Instructions to use vickygupta1996/VGG16_Sign_Verification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use vickygupta1996/VGG16_Sign_Verification with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("vickygupta1996/VGG16_Sign_Verification") - Notebooks
- Google Colab
- Kaggle
|
Download SignatureDetection/ultralytics/docs/usage/callbacks.md from vickygupta1996/VGG16_Sign_Verification: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/vickygupta1996/VGG16_Sign_Verification/resolve/main/SignatureDetection/ultralytics/docs/usage/callbacks.md
- Command line
-
hf download hf://vickygupta1996/VGG16_Sign_Verification/SignatureDetection/ultralytics/docs/usage/callbacks.md
-
curl -L -o callbacks.md https://huggingface.co/vickygupta1996/VGG16_Sign_Verification/resolve/main/SignatureDetection/ultralytics/docs/usage/callbacks.md
4.41 kB
| comments: true | |
| description: Learn how to utilize callbacks in the Ultralytics framework during train, val, export, and predict modes for enhanced functionality. | |
| keywords: Ultralytics, YOLO, callbacks guide, training callback, validation callback, export callback, prediction callback | |
| ## Callbacks | |
| Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes. | |
| Each callback accepts a `Trainer`, `Validator`, or `Predictor` object depending on the operation type. All properties of | |
| these objects can be found in Reference section of the docs. | |
| ## Examples | |
| ### Returning additional information with Prediction | |
| In this example, we want to return the original frame with each result object. Here's how we can do that | |
| ```python | |
| def on_predict_batch_end(predictor): | |
| # Retrieve the batch data | |
| _, im0s, _, _ = predictor.batch | |
| # Ensure that im0s is a list | |
| im0s = im0s if isinstance(im0s, list) else [im0s] | |
| # Combine the prediction results with the corresponding frames | |
| predictor.results = zip(predictor.results, im0s) | |
| # Create a YOLO model instance | |
| model = YOLO(f'yolov8n.pt') | |
| # Add the custom callback to the model | |
| model.add_callback("on_predict_batch_end", on_predict_batch_end) | |
| # Iterate through the results and frames | |
| for (result, frame) in model.track/predict(): | |
| pass | |
| ``` | |
| ## All callbacks | |
| Here are all supported callbacks. See callbacks [source code](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/base.py) for additional details. | |
| ### Trainer Callbacks | |
| | Callback | Description | | |
| |-----------------------------|---------------------------------------------------------| | |
| | `on_pretrain_routine_start` | Triggered at the beginning of pre-training routine | | |
| | `on_pretrain_routine_end` | Triggered at the end of pre-training routine | | |
| | `on_train_start` | Triggered when the training starts | | |
| | `on_train_epoch_start` | Triggered at the start of each training epoch | | |
| | `on_train_batch_start` | Triggered at the start of each training batch | | |
| | `optimizer_step` | Triggered during the optimizer step | | |
| | `on_before_zero_grad` | Triggered before gradients are zeroed | | |
| | `on_train_batch_end` | Triggered at the end of each training batch | | |
| | `on_train_epoch_end` | Triggered at the end of each training epoch | | |
| | `on_fit_epoch_end` | Triggered at the end of each fit epoch | | |
| | `on_model_save` | Triggered when the model is saved | | |
| | `on_train_end` | Triggered when the training process ends | | |
| | `on_params_update` | Triggered when model parameters are updated | | |
| | `teardown` | Triggered when the training process is being cleaned up | | |
| ### Validator Callbacks | |
| | Callback | Description | | |
| |----------------------|-------------------------------------------------| | |
| | `on_val_start` | Triggered when the validation starts | | |
| | `on_val_batch_start` | Triggered at the start of each validation batch | | |
| | `on_val_batch_end` | Triggered at the end of each validation batch | | |
| | `on_val_end` | Triggered when the validation ends | | |
| ### Predictor Callbacks | |
| | Callback | Description | | |
| |------------------------------|---------------------------------------------------| | |
| | `on_predict_start` | Triggered when the prediction process starts | | |
| | `on_predict_batch_start` | Triggered at the start of each prediction batch | | |
| | `on_predict_postprocess_end` | Triggered at the end of prediction postprocessing | | |
| | `on_predict_batch_end` | Triggered at the end of each prediction batch | | |
| | `on_predict_end` | Triggered when the prediction process ends | | |
| ### Exporter Callbacks | |
| | Callback | Description | | |
| |-------------------|------------------------------------------| | |
| | `on_export_start` | Triggered when the export process starts | | |
| | `on_export_end` | Triggered when the export process ends | | |