MohamedKhayat/fruit-detector-deformable-detr
019
Fruit Detector - defor_detr
This model is a fine-tuned version of SenseTime/deformable-detr for fruit and vegetable detection.
Model Details
- Base Model: SenseTime/deformable-detr
- Architecture: defor_detr
- Task: Object Detection
- mAP@50:95 Score: 0.5961
- Input Size: 640x640
Classes
The model detects the following 12 fruit/vegetable classes:
Usage
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import torch
# Load model and processor
processor = AutoImageProcessor.from_pretrained("MohamedKhayat/fruit-detector-deformable-detr")
model = AutoModelForObjectDetection.from_pretrained("MohamedKhayat/fruit-detector-deformable-detr")
# Load and process image
image = Image.open("fruit_image.jpg")
inputs = processor(images=image, return_tensors="pt")
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Post-process results
target_sizes = torch.tensor([[image.height, image.width]])
results = processor.post_process_object_detection(
outputs,
threshold=0.5,
target_sizes=target_sizes
)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = box.tolist()
print(f"Detected {model.config.id2label[label.item()]} with confidence {score:.2f} at {box}")Training
This model was trained on a custom fruit detection dataset.
Training Repository: transformers-for-fruit-object-detection-internship
License
Apache 2.0