Update README.md

README.md

@@ -1,8 +1,8 @@
-
 ---
 tags:
-- autotrain
 - image-classification
+- auto-train
+- vision
 widget:
 - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
   example_title: Tiger
@@ -12,11 +12,50 @@ widget:
   example_title: Palace
 datasets:
 - Falah/Blood_8_classes_Dataset
+license: apache-2.0
+pipeline_tag: image-classification
 ---
 
-# Model Trained Using AutoTrain
 
-- Problem type: Image Classification
+# ResNet-50 v1.5
+
+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. 
+
+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.
+
+## Model description
+
+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.
+
+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 small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)
+
 
 ## Validation Metrics
 No validation metrics available
+
+
+### How to use
+
+Here is how to use this model to classify an image of :
+
+```python
+from transformers import AutoImageProcessor, ResNetForImageClassification
+import torch
+from datasets import load_dataset
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
+model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
+
+inputs = processor(image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# model predicts one of the 1000 ImageNet classes
+predicted_label = logits.argmax(-1).item()
+print(model.config.id2label[predicted_label])