Update README.md

README.md

@@ -11,51 +11,77 @@ model-index:
   results: []
 ---
 
-<!-- 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. -->
+# Model Card for envisage
 
-# envisage
+This is the official model card for `envisage`, a Vision Transformer (ViT) model fine-tuned for image classification.
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1283
-- Accuracy: 0.9828
+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
+## Model Description
 
-More information needed
+- **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`
 
-## Intended uses & limitations
+## How to Use
 
-More information needed
+The easiest way to use this model for inference is with the `pipeline` API from the `transformers` library.
 
-## Training and evaluation data
+First, ensure you have the necessary libraries installed:
+```bash
+pip install transformers torch pillow
+```
 
-More information needed
+Then, you can use the following Python snippet to classify an image:
 
-## Training procedure
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
 
-### Training hyperparameters
+# Load the classification pipeline with your model
+pipe = pipeline("image-classification", model="louijiec/envisage")
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 64
-- optimizer: Use OptimizerNames.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_ratio: 0.1
-- num_epochs: 1
+# 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)
 
-### Training results
+# 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.
+```
 
-### Framework versions
+## Training Procedure
 
-- Transformers 4.52.4
-- Pytorch 2.6.0+cu124
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+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.