tessl install tessl/pypi-rembg@2.0.0Remove image background using advanced AI models including U-Net, BiRefNet, and SAM with support for multiple input formats and GPU acceleration
Agent Success
Agent success rate when using this tile
84%
Improvement
Agent success rate improvement when using this tile compared to baseline
0.94x
Baseline
Agent success rate without this tile
89%
{
"context": "This evaluation assesses how well the engineer uses rembg's hardware acceleration capabilities, specifically the ability to configure execution providers (CUDA, ROCm, CPU) for GPU-accelerated background removal processing.",
"type": "weighted_checklist",
"checklist": [
{
"name": "Session creation with provider",
"description": "Uses new_session() function with appropriate model name to create a reusable session for processing. This is essential for efficient GPU utilization.",
"max_score": 25
},
{
"name": "Execution provider handling",
"description": "Properly handles different execution provider options (CUDA, ROCm, CPU) by understanding rembg's integration with onnxruntime providers and correctly initializing sessions based on the specified provider argument.",
"max_score": 30
},
{
"name": "Session reuse",
"description": "Passes the created session to the remove() function to leverage hardware acceleration and avoid repeated model loading overhead.",
"max_score": 20
},
{
"name": "Fallback handling",
"description": "Implements proper error handling or try-catch logic to detect when GPU providers are unavailable and falls back to CPU execution without crashing.",
"max_score": 15
},
{
"name": "Provider detection",
"description": "Detects and reports which execution provider (CUDA, ROCm, or CPU) is actually being used, allowing users to verify hardware acceleration is active.",
"max_score": 10
}
]
}