# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Mozilla/docornot")
model = AutoModelForImageClassification.from_pretrained("Mozilla/docornot")Quick Links
This model is a fine-tuned version of facebook/deit-tiny-distilled-patch16-224 on the docornot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
CO2 emissions
This model was trained on an M1 and took 0.322 g of CO2 (measured with CodeCarbon)
Model description
This model is distilled Vision Transformer (ViT) model. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
Intended uses & limitations
You can use this model to detect if an image is a picture or a document.
Training procedure
Source code used to generate this model : https://github.com/mozilla/docornot
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0 | 1.0 | 1600 | 0.0000 | 1.0 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Mozilla/docornot
Base model
facebook/deit-tiny-distilled-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mozilla/docornot") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")