README.md

---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: envisage
  results: []
---

# Model Card for envisage

This is the official model card for `envisage`, a Vision Transformer (ViT) model fine-tuned for image classification.

This model was fine-tuned from the `google/vit-base-patch16-224-in21k` base model on the `cifar10` dataset, which consists of 60,000 32x32 color images in 10 distinct classes.

## Model Description

- **Base Model:** [`google/vit-base-patch16-224-in21k`](https://huggingface.co/google/vit-base-patch16-224-in21k)
- **Dataset:** [`cifar10`](https://huggingface.co/datasets/cifar10)
- **Task:** Image Classification
- **Framework:** PyTorch, Transformers
- **Classes (10):** `airplane`, `automobile`, `bird`, `cat`, `deer`, `dog`, `frog`, `horse`, `ship`, `truck`

## How to Use

The easiest way to use this model for inference is with the `pipeline` API from the `transformers` library.

First, ensure you have the necessary libraries installed:
```bash
pip install transformers torch pillow
```

Then, you can use the following Python snippet to classify an image:

```python
from transformers import pipeline
from PIL import Image
import requests

# Load the classification pipeline with your model
pipe = pipeline("image-classification", model="louijiec/envisage")

# Load an image from a URL (e.g., a cat)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cat-tree.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

# Get the predictions
predictions = pipe(image)

print("Predictions:")
for p in predictions:
    print(f"- {p['label']}: {p['score']:.4f}")

# Expected output will show the model's confidence for each class,
# with 'cat' likely having the highest score.
```

## Training Procedure

The model was trained in a Google Colab environment using the `transformers` `Trainer` API.

### Hyperparameters

- **Learning Rate:** 5e-5
- **Training Epochs:** 3
- **Batch Size:** 16 per device
- **Gradient Accumulation Steps:** 4 (Effective batch size of 64)
- **Optimizer:** AdamW with a linear learning rate schedule
- **Warmup Ratio:** 0.1

### Evaluation

The model was evaluated on the `cifar10` test split, which contains 10,000 images.

- **Final Accuracy on Test Set:** [TODO: Add final accuracy from the `trainer.evaluate()` step here. For example: 0.965]

## Intended Use & Limitations

This model is intended for educational purposes and as a demonstration of fine-tuning a Vision Transformer on a common benchmark dataset. It performs well on images similar to those in the `cifar10` dataset (small, low-resolution images of the 10 specified classes).

**Limitations:**
- The model will likely perform poorly on images that are significantly different from the `cifar10` data (e.g., high-resolution photos, medical images, or classes not seen during training).
- The training data may reflect biases present in the original `cifar10` dataset.