ResNet - Classification

ResNet (Residual Network) is a foundational deep learning architecture that introduced skip connections to enable training of very deep neural networks. It remains one of the most widely used architectures for image classification.

Overview

ResNet for classification provides robust and reliable image classification performance. Key features include:

• Residual connections - Skip connections that enable training of very deep networks
• Proven architecture - Battle-tested across countless applications
• Multiple depths - From 18 to 152 layers for different accuracy/speed tradeoffs
• Transfer learning - Excellent feature extraction for custom datasets
• Wide adoption - Extensive community support and resources

License

Apache 2.0

Open Source License

ResNet is licensed under Apache 2.0, making it free for both commercial and non-commercial use without restrictions.

Learn more: Apache 2.0 License

Pre-trained Model IDs

Pre-trained ResNet models are available with ImageNet weights and are open access (no API key required).

Model	Model ID	Layers	Parameters
ResNet-18	resnet18	18	~11M
ResNet-34	resnet34	34	~21M
ResNet-50	resnet50	50	~25M
ResNet-101	resnet101	101	~44M

Custom model ID format: project-url/version (e.g., my-project-abc123/2)

Supported Backends

Backend	Extras Required
onnx	onnx-cpu, onnx-cu12, onnx-cu118, onnx-jp6-cu126
torch	torch-cpu, torch-cu118, torch-cu124, torch-cu126, torch-cu128, torch-jp6-cu126
trt	trt10

Roboflow Platform Compatibility

Feature	Supported
Training	✅ Train custom models on Roboflow
Upload Weights	✅ Upload pre-trained weights (guide)
Serverless API (v2)	✅ Deploy via hosted API
Workflows	✅ Use in Workflows via Classification block
Edge Deployment (Jetson)	✅ Deploy on NVIDIA Jetson devices
Self-Hosting	✅ Deploy with inference-models

Installation

Install with one of the following extras depending on your backend:

• ONNX: onnx-cpu, onnx-cu12
• TensorRT: trt10 (requires CUDA 12.x)
• PyTorch: torch-cpu, torch-cu118, torch-cu124, torch-cu126, torch-cu128, torch-jp6-cu126

Usage Example

import cv2
from inference_models import AutoModel

# Load pre-trained model (ImageNet weights)
model = AutoModel.from_pretrained("resnet50")
image = cv2.imread("path/to/image.jpg")

# Run inference
prediction = model(image)

# Get top prediction
top_class_id = prediction.class_id[0].item()
top_class = model.class_names[top_class_id]
confidence = prediction.confidence[0][top_class_id].item()

print(f"Class: {top_class}")
print(f"Confidence: {confidence:.2f}")

# Or load your custom model
custom_model = AutoModel.from_pretrained(
    "my-project-abc123/2",
    api_key="your_roboflow_api_key"
)