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fixed run_val

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nielseni6 2024-10-15 11:53:27 -04:00
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commit 2a95a652bd
1 changed files with 17 additions and 41 deletions
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run_val.py
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@ -1,51 +1,27 @@
from ultralytics import YOLOv10
import torch
from PIL import Image
from torchvision import transforms
from ultralytics import YOLOv10, YOLO
# from ultralytics.engine.pgt_trainer import PGTTrainer
# from ultralytics import BaseTrainer
# from ultralytics.engine.trainer import BaseTrainer
import os
# Define the device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Set CUDA device (only needed for multi-gpu machines)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
# model = YOLOv10()
# model = YOLO()
# If you want to finetune the model with pretrained weights, you could load the
# pretrained weights like below
# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# model = YOLOv10.from_pretrained('jameslahm/yolov10n')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt
# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model = YOLOv10('yolov10n.pt').to(device)
model = YOLOv10('yolov10n.pt')
# Load the image
# path = '/home/nielseni6/PythonScripts/Github/yolov10/images/fat-dog.jpg'
path = '/home/nielseni6/PythonScripts/Github/yolov10/images/The-Cardinal-Bird.jpg'
image = Image.open(path)
# Evaluate the model on the validation set
results = model.val(data='coco.yaml')
# Define the transformation to resize the image, convert it to a tensor, and normalize it
transform = transforms.Compose([
transforms.Resize((640, 640)),
transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Apply the transformation
image_tensor = transform(image)
# Add a batch dimension
image_tensor = image_tensor.unsqueeze(0).to(device)
image_tensor = image_tensor.requires_grad_(True)
# Predict for a specific image
# results = model.predict(image_tensor, save=True)
# model.requires_grad_(True)
# for p in model.parameters():
# p.requires_grad = True
results = model.predict(image_tensor, save=True)
# Display the results
for result in results:
print(result)
# Print the evaluation results
print(results)
# pred = results[0].boxes[0].conf