docs: add contents to README.md

README.md

@@ -1,9 +1,107 @@
 ---
 base_model:
-- google/vit-base-patch16-224-in21k
+  - google/vit-base-patch16-224-in21k
 library_name: transformers
 tags:
-- image-classification
-- vision-transformer
-- just-for-fun
----
+  - image-classification
+  - vision-transformer
+  - just-for-fun
+---
+
+# MaxVision: Max vs. Not Max Classifier
+
+## Model Overview
+
+**MaxVision** is a fun, hobby AI vision classifier designed to distinguish between images of Max, a black and white
+sprocker spaniel, and all other images. The model has been trained using personal photos of Max and general images of
+other dogs and non-dog subjects to improve its classification accuracy. It is intended purely for personal and
+experimental use.
+
+## Model Details
+
+- **Developed by:** Patrick Skillen
+- **Use Case:** Identifying whether an image contains Max
+- **Architecture:** Based on a fine-tuned vision transformer (ViT)
+- **Training Dataset:** Curated personal dataset of Max and various non-Max images
+- **Framework:** PyTorch with Hugging Face Transformers
+- **Training Platform:** Google Colab
+- **Labels:**
+    - `0`: Max
+    - `1`: Not Max
+
+## Intended Use
+
+This model is built as a fun, personal experiment in AI/ML and image classification. It is not intended for commercial
+applications, biometric identification, or general dog breed classification.
+
+## Limitations & Biases
+
+- The model is heavily biased toward distinguishing Max from non-Max images and is not robust for identifying specific
+  breeds or other dogs.
+- Performance may degrade on images with low resolution, extreme lighting conditions, or unusual poses.
+- Limited dataset size and personal image selection may affect generalizability.
+
+## How to Use
+
+Try it in the HF Space at https://huggingface.co/spaces/paddeh/is-it-max
+
+To use the model, you can run inference using the Hugging Face `transformers` or `timm` library, depending on the model
+backbone. Below is a sample inference script:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("image-classification", model="paddeh/is-it-max")
+
+result = classifier("path/to/image.jpg")
+print("Max" if prediction.item() == 0 else "Not Max")
+```
+
+Alternatively, with `torchvision`:
+
+```python
+import torch
+from torchvision import transforms
+from transformers import ViTForImageClassification, ViTImageProcessor
+from PIL import Image
+
+model = ViTForImageClassification.from_pretrained('model.safetensors')
+model.eval()
+processor = ViTImageProcessor.from_pretrained(model_path)
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
+])
+
+image = Image.open("path/to/image.jpg")
+image = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    output = model(image)
+
+prediction = torch.argmax(output, dim=1)
+print("Max" if prediction.item() == 0 else "Not Max")
+```
+
+## Model Performance
+
+As this is a personal hobby project, there is no formal benchmark, but the model has been tested informally using
+validation images from Max’s personal collection and various other dog breeds.
+
+## Ethical Considerations
+
+Since this model is built for personal use, there are no significant ethical concerns. However, users should be mindful
+of data privacy and not use the model for unauthorized biometric identification of pets or people.
+
+## Future Improvements
+
+- Expand the dataset with more diverse images of Max in different lighting conditions and settings.
+- Improve augmentation techniques to enhance robustness.
+- Fine-tune using more advanced architectures like CLIP or Swin Transformer for better accuracy.
+
+---
+
+**Disclaimer:** This model is intended for personal and educational use only. It is not designed for commercial
+applications or general-purpose image recognition.