Update README.md

README.md

@@ -1,12 +1,19 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- object-detection
+- vision
+- chess
+license: apache-2.0
+base_model:
+- facebook/detr-resnet-50
 ---
 
-# Model Card for Model ID
+# DETR (End-to-End Object Detection) model with ResNet-50 backbone fine-tuned on chess pieces
 
 <!-- Provide a quick summary of what the model is/does. -->
 
+DEtection TRansformer (DETR) model trained end-to-end on Chess pieces recognition dataset
 
 
 ## Model Details
@@ -14,8 +21,8 @@ tags: []
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. 
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
@@ -37,10 +44,37 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
+### How To Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+```python
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image
+import requests
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = DetrImageProcessor.from_pretrained("aesat/detr-finetuned-chess", revision="no_timm")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-finetuned-chess", revision="no_timm")
+
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+
+# convert outputs (bounding boxes and class logits) to COCO API
+# let's only keep detections with score > 0.9
+target_sizes = torch.tensor([image.size[::-1]])
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 2) for i in box.tolist()]
+    print(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+    )
+```
 [More Information Needed]
 
 ### Downstream Use [optional]