---
license: apache-2.0
tags:
- vision
- image-classification
- keras
- tflite
- biology
- ornithology
- vit
library_name: keras
metrics:
- accuracy
---

# British Birds Vision Transformer (ViT)

This repository contains a fine-tuned Vision Transformer (ViT) model designed to classify images of 224 common British bird species. 

The model was fine-tuned from the `vit_base_patch16_224_imagenet21k` backbone using Keras 3.

## Dataset
The dataset comprises **224 classes** with exactly **500 image samples per class** (112,000 images total). 
Images were sourced from [eBird](https://ebird.org/) on February 28, 2026, using the [Birdhouse](https://github.com/rossheat/birdhouse) CLI tool, strictly filtering for the Great Britain region (`--region=GB`), moderately rated images (`--min-avg-rating=4`), with a reasonable number of reviews (`--min-reviews=2`).

The data was split:
* **Training:** 80% (89,600 images)
* **Validation:** 10% (11,200 images)
* **Test:** 10% (11,200 images)

## Model Performance
Evaluated on the 10% unseen Test Set:
* **Final Test Accuracy:** 85.76%
* **Top-3 Accuracy:** 94.03%
* **Loss:** 0.5567

## Files included
* `model/final_224c_500i_GB_bird_vit.keras`: The full Keras 3 model.
* `model/224c_500i_GB_bird_vit_float32.tflite`: Float32 TFLite model for mobile/edge deployment.
* `model/224c_500i_GB_bird_vit_quantized.tflite`: INT8 Quantized TFLite model for optimized edge deployment.
* `model/224c_500i_GB_bird_classes.json`: Ordered list of the 224 eBird taxon codes representing the classes.
* `model/friendly_class_names.csv`: Mapping of taxon codes to friendly human-readable bird names.
* `training/british-birds-vit-training.ipynb`: The Jupyter Notebook used to train the model.

## Usage 

The following snippet demonstrates how to download the model, process an image, and use the included CSV file to output a human-readable English bird name (e.g., "Arctic Skua") rather than the raw eBird taxon code (e.g., "parjae").

```python
import keras
import numpy as np
import json
import csv
from huggingface_hub import hf_hub_download

REPO_ID = "rossheaton/british-birds-vit-base-patch16-224"

# Download model, class codes, and the friendly names CSV
model_path = hf_hub_download(repo_id=REPO_ID, filename="model/final_224c_500i_GB_bird_vit.keras")
labels_path = hf_hub_download(repo_id=REPO_ID, filename="model/224c_500i_GB_bird_classes.json")
csv_path = hf_hub_download(repo_id=REPO_ID, filename="model/friendly_class_names.csv")

# Load the Keras model
model = keras.models.load_model(model_path)

# Load taxon codes (the raw array of classes)
with open(labels_path, 'r') as f:
    taxon_codes = json.load(f)

# Build a dictionary mapping taxon codes to friendly names
taxon_to_friendly = {}
with open(csv_path, mode='r', encoding='utf-8') as f:
    reader = csv.DictReader(f)
    for row in reader:
        # Maps e.g. "parjae" -> "Arctic Skua - Stercorarius parasiticus"
        # Note: Change 'ebird_search_term' to 'rspb_name' if you prefer just the short English name
        taxon_to_friendly[row['ebird_taxon_code']] = row['ebird_search_term']

# Load and preprocess a local image
image_path = "path/to/bird/image.jpg"
img = keras.utils.load_img(image_path, target_size=(224, 224))
img_array = keras.utils.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)

# Predict
predictions = model.predict(img_array)
predicted_index = np.argmax(predictions)
confidence = predictions[0][predicted_index] * 100

# Get the raw taxon code, then look up the friendly name
predicted_taxon = taxon_codes[predicted_index]
friendly_name = taxon_to_friendly.get(predicted_taxon, predicted_taxon) # Fallback to taxon code if missing

print(f"Prediction: {friendly_name} ({confidence:.2f}% confidence)")
```

## Training Details
- Optimiser: AdamW (Learning Rate: 1e-4, Weight Decay: 0.01)
- Loss: Sparse Categorical Crossentropy
- Epochs: 8 (Saved best weights based on Validation Accuracy from Epoch 7)
- Batch Size: 64
- Hardware: 1x NVIDIA RTX 4090 24 GB VRAM, 16 vCPUs, 62 GB RAM