Compare CNN architectures for vision tasks - ResNet vs EfficientNet vs MobileNet
Compare CNN architectures (ResNet, EfficientNet, MobileNet) for your deployment target—cloud, edge, mobile, or small datasets. Get specific recommendations based on accuracy, latency, and parameter efficiency benchmarks.
/plugin marketplace add tachyon-beep/skillpacks/plugin install yzmir-neural-architectures@foundryside-marketplace[constraint: cloud|edge|mobile|accuracy]You are comparing CNN architectures for a computer vision task. Provide data-driven comparison based on constraints.
EfficientNet dominates ResNet on efficiency. MobileNet wins on mobile. Match architecture to deployment target.
| Architecture | Params | GFLOPs | ImageNet Acc | Best For |
|---|---|---|---|---|
| ResNet-18 | 11M | 1.8 | 69.8% | Small datasets, baselines |
| ResNet-50 | 25M | 4.1 | 76.1% | General cloud, detection backbone |
| ResNet-101 | 44M | 7.8 | 77.4% | High accuracy cloud |
| EfficientNet-B0 | 5M | 0.4 | 77.3% | Efficiency priority |
| EfficientNet-B2 | 9M | 1.0 | 80.3% | Balanced production |
| EfficientNet-B4 | 19M | 4.2 | 82.9% | High accuracy, efficient |
| EfficientNet-B7 | 66M | 37 | 84.4% | Maximum accuracy |
| MobileNetV3-Small | 2.5M | 0.06 | 67.4% | Mobile real-time |
| MobileNetV3-Large | 5.4M | 0.2 | 75.2% | Mobile balanced |
| EfficientNet-Lite0 | 4.7M | 0.4 | 75.0% | Edge devices |
Comparison:
Same ~76% accuracy:
├─ ResNet-50: 25M params, 4.1 GFLOPs
└─ EfficientNet-B0: 5M params, 0.4 GFLOPs ← 5× fewer params, 10× fewer FLOPs!
Better accuracy (83%):
├─ ResNet-152: 60M params, 11.6 GFLOPs → 78.3%
└─ EfficientNet-B4: 19M params, 4.2 GFLOPs → 82.9% ← Better accuracy, 3× smaller!
Recommendation:
Latency benchmarks (Jetson Nano, FP16):
| Architecture | Latency | Accuracy |
|---|---|---|
| ResNet-50 | 150ms | 76.1% |
| ResNet-18 | 45ms | 69.8% |
| EfficientNet-Lite0 | 25ms | 75.0% |
| MobileNetV3-Large | 20ms | 75.2% |
| MobileNetV3-Small | 12ms | 67.4% |
Recommendation:
Latency benchmarks (iPhone 12, INT8):
| Architecture | Latency | Accuracy |
|---|---|---|
| MobileNetV3-Small | 5-10ms | 67.4% |
| MobileNetV3-Large | 15-25ms | 75.2% |
| EfficientNet-Lite0 | 20-30ms | 75.0% |
Recommendation:
Overfitting risk by model size:
| Architecture | Params | Risk for 10k samples |
|---|---|---|
| ResNet-152 | 60M | HIGH (60M/10k = 6000:1) |
| ResNet-50 | 25M | MEDIUM (25M/10k = 2500:1) |
| ResNet-18 | 11M | LOW (11M/10k = 1100:1) |
| EfficientNet-B0 | 5M | LOWEST (5M/10k = 500:1) |
Recommendation:
Detection frameworks expect:
| Framework | Recommended Backbone |
|---|---|
| Faster R-CNN | ResNet-50 + FPN |
| Mask R-CNN | ResNet-101 + FPN |
| EfficientDet | EfficientNet + BiFPN |
| YOLOv8 | CSPDarknet (built-in) |
| RetinaNet | ResNet-50 + FPN |
Recommendation: Use framework's default backbone unless specific constraint.
Best architecture for each point on accuracy-efficiency frontier:
Accuracy: 67% 70% 75% 77% 80% 83% 84%
│ │ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼ ▼
FLOPs: 0.06G 0.2G 0.4G 0.4G 1.0G 4.2G 37G
MNv3-S MNv3-L EffB0 EffB0 EffB2 EffB4 EffB7
Key insight: EfficientNet dominates every point except ultra-mobile
START: What's your deployment target?
├─ CLOUD (no constraints)
│ └─ Dataset size?
│ ├─ Small (<10k) → EfficientNet-B0 or ResNet-18
│ ├─ Medium (10k-100k) → EfficientNet-B2
│ └─ Large (>100k) → EfficientNet-B4
│
├─ EDGE (Jetson, Coral)
│ └─ Latency requirement?
│ ├─ <15ms → MobileNetV3-Small
│ ├─ <30ms → MobileNetV3-Large or EfficientNet-Lite0
│ └─ <60ms → EfficientNet-Lite2
│
├─ MOBILE (iOS/Android)
│ └─ MobileNetV3 + INT8 quantization (always)
│ ├─ Speed priority → MobileNetV3-Small
│ └─ Accuracy priority → MobileNetV3-Large
│
└─ DETECTION/SEGMENTATION
└─ Use framework default or ResNet-50 + FPN
import timm
# Balanced production
model = timm.create_model('efficientnet_b2', pretrained=True)
# Maximum accuracy
model = timm.create_model('efficientnet_b4', pretrained=True)
import timm
# Edge optimized
model = timm.create_model('efficientnet_lite0', pretrained=True)
import torchvision.models as models
# Mobile balanced
model = models.mobilenet_v3_large(pretrained=True)
# Mobile fastest
model = models.mobilenet_v3_small(pretrained=True)
import torchvision.models as models
# Avoid overfitting on small data
model = models.resnet18(pretrained=True)
# Freeze early layers
for param in model.layer1.parameters():
param.requires_grad = False
After comparison, provide:
## CNN Comparison Results
**Constraint**: [deployment/dataset/accuracy]
**Winner**: [Architecture name]
### Comparison Table
| Metric | [Arch 1] | [Arch 2] | [Arch 3] |
|--------|----------|----------|----------|
| Params | | | |
| FLOPs | | | |
| Accuracy | | | |
| Latency* | | | |
*on target hardware
### Recommendation
**Choose [Winner]** because [constraint-specific reason]
### Alternatives
- [Alternative]: Consider if [condition]
### Next Steps
1. [Implementation step]
2. [Training step]
3. [Deployment step]