DETR (End-to-End Object Detection) model with ResNet-50 backbone fine-tuned on chess pieces
DEtection TRansformer (DETR) model trained end-to-end on Chess pieces recognition dataset
Model Details
Model Description
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.
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Uses
How To Use
1from transformers import DetrImageProcessor, DetrForObjectDetection
2import torch
3from PIL import Image
4import requests
5
6url = "http://images.cocodataset.org/val2017/000000039769.jpg"
7image = Image.open(requests.get(url, stream=True).raw)
8
9processor = DetrImageProcessor.from_pretrained("aesat/detr-finetuned-chess", revision="no_timm")
10model = DetrForObjectDetection.from_pretrained("facebook/detr-finetuned-chess", revision="no_timm")
11
12inputs = processor(images=image, return_tensors="pt")
13outputs = model(**inputs)
14
15# convert outputs (bounding boxes and class logits) to COCO API
16# let's only keep detections with score > 0.9
17target_sizes = torch.tensor([image.size[::-1]])
18results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
19
20for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
21 box = [round(i, 2) for i in box.tolist()]
22 print(
23 f"Detected {model.config.id2label[label.item()]} with confidence "
24 f"{round(score.item(), 3)} at location {box}"
25 )
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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