nielsr HF staff regisss HF staff commited on
Commit
aec901e
1 Parent(s): 8d711cf

Create README.md (#1)

Browse files

- Create README.md (5fec255894d497aac3cd6e299432dab85444a9f5)
- Remove imagenet tag (4ef075409f32da36ade0c89970fbde12605e6acf)


Co-authored-by: Régis Pierrard <regisss@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +66 -0
README.md ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - vision
5
+ - image-classification
6
+ datasets:
7
+ - imagenet-1k
8
+ ---
9
+
10
+ # ResNet-50 v1.5
11
+
12
+ ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
13
+
14
+ Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
15
+
16
+ ## Model description
17
+
18
+ ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
19
+
20
+ This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a smallperformance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
21
+
22
+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)
23
+
24
+ ## Intended uses & limitations
25
+
26
+ You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
27
+ fine-tuned versions on a task that interests you.
28
+
29
+ ### How to use
30
+
31
+ Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
32
+
33
+ ```python
34
+ from transformers import AutoFeatureExtractor, ResNetForImageClassification
35
+ import torch
36
+ from datasets import load_dataset
37
+
38
+ dataset = load_dataset("huggingface/cats-image")
39
+ image = dataset["test"]["image"][0]
40
+
41
+ feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
42
+ model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
43
+
44
+ inputs = feature_extractor(image, return_tensors="pt")
45
+
46
+ with torch.no_grad():
47
+ logits = model(**inputs).logits
48
+
49
+ # model predicts one of the 1000 ImageNet classes
50
+ predicted_label = logits.argmax(-1).item()
51
+ print(model.config.id2label[predicted_label])
52
+ ```
53
+
54
+ For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
55
+
56
+ ### BibTeX entry and citation info
57
+
58
+ ```bibtex
59
+ @inproceedings{he2016deep,
60
+ title={Deep residual learning for image recognition},
61
+ author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
62
+ booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
63
+ pages={770--778},
64
+ year={2016}
65
+ }
66
+ ```