Downloads · 30 days
6
2% of all-time downloads
breastmnist-beit-base-finetuned
selmamalak/breastmnist-beit-base-finetuned
breastmnist-beit-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
366
Public
Repo size
23.7 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 30, 2024
- Updated
- May 30, 2024
- SHA
- 91be5f2b
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. -->
breastmnist-beit-base-finetuned
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the medmnist-v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5228
- Accuracy: 0.7308
- Precision: 0.3654
- Recall: 0.5
- F1: 0.4222
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 |
|---|---|---|---|---|---|---|---|
| No log | 0.9143 | 8 | 0.8325 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.7315 | 1.9429 | 17 | 0.5744 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.6223 | 2.9714 | 26 | 0.5911 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.5815 | 4.0 | 35 | 0.5743 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.5627 | 4.9143 | 43 | 0.6546 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.5552 | 5.9429 | 52 | 0.5381 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.536 | 6.9714 | 61 | 0.5101 | 0.7949 | 0.8904 | 0.6190 | 0.6308 |
| 0.5454 | 8.0 | 70 | 0.5273 | 0.7692 | 0.7246 | 0.6165 | 0.6286 |
| 0.5454 | 8.9143 | 78 | 0.5176 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
| 0.5058 | 9.1429 | 80 | 0.5228 | 0.7308 | 0.3654 | 0.5 | 0.4222 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- License
- apache-2.0
- Created
- May 30, 2024
- Updated
- May 30, 2024