WILDBOOK IA - MiewID Plugin

A plugin for matching and interpreting embeddings for wildlife identification.

Setup

pip install -r requirements.txt
pip install -e .

Optionally, these environment variables must be set to enable Weights and Biases logging capability:

WANDB_API_KEY={your_wanb_api_key}
WANDB_MODE={'online'/'offline'}

Multispecies Models

Model specs and dataset overview can be found at the model card page for the Multispecies-v2 model and the Multispecies-v3 model

Pretrained Model Embeddings Extraction

import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
from transformers import AutoModel

model_tag = f"conservationxlabs/miewid-msv2"
model = AutoModel.from_pretrained(model_tag, trust_remote_code=True)

def generate_random_image(height=440, width=440, channels=3):
    random_image = np.random.randint(0, 256, (height, width, channels), dtype=np.uint8)
    return Image.fromarray(random_image)

random_image = generate_random_image()

preprocess = transforms.Compose([
    transforms.Resize((440, 440)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

input_tensor = preprocess(random_image)
input_batch = input_tensor.unsqueeze(0) 

with torch.no_grad():
    output = model(input_batch)

print(output)
print(output.shape)

Pretrained Model Evaluation

import torch
from wbia_miew_id.evaluate import Evaluator
from transformers import AutoModel

evaluator = Evaluator(
    device=torch.device('cuda'),
    seed=0,
    anno_path='beluga_example_miewid/benchmark_splits/test.csv',
    name_keys=['name'],
    viewpoint_list=None,
    use_full_image_path=True,
    images_dir=None,
    image_size=(440, 440),
    crop_bbox=True,
    valid_batch_size=12,
    num_workers=8,
    eval_groups=[['species', 'viewpoint']],
    fliplr=False,
    fliplr_view=[],
    n_filter_min=2,
    n_subsample_max=10,
    model_params=None,
    checkpoint_path=None,
    model=model,
    visualize=False,
    visualization_output_dir='beluga_example_visualizations'
)

Example Usage

Example dataset download

cd wbia_miew_id
python examples/download_example.py

Training

python train.py --config=examples/beluga_example_miewid/benchmark_model/miew_id.msv2_all.yaml

Evaluation

python evaluate.py --config=examples/beluga_example_miewid/benchmark_model/miew_id.msv2_all.yaml

Optional --visualize flag can be used to produce top 5 match results for each individual in the test set, along with gradcam visualizations.

Data Splitting, Training, and Evaluation Using Python Bindings

Demo notebooks are avaliable at examples directory

Data files

Example dataset

The data is expected to be in the CSV or COCO JSON Format.

[Recommended] The CSV beluga data can be downlaoded from here.

The COCO beluga data can be downloaded from here.

Expected CSV data format

• theta: Bounding box rotation in radians
• viewpoint: Viewpoint of the individual facing the camera. Used for calculating per-viewpoint stats or separating individuals based on viewpoint
• name: Individual ID
• file_name: File name
• viewpoint: Species name. Used for calculating per-species stats
• file_path: Full path to images
• x, y, w, h: Bounding box coordinates

theta	viewpoint	name	file_name	species	file_path	x	y	w	h
0	up	1030	000000006040.jpg	beluga_whale	/datasets/beluga-440/000000006040.jpg	0	0	162	440
0	up	1030	000000006043.jpg	beluga_whale	/datasets/beluga-440/000000006043.jpg	0	0	154	440
0	up	508	000000006044.jpg	beluga_whale	/datasets/beluga-440/000000006044.jpg	0	0	166	440

Configuration file

A config file path can be set by: python train.py --config {path_to_config}

• exp_name: Name of the experiment
• project_name: Name of the project
• checkpoint_dir: Directory for storing training checkpoints
• comment: Comment text for the experiment
• viewpoint_list: List of viewpoint values to keep for all subsets.
• data: Subfield for data-related settings
  • images_dir: Directory containing the all of the dataset images
  • use_full_image_path: Overrides the images_dir for path construction and instead uses an absolute path that should be defined in the file_path file path under the images entries for each entry in the COCO JSON. In such a case, images_dir can be set to null
  • crop_bbox: Whether to use the bounding box metadata to crop the images. The crops will also be adjusted for rotation if the theta field is present for the annotations
  • preprocess_images pre-applies cropping and resizing and caches the images for training
  • train: Data parameters regarding the train set used in train.py
    • anno_path: Path to the JSON file containing the annotations
    • n_filter_min: Minimum number of samples per name (individual) to keep that individual in the set. Names under the threshold will be discarded
    • n_subsample_max: Maximum number of samples per name to keep for the training set. Annotations for names over the threshold will be randomly subsampled once at the start of training
  • val: Data parameters regarding the validation set used in train.py
    • anno_path
    • n_filter_min
    • n_subsample_max
  • test: Data parameters regarding the test set used in test.py
    • anno_path
    • n_filter_min
    • n_subsample_max
    • checkpoint_path: Path to model checkpoint to test
    • eval_groups: Attributes for which to group the testing sets. For example, the value of ['viewpoint'] will create subsets of the test set for each unique value of the viewpoint and run one-vs-all evaluation for each subset separately. The value can be a list - [['species', 'viewpoint']] will run evaluation separately for each species+viewpoint combination. ['species', 'viewpoint'] will run grouped eval for each species, and then for each viewpoint. The corresponding fields to be grouped should be present under annotation entries in the COCO file. Can be left as null to do eval for the full test set.
  • name_keys: List of keys used for defining a unique name (individual). Fields from multiple keys will be combined to form the final representation of a name. A common use-case is name_keys: ['name', 'viewpoint'] for treating each name + viewpoint combination as a unique individual
  • image_size:
    • Image height to resize to
    • Image width to resize to
• engine: Subfields for engine-related settings
  • num_workers: Number of workers for data loading (default: 0)
  • train_batch_size: Batch size for training
  • valid_batch_size: Batch size for validation
  • epochs: Number of training epochs
  • seed: Random seed for reproducibility
  • device: Device to be used for training
  • use_wandb: Whether to use Weights and Biases for logging
  • use_swa: Whether to use SWA during training
• scheduler_params: Subfields for learning rate scheduler parameters
  • lr_start: Initial learning rate
  • lr_max: Maximum learning rate
  • lr_min: Minimum learning rate
  • lr_ramp_ep: Number of epochs to ramp up the learning rate
  • lr_sus_ep: Number of epochs to sustain the maximum learning rate
  • lr_decay: Rate of learning rate decay per epoch
• model_params: Dictionary containing model-related settings
  • model_name: Name of the model backbone architecture
  • use_fc: Whether to use a fully connected layer after backbone extraction
  • fc_dim: Dimension of the fully connected layer
  • dropout: Dropout rate
  • loss_module: Loss function module
  • s: Scaling factor for the loss function
  • margin: Margin for the loss function
  • pretrained: Whether to use a pretrained model backbone
  • n_classes: Number of classes in the training dataset, used for loading checkpoint
• swa_params: Subfields for SWA training
  • swa_lr: SWA learning rate
  • swa_start: Epoch number to begin SWA training
• test: Subfields for plugin-related settings
  • fliplr: Whether to perform horizontal flipping during testing
  • fliplr_view: List of viewpoints to apply horizontal flipping
  • batch_size: Batch size for plugin inference

Citation

@misc{WildMe2023,
  author = {Otarashvili, Lasha},
  title = {MiewID},
  year = {2023},
  url = {https://github.com/WildMeOrg/wbia-plugin-miew-id},
  doi = {10.5281/zenodo.13647526},
}