# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Newvel/face_detection_model_output")
model = AutoModelForImageClassification.from_pretrained("Newvel/face_detection_model_output")Quick Links
face_detection_model_output
This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- 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
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0 | 1.0 | 352 | 0.0000 | 1.0 |
| 0.0 | 2.0 | 704 | 0.0000 | 1.0 |
| 0.0 | 3.0 | 1056 | 0.0000 | 1.0 |
| 0.0 | 4.0 | 1408 | 0.0000 | 1.0 |
| 0.0 | 5.0 | 1760 | 0.0000 | 1.0 |
| 0.0 | 6.0 | 2112 | 0.0000 | 1.0 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for Newvel/face_detection_model_output
Base model
WinKawaks/vit-tiny-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Newvel/face_detection_model_output") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")