 0.9685 | 0.9684 | 0.9683 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
- Library
- peft
- License
- apache-2.0
- Created
- Jul 21, 2025
- Updated
- Jul 21, 2025