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\n\n--------------------------------------------------------------------------------\n\n[](https://badge.fury.io/py/sotabencheval) [](https://paperswithcode.github.io/sotabench-eval/)\n\n`sotabencheval` is a framework-agnostic library that contains a collection of deep learning benchmarks you can use to benchmark your models. It can be used in conjunction with the [sotabench](https://www.sotabench.com) service to record results for models, so the community can compare model performance on different tasks, as well as a continuous integration style service for your repository to benchmark your models on each commit.\n\n## Benchmarks Supported\n\n- [ADE20K](https://paperswithcode.github.io/sotabench-eval/ade20k/) (Semantic Segmentation)\n- [COCO](https://paperswithcode.github.io/sotabench-eval/coco/) (Object Detection)\n- [ImageNet](https://paperswithcode.github.io/sotabench-eval/imagenet/) (Image Classification)\n- [SQuAD](https://paperswithcode.github.io/sotabench-eval/squad/) (Question Answering)\n- [WikiText-103](https://paperswithcode.github.io/sotabench-eval/wikitext-103/) (Language Modelling)\n- [WMT](https://paperswithcode.github.io/sotabench-eval/wmt/) (Machine Translation)\n\nPRs welcome for further benchmarks! \n\n## Installation\n\nRequires Python 3.6+. \n\n```bash\npip install sotabench-eval\n```\n\n## Get Benching! \ud83c\udfcb\ufe0f\n\nYou should read the [full documentation here](https://paperswithcode.github.io/sotabench-eval/index.html), which contains guidance on getting started and connecting to [sotabench](https://www.sotabench.com).\n\nIntegration is lightweight. For example, if you are evaluating an ImageNet model, you initialize an Evaluator object and (optionally) link to any linked paper:\n\n```python\nfrom sotabencheval.image_classification import ImageNetEvaluator\nevaluator = ImageNetEvaluator(\n model_name='FixResNeXt-101 32x48d',\n paper_arxiv_id='1906.06423')\n```\n\nThen for each batch of predictions your model makes on ImageNet, pass a dictionary of keys as image IDs and values as a `np.ndarray`s of logits to the `evaluator.add` method:\n\n```python\nevaluator.add(output_dict=dict(zip(image_ids, batch_output)))\n```\n\nThe evaluation logic just needs to be written in a `sotabench.py` file and sotabench will run it on each commit and record the results:\n\n![](\"/docs/docs/img/results.png\")
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