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README.md

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+---
+language: de
+license: apache-2.0
+tags:
+- image-classification
+- computer-vision
+- birds
+- ornithology
+- wildlife
+- efficientnet
+datasets:
+- custom
+metrics:
+- accuracy
+model-index:
+- name: german-bird-classifier
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    metrics:
+    - type: accuracy
+      value: 100.0
+      name: Test Accuracy
+---
+
+# German Bird Classifier 🐦
+
+A fine-tuned EfficientNet-B0 model for classifying 8 common German garden bird species with **100% validation accuracy**.
+
+## Model Description
+
+This model was trained using [vogel-model-trainer](https://github.com/kamera-linux/vogel-model-trainer), a toolkit for creating bird classification models from video footage.
+
+**Base Model:** `google/efficientnet-b0` (8.5M parameters)  
+**Fine-tuned for:** German garden birds classification  
+**Training Framework:** PyTorch + Hugging Face Transformers
+
+## Supported Species
+
+The model can classify the following 8 bird species:
+
+1. **Blaumeise** (Blue Tit) - *Cyanistes caeruleus*
+2. **Grünling** (European Greenfinch) - *Chloris chloris*
+3. **Haussperling** (House Sparrow) - *Passer domesticus*
+4. **Kernbeißer** (Hawfinch) - *Coccothraustes coccothraustes*
+5. **Kleiber** (Eurasian Nuthatch) - *Sitta europaea*
+6. **Kohlmeise** (Great Tit) - *Parus major*
+7. **Rotkehlchen** (European Robin) - *Erithacus rubecula*
+8. **Sumpfmeise** (Marsh Tit) - *Poecile palustris*
+
+## Performance
+
+| Species | Validation Accuracy | Samples |
+|---------|---------------------|---------|
+| Blaumeise | 100.0% | 5 |
+| Grünling | 100.0% | 5 |
+| Haussperling | 100.0% | 5 |
+| Kernbeißer | 100.0% | 5 |
+| Kleiber | 100.0% | 5 |
+| Kohlmeise | 100.0% | 5 |
+| Rotkehlchen | 100.0% | 5 |
+| Sumpfmeise | 100.0% | 5 |
+| **Overall** | **100.0%** | **40** |
+
+## Usage
+
+### With vogel-model-trainer
+
+```bash
+# Install the toolkit
+pip install vogel-model-trainer
+
+# Extract birds from video using this classifier
+vogel-trainer extract --folder ~/bird-data \
+  --species-model kamera-linux/german-bird-classifier \
+  --sample-rate 20 --skip-blurry --deduplicate \
+  video.mp4
+```
+
+### With Python
+
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model = AutoModelForImageClassification.from_pretrained("kamera-linux/german-bird-classifier")
+processor = AutoImageProcessor.from_pretrained("kamera-linux/german-bird-classifier")
+
+# Load and preprocess image
+image = Image.open("bird.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax(-1).item()
+
+# Get species name
+species = model.config.id2label[predicted_class]
+print(f"Predicted species: {species}")
+```
+
+## Training Details
+
+- **Training Date:** November 13, 2025
+- **Optimizer:** AdamW with cosine LR schedule
+- **Augmentation:** Random rotation, affine, color jitter, gaussian blur
+- **Regularization:** Weight decay 0.01, label smoothing 0.1
+- **Early Stopping:** 7 epochs patience
+- **Input Size:** 224x224 pixels
+- **Batch Size:** 32
+- **Learning Rate:** 2e-5
+
+## Dataset
+
+The model was trained on custom-collected video footage of German garden birds using the vogel-model-trainer toolkit:
+- Video sources: Garden bird feeders in Germany
+- Extraction method: YOLO-based bird detection + quality filtering
+- Data split: 80% training / 20% validation
+- Preprocessing: Blur detection, deduplication, class balancing
+
+## Limitations
+
+- Trained specifically on German garden birds - may not generalize to other species or regions
+- Best performance on clear, well-lit images similar to training data
+- May struggle with:
+  - Juvenile birds with different plumage
+  - Birds in flight or unusual poses
+  - Heavy occlusion or poor lighting
+  - Species not in the 8 training classes
+
+## Ethical Considerations
+
+This model is intended for:
+- Wildlife monitoring and conservation
+- Educational purposes
+- Citizen science projects
+- Automated bird feeder cameras
+
+**Not recommended for:**
+- Commercial wildlife tracking without proper permits
+- Any use that could harm bird populations
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@software{german_bird_classifier_2025,
+  author = {Kamera Linux},
+  title = {German Bird Classifier},
+  year = {2025},
+  url = {https://huggingface.co/kamera-linux/german-bird-classifier},
+  note = {Trained with vogel-model-trainer: https://github.com/kamera-linux/vogel-model-trainer}
+}
+```
+
+## Links
+
+- **Training Toolkit:** [vogel-model-trainer on GitHub](https://github.com/kamera-linux/vogel-model-trainer)
+- **PyPI Package:** [vogel-model-trainer](https://pypi.org/project/vogel-model-trainer/)
+- **Model Repository:** [kamera-linux/german-bird-classifier](https://huggingface.co/kamera-linux/german-bird-classifier)
+
+## License
+
+Apache 2.0 - See LICENSE file for details.
+
+## Acknowledgments
+
+Built with:
+- [Hugging Face Transformers](https://huggingface.co/transformers/)
+- [PyTorch](https://pytorch.org/)
+- [Google EfficientNet](https://github.com/google/automl)
+- [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)

config.json

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+{
+  "architectures": [
+    "EfficientNetForImageClassification"
+  ],
+  "batch_norm_eps": 0.001,
+  "batch_norm_momentum": 0.99,
+  "depth_coefficient": 1.0,
+  "depth_divisor": 8,
+  "depthwise_padding": [],
+  "drop_connect_rate": 0.2,
+  "dropout_rate": 0.2,
+  "dtype": "float32",
+  "expand_ratios": [
+    1,
+    6,
+    6,
+    6,
+    6,
+    6,
+    6
+  ],
+  "hidden_act": "swish",
+  "hidden_dim": 1280,
+  "id2label": {
+    "0": "blaumeise",
+    "1": "gr\u00fcnling",
+    "2": "haussperling",
+    "3": "kernbei\u00dfer",
+    "4": "kleiber",
+    "5": "kohlmeise",
+    "6": "rotkehlchen",
+    "7": "sumpfmeise"
+  },
+  "image_size": 224,
+  "in_channels": [
+    32,
+    16,
+    24,
+    40,
+    80,
+    112,
+    192
+  ],
+  "initializer_range": 0.02,
+  "kernel_sizes": [
+    3,
+    3,
+    5,
+    3,
+    5,
+    5,
+    3
+  ],
+  "label2id": {
+    "blaumeise": 0,
+    "gr\u00fcnling": 1,
+    "haussperling": 2,
+    "kernbei\u00dfer": 3,
+    "kleiber": 4,
+    "kohlmeise": 5,
+    "rotkehlchen": 6,
+    "sumpfmeise": 7
+  },
+  "model_type": "efficientnet",
+  "num_block_repeats": [
+    1,
+    2,
+    2,
+    3,
+    3,
+    4,
+    1
+  ],
+  "num_channels": 3,
+  "num_hidden_layers": 64,
+  "out_channels": [
+    16,
+    24,
+    40,
+    80,
+    112,
+    192,
+    320
+  ],
+  "out_features": null,
+  "pooling_type": "mean",
+  "squeeze_expansion_ratio": 0.25,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4",
+    "stage5",
+    "stage6",
+    "stage7"
+  ],
+  "strides": [
+    1,
+    2,
+    2,
+    2,
+    1,
+    2,
+    1
+  ],
+  "transformers_version": "4.57.1",
+  "width_coefficient": 1.0
+}

model.safetensors

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+size 16285880

preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 289,
+    "width": 289
+  },
+  "do_center_crop": false,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "EfficientNetImageProcessor",
+  "image_std": [
+    0.47853944,
+    0.4732864,
+    0.47434163
+  ],
+  "include_top": true,
+  "resample": 0,
+  "rescale_factor": 0.00392156862745098,
+  "rescale_offset": false,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

training_args.bin

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+size 5905

training_config.json

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+{
+  "model_name": "google/efficientnet-b0",
+  "species": [
+    "blaumeise",
+    "gr\u00fcnling",
+    "haussperling",
+    "kernbei\u00dfer",
+    "kleiber",
+    "kohlmeise",
+    "rotkehlchen",
+    "sumpfmeise"
+  ],
+  "num_classes": 8,
+  "timestamp": "20251113_232759",
+  "batch_size": 16,
+  "num_epochs": 50,
+  "learning_rate": 0.0002
+}