Add model card

README.md

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+---
+tags:
+- resnet
+license: apache-2.0
+datasets:
+- imagenet
+---
+
+# ResNet-50d
+
+Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in
+[this paper](https://arxiv.org/abs/1512.03385) and is adapted with the ResNet-D trick from
+[this paper](https://arxiv.org/abs/1812.01187)
+
+
+## Model description
+
+ResNet are deep convolutional neural networks using residual connections. Each layer is composed of two convolutions
+with a ReLU in the middle, but the output is the sum of the input with the output of the convolutional blocks.
+
+![](https://neurohive.io/wp-content/uploads/2019/01/resnet-e1548261477164.png)
+
+This way, there is a direct connection from the original inputs to even the deepest layers in the network.
+
+## Intended uses & limitations
+
+You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
+to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
+object detection, to name a few).
+
+### How to use
+
+You can use this model with the usual factory method in `timm`:
+
+```python
+import PIL
+import timm
+import torch
+
+model = timm.create_model("sgugger/resnet50d")
+img = PIL.Image.open(path_to_an_image)
+img = img.convert("RGB")
+
+config = model.default_cfg
+
+if isinstance(config["input_size"], tuple):
+    img_size = config["input_size"][-2:]
+else:
+    img_size = config["input_size"]
+
+transform = timm.data.transforms_factory.transforms_imagenet_eval(
+    img_size=img_size,
+    interpolation=config["interpolation"],
+    mean=config["mean"],
+    std=config["std"],
+)
+
+input_tensor = transform(cat_img)
+input_tensor = input_tensor.unsqueeze(0)
+# ^ batch size = 1
+with torch.no_grad():
+    output = model(input_tensor)
+
+probs = output.squeeze(0).softmax(dim=0)
+```
+
+### Limitations and bias
+
+The training images in the dataset are usually photos clearly representing one of the 1,000 labels. The model will
+probably not generalize well on drawings or images containing multiple objects with different labels.
+
+The training images in the dataset come mostly from the US (45.4%) and Great Britain (7.6%). As such the model or
+models created by fine-tuning this model will work better on images picturing scenes from these countries (see 
+[this paper](https://arxiv.org/abs/1906.02659) for examples).
+
+More generally, [recent research](https://arxiv.org/abs/2010.15052) has shown that even models trained in an
+unsupervised fashion on ImageNet (i.e. without using the labels) will pick up racial and gender bias represented in
+the training images.
+
+## Training data
+
+This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 millions of
+hand-annotated images with 1,000 categories.
+
+## Training procedure
+
+### Preprocessing
+
+The images are resized using bicubic interpolation to 224x224 and normalized with the usual ImageNet statistics.
+
+## Evaluation results
+
+This model has a top1-accuracy of 80.53% and a top-5 accuracy of 95.16% in the evaluation set of ImageNet
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/HeZRS15,
+  author    = {Kaiming He and
+               Xiangyu Zhang and
+               Shaoqing Ren and
+               Jian Sun},
+  title     = {Deep Residual Learning for Image Recognition},
+  journal   = {CoRR},
+  volume    = {abs/1512.03385},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1512.03385},
+  archivePrefix = {arXiv},
+  eprint    = {1512.03385},
+  timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```