![]() ![]() MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.MMYOLO: OpenMMLab YOLO series toolbox and benchmark.MMRotate: OpenMMLab rotated object detection toolbox and benchmark.MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.MMDetection: OpenMMLab detection toolbox and benchmark.MMClassification: OpenMMLab image classification toolbox and benchmark.MMEval: A unified evaluation library for multiple machine learning libraries.MMCV: OpenMMLab foundational library for computer vision.MMEngine: OpenMMLab foundational library for training deep learning models.This project is released under the Apache 2.0 license. If you find this project useful in your research, please consider 2020mmclassification, We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. Please refer to CONTRUBUTING.md for the contributing guideline. We appreciate all contributions to improve MMClassification. Results and models are available in the model zoo. We provided a series of tutorials about the basic usage of MMClassification for new users: Please refer to install.md for more detailed installation and dataset preparation. InstallationĬonda create -n open-mmlab python=3.8 pytorch=1.10.1 torchvision=0.11.2 cudatoolkit=11.3 -c pytorch -y Please refer to changelog.md for more details and other release history. ![]() And we will still maintain 0.x version still at least the end of 2023. The release candidate will last until the end of 2022, and during the release candidate, we will develop on the 1.x branch. WelcomeĪnd there are some BC-breaking changes. This release introduced a brand new and flexible training & test engine, but it's still in progress. Reproduce mobileone training accuracy.Now you can train/use models in TIMM/HuggingFace directly, see #1102. Add Switch Recipe Hook, Now we can modify training pipeline, mixup and loss settings during training, see #1101.Refactor BEiT backbone and support v1/v2 inference.Upgrade API to get pre-defined models of MMClassification.Support multi-task training and testing.Reproduce the training accuracy of ConvNeXt and RepVGG.Support EVA, RevViT, EfficientnetV2, CLIP, TinyViT and MixMIM backbones.Various backbones and pretrained models.Finally the values are first rescaled to and then normalized using mean= and std=. The images are resized to resize_size= using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=. The inference transforms are available at Inception_V3_Weights.IMAGENET1K_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. These weights are ported from the original paper. weights='DEFAULT' or weights='IMAGENET1K_V1'. Inception_V3_Weights.DEFAULT is equivalent to Inception_V3_Weights.IMAGENET1K_V1. The inference transforms are available at Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. Tench, goldfish, great white shark, … (997 omitted) These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1: weights='DEFAULT' or weights='IMAGENET1K_FBGEMM_V1'. Inception_V3_QuantizedWeights.DEFAULT is equivalent to Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1. The model builder above accepts the following values as the weights parameter. Inception_V3_QuantizedWeights ( value ) ¶ **kwargs – parameters passed to the 3īase class. Quantize ( bool, optional) – If True, return a quantized version of the model. Progress ( bool, optional) – If True, displays a progress bar of the download to stderr. Weights ( Inception_V3_QuantizedWeights or Inception_V3_Weights, optional) – The pretrained Quantized models only support inference and run on CPUs. Note that quantize = True returns a quantized model with 8 bit ![]()
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