wide_resnet50_2.racm_in1k

Model Details

⚡ Quick Usage (Python)

Using the 🤗 Transformers library. Install with pip install transformers

from transformers import pipeline

# Load the model
pipe = pipeline("image-classification", model="timm/wide_resnet50_2.racm_in1k")

# Run inference
result = pipe("Your input here")
print(result)

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🚀 Use This Model

Access model files, inference API, and full documentation on Hugging Face.

🖼️ Task: Image Classification

This model is designed for the Image Classification task. Explore more models for this use case.

📊 Popularity

🛠️ Requirements

• →Install: pip install timm
• →Python 3.8+ recommended for Transformers.
• →GPU (CUDA) speeds up inference significantly.
• →Use model.half() for fp16 on limited VRAM.

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