القائمة
Build computer vision solutions - image classification, object detection, and transfer learning
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-machine-learning --skill computer-visionيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Discover patterns in unlabeled data using clustering, dimensionality reduction, and anomaly detection
Build and train neural networks with PyTorch - MLPs, CNNs, and training best practices
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
| name | computer-vision |
| description | Build computer vision solutions - image classification, object detection, and transfer learning |
| version | 1.4.0 |
| sasmp_version | 1.4.0 |
| bonded_agent | 06-computer-vision |
| bond_type | PRIMARY_BOND |
| parameters | {"required":[{"name":"images","type":"tensor|array","validation":"4D tensor [B, C, H, W] or list of images"}],"optional":[{"name":"model_name","type":"string","default":"efficientnet_b0"},{"name":"num_classes","type":"integer","default":1000}]} |
| retry_logic | {"strategy":"exponential_backoff","max_attempts":3,"base_delay_ms":1000} |
| logging | {"level":"info","metrics":["inference_time","batch_size","image_size"]} |
Build visual AI systems from classification to detection.
import torch
import timm
from PIL import Image
from torchvision import transforms
# Load pretrained model
model = timm.create_model('efficientnet_b0', pretrained=True, num_classes=10)
model.eval()
# Preprocessing
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Inference
image = Image.open('image.jpg').convert('RGB')
input_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
output = model(input_tensor)
predicted_class = output.argmax(dim=1).item()
import albumentations as A
from albumentations.pytorch import ToTensorV2
train_transform = A.Compose([
A.RandomResizedCrop(224, 224, scale=(0.8, 1.0)),
A.HorizontalFlip(p=0.5),
A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=15),
A.ColorJitter(brightness=0.2, contrast=0.2),
A.Normalize(mean=[, , ], std=[, , ]),
ToTensorV2()
])
val_transform = A.Compose([
A.Resize(, ),
A.CenterCrop(, ),
A.Normalize(mean=[, , ], std=[, , ]),
ToTensorV2()
])
| Model | Params | ImageNet Acc | Speed |
|---|---|---|---|
| EfficientNet-B0 | 5.3M | 77% | Fast |
| ResNet-50 | 25.6M | 76% | Fast |
| ViT-B/16 | 86M | 84% | Slow |
import timm
class TransferClassifier(torch.nn.Module):
def __init__(self, backbone='efficientnet_b0', num_classes=10):
super().__init__()
self.backbone = timm.create_model(backbone, pretrained=True, num_classes=0)
self.classifier = torch.nn.Linear(self.backbone.num_features, num_classes)
# Freeze backbone
for param in self.backbone.parameters():
param.requires_grad = False
def unfreeze(self):
for param in self.backbone.parameters():
param.requires_grad = True
def forward(self, x):
features = self.backbone(x)
return self.classifier(features)
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n.pt')
# Train
results = model.train(
data='dataset.yaml',
epochs=100,
imgsz=640,
batch=16
)
# Inference
results = model('image.jpg')
for r in results:
boxes = r.boxes
for box in boxes:
print(f"Class: {r.names[int(box.cls)]}, Conf: {box.conf:.2f}")
import segmentation_models_pytorch as smp
# Create U-Net model
model = smp.Unet(
encoder_name='resnet50',
encoder_weights='imagenet',
in_channels=3,
classes=21
)
# Loss function
loss_fn = smp.losses.DiceLoss(mode='multiclass')
from sklearn.metrics import classification_report, confusion_matrix
def evaluate_classifier(model, dataloader, device):
model.eval()
all_preds, all_labels = [], []
with torch.no_grad():
for images, labels in dataloader:
outputs = model(images.to(device))
preds = outputs.argmax(dim=1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.numpy())
return {
'report': classification_report(all_labels, all_preds),
'confusion_matrix': confusion_matrix(all_labels, all_preds)
}
# TODO: Fine-tune EfficientNet on CIFAR-10
# Freeze backbone first, then unfreeze
# TODO: Train YOLOv8 on custom dataset
# Create dataset.yaml and train
import pytest
import torch
def test_model_output_shape():
"""Test model output dimensions."""
model = TransferClassifier(num_classes=10)
x = torch.randn(4, 3, 224, 224)
output = model(x)
assert output.shape == (4, 10)
def test_augmentation_preserves_shape():
"""Test augmentation output shape."""
import numpy as np
image = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
augmented = train_transform(image=image)['image']
assert augmented.shape == (3, 224, 224)
| Problem | Cause | Solution |
|---|---|---|
| Overfitting | Small dataset | More augmentation |
| Slow training | Large images | Resize, use AMP |
| Poor detection | Wrong anchors | Adjust anchor sizes |
| Memory error | Batch too large | Reduce batch size |
06-computer-visionnlp-basicsml-deploymentVersion: 1.4.0 | Status: Production Ready