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Amazon

AI4GAmazonRainforest

Bases: PlainResNetInference

Amazon Ranforest Animal Classifier that inherits from PlainResNetInference. This classifier is specialized for recognizing 36 different animals in the Amazon Rainforest.

Source code in PytorchWildlife/models/classification/resnet_base/amazon.py 
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class AI4GAmazonRainforest(PlainResNetInference):
    """
    Amazon Ranforest Animal Classifier that inherits from PlainResNetInference.
    This classifier is specialized for recognizing 36 different animals in the Amazon Rainforest.
    """

    # Image size for the Opossum classifier
    IMAGE_SIZE = 224

    # Class names for prediction
    CLASS_NAMES = {
        0: 'Dasyprocta',
        1: 'Bos',
        2: 'Pecari',
        3: 'Mazama',
        4: 'Cuniculus',
        5: 'Leptotila',
        6: 'Human',
        7: 'Aramides',
        8: 'Tinamus',
        9: 'Eira',
        10: 'Crax',
        11: 'Procyon',
        12: 'Capra',
        13: 'Dasypus',
        14: 'Sciurus',
        15: 'Crypturellus',
        16: 'Tamandua',
        17: 'Proechimys',
        18: 'Leopardus',
        19: 'Equus',
        20: 'Columbina',
        21: 'Nyctidromus',
        22: 'Ortalis',
        23: 'Emballonura',
        24: 'Odontophorus',
        25: 'Geotrygon',
        26: 'Metachirus',
        27: 'Catharus',
        28: 'Cerdocyon',
        29: 'Momotus',
        30: 'Tapirus',
        31: 'Canis',
        32: 'Furnarius',
        33: 'Didelphis',
        34: 'Sylvilagus',
        35: 'Unknown'
    }

    def __init__(self, weights=None, device="cpu", pretrained=True, version="v2"):
        """
        Initialize the Amazon animal Classifier.

        Args:
            weights (str, optional): Path to the model weights. Defaults to None.
            device (str, optional): Device for model inference. Defaults to "cpu".
            pretrained (bool, optional): Whether to use pretrained weights. Defaults to True.
            version (str, optional): Version of the model to load. Default is 'v2'.
        """

        # If pretrained, use the provided URL to fetch the weights
        if pretrained:
            if version == 'v1':
                url = "https://zenodo.org/records/10042023/files/AI4GAmazonClassification_v0.0.0.ckpt?download=1"
            elif version == 'v2':
                url = "https://zenodo.org/records/14252214/files/AI4GAmazonDeforestationv2?download=1"
        else:
            url = None

        super(AI4GAmazonRainforest, self).__init__(weights=weights, device=device,
                                                   num_cls=36, num_layers=50, url=url)

    def results_generation(self, logits: torch.Tensor, img_ids: list[str], id_strip: str = None) -> list[dict]:
        """
        Generate results for classification.

        Args:
            logits (torch.Tensor): Output tensor from the model.
            img_ids (str): Image identifier.
            id_strip (str): stiping string for better image id saving.       

        Returns:
            dict: Dictionary containing image ID, prediction, and confidence score.
        """

        probs = torch.softmax(logits, dim=1)
        preds = probs.argmax(dim=1)
        confs = probs.max(dim=1)[0]
        confidences = probs[0].tolist()
        result = [[self.CLASS_NAMES[i], confidence] for i, confidence in enumerate(confidences)]

        results = []
        for pred, img_id, conf in zip(preds, img_ids, confs):
            r = {"img_id": str(img_id).strip(id_strip)}
            r["prediction"] = self.CLASS_NAMES[pred.item()]
            r["class_id"] = pred.item()
            r["confidence"] = conf.item()
            r["all_confidences"] = result
            results.append(r)

        return results

__init__(weights=None, device='cpu', pretrained=True, version='v2')

Initialize the Amazon animal Classifier.

Parameters:

Name Type Description Default
weights str

Path to the model weights. Defaults to None.

 None
device str

Device for model inference. Defaults to "cpu".

 'cpu'
pretrained bool

Whether to use pretrained weights. Defaults to True.

 True
version str

Version of the model to load. Default is 'v2'.

 'v2'
Source code in PytorchWildlife/models/classification/resnet_base/amazon.py 
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def __init__(self, weights=None, device="cpu", pretrained=True, version="v2"):
    """
    Initialize the Amazon animal Classifier.

    Args:
        weights (str, optional): Path to the model weights. Defaults to None.
        device (str, optional): Device for model inference. Defaults to "cpu".
        pretrained (bool, optional): Whether to use pretrained weights. Defaults to True.
        version (str, optional): Version of the model to load. Default is 'v2'.
    """

    # If pretrained, use the provided URL to fetch the weights
    if pretrained:
        if version == 'v1':
            url = "https://zenodo.org/records/10042023/files/AI4GAmazonClassification_v0.0.0.ckpt?download=1"
        elif version == 'v2':
            url = "https://zenodo.org/records/14252214/files/AI4GAmazonDeforestationv2?download=1"
    else:
        url = None

    super(AI4GAmazonRainforest, self).__init__(weights=weights, device=device,
                                               num_cls=36, num_layers=50, url=url)

results_generation(logits, img_ids, id_strip=None)

Generate results for classification.

Parameters:

Name Type Description Default
logits Tensor

Output tensor from the model.

 required
img_ids str

Image identifier.

 required
id_strip str

stiping string for better image id saving.

None

Returns:

Name Type Description
dict list[dict]

Dictionary containing image ID, prediction, and confidence score.
Source code in PytorchWildlife/models/classification/resnet_base/amazon.py 
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def results_generation(self, logits: torch.Tensor, img_ids: list[str], id_strip: str = None) -> list[dict]:
    """
    Generate results for classification.

    Args:
        logits (torch.Tensor): Output tensor from the model.
        img_ids (str): Image identifier.
        id_strip (str): stiping string for better image id saving.       

    Returns:
        dict: Dictionary containing image ID, prediction, and confidence score.
    """

    probs = torch.softmax(logits, dim=1)
    preds = probs.argmax(dim=1)
    confs = probs.max(dim=1)[0]
    confidences = probs[0].tolist()
    result = [[self.CLASS_NAMES[i], confidence] for i, confidence in enumerate(confidences)]

    results = []
    for pred, img_id, conf in zip(preds, img_ids, confs):
        r = {"img_id": str(img_id).strip(id_strip)}
        r["prediction"] = self.CLASS_NAMES[pred.item()]
        r["class_id"] = pred.item()
        r["confidence"] = conf.item()
        r["all_confidences"] = result
        results.append(r)

    return results