Downloads · 30 days
4
4% of all-time downloads
mlops_deepfake
Gautam20/mlops_deepfake
mlops_deepfake is a image classification model from Gautam20. 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
95
Public
Parameters
85.8M
1 GB 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 18, 2026
- Updated
- Apr 18, 2026
- SHA
- da54d6b8
Base models
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. -->
mlops_deepfake
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3353
- Accuracy: 0.9361
- Auc: 0.9997
- F1 Fake: 0.9399
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- total_eval_batch_size: 96
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 53
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | F1 Fake |
|---|---|---|---|---|---|---|
| No log | 1.0 | 36 | 0.3359 | 0.8996 | 0.9722 | 0.8959 |
| 0.1294 | 2.0 | 72 | 0.4343 | 0.9116 | 0.9767 | 0.9042 |
| 0.0698 | 3.0 | 108 | 0.3150 | 0.9107 | 0.9798 | 0.9111 |
| 0.0698 | 4.0 | 144 | 0.4733 | 0.8812 | 0.9751 | 0.8638 |
| 0.0028 | 5.0 | 180 | 0.3431 | 0.9144 | 0.9766 | 0.9073 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.5.1
- Datasets 4.8.4
- Tokenizers 0.22.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Apr 18, 2026
- Updated
- Apr 18, 2026