
* Added SuperPoint docs * Added tests * Removed commented part * Commit to create and fix add_superpoint branch with a new branch * Fixed dummy_pt_objects * Committed missing files * Fixed README.md * Apply suggestions from code review Fixed small changes Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Moved ImagePointDescriptionOutput from modeling_outputs.py to modeling_superpoint.py * Removed AutoModelForKeypointDetection and related stuff * Fixed inconsistencies in image_processing_superpoint.py * Moved infer_on_model logic simply in test_inference * Fixed bugs, added labels to forward method with checks whether it is properly a None value, also added tests about this logic in test_modeling_superpoint.py * Added tests to SuperPointImageProcessor to ensure that images are properly converted to grayscale * Removed remaining mentions of MODEL_FOR_KEYPOINT_DETECTION_MAPPING * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Fixed from (w, h) to (h, w) as input for tests * Removed unnecessary condition * Moved last_hidden_state to be the first returned * Moved last_hidden_state to be the first returned (bis) * Moved last_hidden_state to be the first returned (ter) * Switched image_width and image_height in tests to match recent changes * Added config as first SuperPointConvBlock init argument * Reordered README's after merge * Added missing first config argument to SuperPointConvBlock instantiations * Removed formatting error * Added SuperPoint to README's de, pt-br, ru, te and vi * Checked out README_fr.md * Fixed README_fr.md * Test fix README_fr.md * Test fix README_fr.md * Last make fix-copies ! * Updated checkpoint path * Removed unused SuperPoint doc * Added missing image * Update src/transformers/models/superpoint/modeling_superpoint.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Removed unnecessary import * Update src/transformers/models/superpoint/modeling_superpoint.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Added SuperPoint to _toctree.yml --------- Co-authored-by: steven <steven.bucaillle@gmail.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
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🤗 Transformers
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.
🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:
📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.
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Contents
The documentation is organized into five sections:
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GET STARTED provides a quick tour of the library and installation instructions to get up and running.
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TUTORIALS are a great place to start if you're a beginner. This section will help you gain the basic skills you need to start using the library.
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HOW-TO GUIDES show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.
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CONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
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API describes all classes and functions:
- MAIN CLASSES details the most important classes like configuration, model, tokenizer, and pipeline.
- MODELS details the classes and functions related to each model implemented in the library.
- INTERNAL HELPERS details utility classes and functions used internally.
Supported models and frameworks
The table below represents the current support in the library for each of those models, whether they have a Python tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via Flax), PyTorch, and/or TensorFlow.