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README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+  - image-classification
+  - pytorch
+  - convnext
+  - birds
+  - computer-vision
+datasets:
+  - CUB-200-2011
+metrics:
+  - accuracy
+library_name: pytorch
+---
+
+# Bird Species Classification - ConvNeXt-Base
+
+## Model Description
+
+This model classifies 200 bird species using ConvNeXt-Base architecture with transfer learning.
+
+## Performance
+
+- **Test Accuracy**: 83.64%
+- **Average Per-Class Accuracy**: 83.29%
+- **Architecture**: ConvNeXt-Base (87M parameters)
+- **Dataset**: CUB-200-2011 (200 bird species)
+
+## Training Details
+
+### Model Architecture
+- **Base Model**: ConvNeXt-Base pretrained on ImageNet-1K
+- **Classifier**: Custom 2-layer classifier with dropout
+- **Input Size**: 224x224 RGB images
+
+### Training Strategy
+- **Phase 1** (40 epochs): Frozen backbone, train classifier only
+  - Learning Rate: 0.001
+  - Batch Size: 32
+  
+- **Phase 2** (20 epochs): Full fine-tuning
+  - Learning Rate: 0.0001
+  - Batch Size: 32
+
+### Regularization
+- Dropout: 0.6, 0.5
+- Label Smoothing: 0.2
+- Weight Decay: 0.005
+- Data Augmentation: rotation, flip, color jitter, random erasing
+
+## Usage
+
+```python
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+
+# Load model
+model = models.convnext_base(weights=None)
+num_features = model.classifier[2].in_features
+model.classifier[2] = nn.Sequential(
+    nn.Dropout(0.6),
+    nn.Linear(num_features, 1024),
+    nn.ReLU(),
+    nn.Dropout(0.5),
+    nn.Linear(1024, 200)
+)
+
+# Load weights
+model.load_state_dict(torch.load('final_model.pth', map_location='cpu'))
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Predict
+image = Image.open('bird.jpg').convert('RGB')
+image_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    outputs = model(image_tensor)
+    probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+    top5_prob, top5_indices = torch.topk(probabilities, 5)
+    
+print("Top 5 Predictions:")
+for prob, idx in zip(top5_prob, top5_indices):
+    print(f"Class {idx}: {prob.item()*100:.2f}%")
+```
+
+## Try it out!
+
+Try the live demo: [Bird Species Classifier](https://huggingface.co/spaces/AshProg/AppliedMachineLearning_BirdClassifierInterface)
+
+## Model Files
+
+- `final_model.pth` (1.06 GB): Full model weights
+
+## Citation
+
+Dataset: CUB-200-2011
+```
+@techreport{WahCUB_200_2011,
+    Title = {{The Caltech-UCSD Birds-200-2011 Dataset}},
+    Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.},
+    Year = {2011},
+    Institution = {California Institute of Technology},
+    Number = {CNS-TR-2011-001}
+}
+```
+
+## Contact
+
+For questions or issues, please open an issue on the Space repository.