| --- |
| 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"] |
