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        "description": "<p align=\"center\"><img width=500 src=\"/docs/images/torchbench.png\"></p>\n\nEasily benchmark PyTorch models on selected tasks and datasets. \n\n## Installation\n\nRequires Python 3.6+. \n\n```bash\npip install torchbench\n```\n\n## Usage\n\nThis library can be used together with the [sotabench](https://sotabench.com) website, or standalone. Below we'll describe the usage with the sotabench website. \n\nSteps to benchmark your model on the sotabench website:\n\n1) Create a `sotabench.py` in the root of your repository. Below you can see an example `sotabench.py` file added to the [torchvision](https://github.com/pytorch/vision/tree/master/torchvision) repository to test one of its constituent models:\n\n```python\nfrom torchbench.image_classification import ImageNet\nfrom torchvision.models.resnet import resnext101_32x8d\nimport torchvision.transforms as transforms\nimport PIL\n\n# Define the transforms need to convert ImageNet data to expected model input\nnormalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\ninput_transform = transforms.Compose([\n    transforms.Resize(256, PIL.Image.BICUBIC),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    normalize,\n])\n\n# Run the benchmark\nImageNet.benchmark(\n    model=resnext101_32x8d(pretrained=True),\n    paper_model_name='ResNeXt-101-32x8d',\n    paper_arxiv_id='1611.05431',\n    input_transform=input_transform,\n    batch_size=256,\n    num_gpu=1\n)\n\n```\n\n2) Run it locally on your machine to verify it works:\n\n```bash\n$ python sotabench.py\n```\n\nIn addition, you can check your parameters (such as model names and arxiv IDs) by running:\n```bash\n$ sb check\n```\n\nAlternatively you can run the same logic within a Notebook if that is your preferred workflow.\n\n3) Login and connect your repository to [sotabench](https://sotabench.com/add-model). After you connect your repository the website will re-evaluate your model on every commit of the `master` branch, to ensure the model is working and results are up-to-date - including if you add additional models to the benchmark file.  \n\nYou can also use the library without the sotabench website, by simply omitting step 3. In that case you also don't need to put in the paper details into the `benchmark()` method. \n\n## Benchmarks\n\n### Image Classification on ImageNet\n\nImage Classification on ImageNet benchmark is implemented in the [image_classification.ImageNet](https://github.com/paperswithcode/torchbench/blob/master/torchbench/image_classification/imagenet.py) class.\n\n#### Benchmarking Pipeline\n\n1. The model is put into evaluation mode and sent to the device\n2. The ImageNet validation dataset is loaded and transformed using `input_transform`\n3. The dataset is put into a DataLoader with options `batch_size` and `num_workers`\n4. The model and dataset are passed into an evaluation function for the task, along with an optional `model_output_transform` function that can transform the outputs after inference\n5. The transformed output is compared to expected output and Top 1 and Top 5 accuracy are calculated\n\n#### Expected Inputs/Outputs\n\n- Model `output` (following `model.forward()` and optionally `model_output_transform`) should be a 2D `torch.Tensor` containing the model output; first dimension should be output for each example (length `batch_size`) and second dimension should be output for each class in ImageNet (length 1000).\n\n### Object Detection on COCO\n\nObject Detection on the COCO benchmark is implemented in the [object_detection.COCO](https://github.com/paperswithcode/torchbench/blob/master/torchbench/object_detection/coco.py) class. \n\n#### Benchmarking Pipeline\n\n1. The model is put into evaluation mode and sent to the device\n2. The COCO dataset is loaded. \n\nWithout `transforms`, COCO dataset returns a tuple for an index where:\n- The first entry is a `PIL.Image`\n- The second entry is a labels dictionary, with keys `'boxes', 'labels', 'masks', 'image_id', 'area', 'iscrowd'`, containing the labels.\n\nWith [default transforms](https://github.com/paperswithcode/torchbench/blob/master/torchbench/object_detection/coco.py), COCO dataset returns a tuple for an index where:\n- The first entry is a `torch.tensor` (representing the image)\n- The second entry is a labels dictionary, with keys `'boxes', 'labels', 'masks', 'image_id', 'area', 'iscrowd'`, where the data is of type `torch.tensor` rather than lists.\n\nYou can specify your own `transforms` to transform the data so your model can process it correctly.\n\n3. The dataset is put into a DataLoader with options `batch_size` and `num_workers`, and collated using [coco_collate_fn](https://github.com/paperswithcode/torchbench/blob/master/torchbench/object_detection/coco.py). Alternatively you can pass in your own collate function. \n4. The model and dataset are passed into an evaluation function for the task, along with `model_output_transform` function that can transform the outputs and targets after inference. The default [model_output_transform](https://github.com/paperswithcode/torchbench/blob/master/torchbench/object_detection/coco.py) follows that used in the torchvision examples, but you can provide your own `model_output_transform` to get your model output in the right format.\n\nThe expected output is a list of dictionaries (length = batch_size), where each dictionary contains keys for `'boxes', 'labels', 'scores', 'masks'`, and each value is of the `torch.tensor` type.\n\n5. 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