---
base_model:
- microsoft/resnet-18
pipeline_tag: image-classification
tags:
- ecology
- birds
- posture
---
# Bowerbird viewpoint classifier (ResNet18)

- Task: classify each frame into one of four viewpoints:  
  `["back", "front", "left_side", "right_side"]`
- Base model: `torchvision.models.resnet18` with `weights="IMAGENET1K_V1"`
- Input size: 224 × 224 (after cropping)
- Preprocessing (training/eval):
  - Resize to 256 px on the shorter side
  - Train: RandomResizedCrop(224), RandomRotation(7°), ColorJitter
  - Eval: CenterCrop(224)
- Normalization:
  - mean  = [0.485, 0.456, 0.406]
  - std   = [0.229, 0.224, 0.225]
- Checkpoint file: `Bbird_viewpoint_classifier.pth`
- The checkpoint stores a **PyTorch `state_dict`** for ResNet18 with a final
  linear layer of 4 outputs (one per viewpoint class).

> This model is **not** generic. It is specific to the four viewpoint classes
> listed above. The classification head must have 4 outputs, in the same
> class order: `back`, `front`, `left_side`, `right_side`.

## Usage

```python
import torch
from torch import nn
from torchvision.models import resnet18
from huggingface_hub import hf_hub_download

# Replace this with the actual repo id on the Hub if different
repo_id = "sarequi/bowerbird-viewpoint-classifier"

# Download checkpoint
ckpt_path = hf_hub_download(
    repo_id=repo_id,
    filename="Bbird_viewpoint_classifier.pth",
)

# Rebuild the model architecture exactly as in training
NUM_CLASSES = 4
model = resnet18(weights="IMAGENET1K_V1")
model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)

# Load weights
state_dict = torch.load(ckpt_path, map_location="cpu")
model.load_state_dict(state_dict)
model.eval()

VIEWPOINT_CLASSES = ["back", "front", "left_side", "right_side"]