Search is not available for this dataset
images
list | problem
string | answer
string |
|---|---|---|
[
{
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [42,20,69,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,32,59,57] |
[
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"path": "image_1.jpg"
},
{
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [36,52,56,72] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,53,85,76] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [21,39,71,56] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [37,41,82,56] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [50,46,78,66] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [34,53,61,72] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [37,50,65,84] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,21,74,75] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [45,32,58,69] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [58,36,69,72] |
[
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"path": "image_1.jpg"
},
{
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [47,47,61,69] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [51,43,78,72] |
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [37,41,58,59] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [51,40,77,53] |
[
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"path": "image_1.jpg"
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [55,34,78,77] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [43,40,61,77] |
[
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",
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,38,57,58] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [50,37,70,52] |
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,29,60,75] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [57,36,76,77] |
[
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g/wDn3H7kH1iv/O/vZ6L/AMNfftZ4x/w1D8Rcen/CbX//AMdpR+2F+1uowv7UnxGA9B43v/8A49XnNFH1LB/8+4/cv8g+sV/5397PRx+2L+10On7U/wARx/3PF/8A/HqR/wBsL9reT/WftS/EZv8Ae8b35/8Aa1ec0UfUsH/z7j9y/wAg+sYj+d/ez//Z",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,59,81,74] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [43,43,70,60] |
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",
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [53,30,80,68] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [50,36,74,73] |
[
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",
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [45,36,71,61] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [24,44,67,88] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [53,51,64,68] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [42,46,54,66] |
[
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"path": "image_1.jpg"
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [29,30,98,66] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [35,46,60,61] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [41,41,57,59] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,49,62,71] |
[
{
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [11,49,100,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [33,52,71,60] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,56,55,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [57,53,73,66] |
[
{
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [48,53,63,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,51,62,69] |
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,36,77,69] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,38,61,65] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [26,29,94,75] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,50,68,69] |
[
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",
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,21,63,81] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [31,21,58,99] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [23,38,72,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [37,39,65,54] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [55,43,75,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [40,48,70,76] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [50,49,72,77] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [30,20,63,85] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,53,75,76] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [51,57,70,69] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [54,51,67,78] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [33,31,67,73] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [33,47,60,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [37,54,55,69] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [55,59,76,74] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [40,55,61,70] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [51,35,73,64] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [54,40,71,58] |
[
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"path": "image_1.jpg"
},
{
"bytes": 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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [48,42,67,56] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [38,39,67,64] |
[
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",
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [31,49,61,80] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [55,37,77,62] |
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [40,49,54,65] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [39,37,57,57] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [49,48,66,73] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [52,46,74,87] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [53,49,63,79] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [56,36,67,70] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [42,51,52,78] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [46,32,59,63] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [47,26,59,99] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [51,40,56,86] |
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QPIs8iIrLNvGYz5qq+ed5Jr4T/wCCd/7Fml2H7SHxD+CXg/4iva6kvhHTdVttE1a4l+yzxCeaGaecwWpRZomltljDsp2XUu0MCxVZdU9hRapfxZXu7b66/mdeZUo4mpF1P4cbW8rbfkUbP/gl74M+PH7VPhT4f/C3TvFPhTwFd6RHceKPFs10l1bwCSytLi0tYYjFEqSj7bDbmQyytKqtM5Z4p2fmf2gf2iPgV8JLDx/8BvBPhyHwtrfh3VtV0Cxgh1S6nGpyw7bSG9kvpFCwvJHbso8yUsFts72FfUHx5+Mvxv8AgR8P/HP7PHgXQ4/7Q03wsbU69f2U6EXsWj2wjurKaVxE8AJU58vmQSBuY2FfktpvxX+EPxI+JniHxx8cvCV3q13remGYX0upzLKuqPGTcTExgjEs8ksgLI/lnZgNg16dLkx9KNNvWL18uyPPxdL6rUlUtvt592fZ37CH7F/wC/bU/ZY13VP2jvD3iWxvfDGv6dp+heJvBPieK7uNU1C9DRzROJC8LIi2ySGMkOxmiCywKzyN51/wXK8OaF4COk/Czw+myDwjpHhjRliLKxiWBNeCKWX5W+QpyvB7V7d+xZ+3P+zn8Bf2d4/h/wDDj4c39g1t4juNcTVtJlFwZ7o2drbNp08E+zdG6xyP9oWUMjSgrH87k/Kf/BVrXdT8WeIfF/izUNQjuRfeI/CjRyQptjP/ABIbhyUXc21SZCwG5sbvvHrTxEozx1KPNfl7f1v+hNCkqeW1JJWv3/TyP0p8D/sT/Am38OpqmpfBfwrPNDqEURWbQYnZwwckr+7KYAUA5YE7xgEbsegeFP2K/gBr2r3zf8KN8FqPsVy8aL4YtY0iMULt8qhTlsKPx5Oec+7eEPh/Hf8AgRLhbYzzS6kB5kZIJCr90c7VHzD5u2O3fu/BXwztbnUhHY2iNNfXQhiCHCsZGwAAOFU/Tp+VebTm44WnJpWsm/M9StOM6k9dVdfkfH95+zUNI1bT7v4beB9DSzmuoINd0uaOCwtLi1UTSMjTQ2csjiSU2/mRufLKwArhgwk9f8d/slfBnwb4Hi8ew/CXRbc2eoRpF9j0KFZj94qyvhcODggnvg1674lMFlox8NWnhu2eR3Bafk+VIjyfMrLjkq20hsg9eoUrU/ac8D6t49+EVl8KPB3j7UNK+zXVjqEuq6WsbmSWFgdhEisGRhlWQggozLxk55KGIpPCYiNTaKWvW7Lm5qvRlDq/w7ngfgD4KeBZPhbZa3468Lf2xYaVaXF1pVncaY2pXIT5jKYIUiaRg/lgRwRKzMqIB5jMKr/Bb4Q/Cf4veFLHxtbWWg6nYeILj7RZWmn20BtraPASAoE3J80QRlYfKI3QISqhm930jwzqNvNofhV7uCaC1sEt2KDyxjczYQDO0Atgc5wOSTkmrdaJD4c1tPCOoaXazwaPdy2Ett9mCpKiSsGDoRg7skEEc85ySc/E160IRd76vc92NWtN769jzP4SfCb4Z+NvEWu6Z4b8P2DaDpLqmsRC0hls7263JiHywuAyB42MrnHKqoJYFOi8ffspfCey0+6XTvhvpVq0Uav51vpEKEcjGWC8A5C/j71ofDH4e6h8PvH/AImv7Pxzqr6X4h1OS4Tw7cwWaWmmFpcoIvKgSQbYwkeWchlQMQXyx9d/bU8S6z4L06y+JHhTwoNQk1uaFLnT3vzbNiVmjUqyLhz5/lDBaMFGMgZSoDfeZRRwtbIKjg/eV23/AF5I+ex2IxFPNKfNs9F+H+Z8h+JU18+Kr3wx4lM97osqQRiW5ij2fvOkOQg5BiDAkljk+lcf4B+H3wV/Zx+Ov/C9LbRLLS57PTJ7axh0nTEMmoPcp5UdpIEeNlRnBcH94N9ug2/xJ6Z8APtPx98B3HxK1vULCfUJNalsdN0ayvQzWFsscMiSBDggymV8uQQ4QBTkEHnfjn+z54k8WWel6ppd/f6VJo2tR3k50+KDfcxqDugkEsMg8tjtLFSjZRSGGK+RVevhMVGbb93X9V8j6Gp9XxVJwikrqzPGf2qviHbfHLxXBo+ieE57GWHwXaaXqmvaze28mq6mYg6uzi2ihhjZmmlDNFHHuUDAVAsafKWt/wDBNr4ZawglsbcWvl27JC9qip5ZZmYl9m3eQWIBbdgBVztAA/QlvCFiIoFlticrlRcIQWyPXAPfuB7gdKiPwiadHkcsnmnOzy84HA2gjGOnfP8AKuarmWY1cQ69Oq437OyNo4bCU6CpVIKSW19T4Y+Bv7Evh34OaJqMj+N/OvtV1ywuo31DR4rmGws7IlpGIEyG4M5uDF5S7MbQQ5JyvhX/AAVobxERqt/c+DVstO1TxrYz299Jfs08nlabJAiSRMoCttUtlDtA2jBJLH9EfGX7N+tQfEi38WWnxF1u2tg6rb6SwgfTwf3Y4URhi+9SdzlnBdwpVGK18kf8FuvDf2D9njStY8p1LfEG0ibdEQP+PC9xhuh+6eOvFfR5bmdeeNoQl7zlZSZ4+Y4an9QqtaJbI/bbQb26g0JLG1sIooiRjylAUdMk8k8+gz6ZrX0TxFDDpFxY2kq5S5jbKqCzldwOXz8vP8IHPqMCsS8guxEVKeVEoCxwocbB+Hc/pS6bFb2MMkAlgRl5ZFIJX6j16/5zXHWx1b2MYLpoNYam5OT6lvULqKxgSKVZLia/uhhHcLnGSW6YAC7jwO+O9WGuLFtZVpgWjSEsU2cbfu5z2P4Gse0kutY1cXjQyEE+VaRCPlFxlnOemSB74ArpjoNtbWjRSxlZLgIzlgcbOcHn6H8fXHHkyxVespKOx0ezhTsmYkupxajqAhtEMbIoLPIcEcn0z71i+I2Gh6rfXmub7sSkTSTNjcZA5BY7zjOWJJJz9a6SXwysF69ruR3kbIlVjjPJ9uM1X8S6ZGjpZapACPIKOj5+9nofTkGvIrxrxg+Y66UqbmrbGYNRtL/UI2ktdjwwmOXbj5sElS3HXBGPqK6f4qeLvD/iz4O6f4S1Ao1xFdENMyLlMBcKWPIU4Y4GBlsnoDWFo2hXI1ie5ht5rkXNorqUUkK33ShIByTjPPpS6sCzKHkJiljKSJtxsYcg9MdP616+V5tXwuBrUk/jTRz4nCUK+Ipyf2Xc4b4feFPBPwwuItMsPDtnY2MjtloEWH94yqkceQBxwu0dyNo5IFdxLp9nJp8Masw85WEbAglug7EnvxnHeq9naWsdo0F/5bMWwGYn5lzkVJeack1v5NtOYnX5k46/7P8An0rtwEZzp2lqZ4hR57o8t+IPwqKanLrKs9xZyso2RBgUkxjG0ZI5BOQPb6QeGdNttB1G3h16JzZo+JLmNf8ASIweoKnh8dgTjmvW/CXh641CWa3jtHmaNMTW7jLKfTB/z6Vy3irw1ZtPIluWBxw4OCvqOc5H4GuPE4KNJ+0jpqdFPEymvZyOA11dKn1D7W9lJNbvITDe2URVtvzACRAScj/gSjOeK/O//g4C0S0tv2UPD2r20kEwl+JVmFmikydrafqDDI7Zxn8BX6Pa74buLe2jkvWkttzYiuYZCo34PQg4zjnafT2r87v+DhEeJLX9kXw5Y6wIrq3/AOFm2b2uoKgDgf2dqA8tioAPqO/B9eOnJ+dZvRu/tGGYqLyyrbseOf8AEUJ+30VRD8H/AIPFYzkKfD+qnLf3jnUutVT/AMHOH7eZBA+EnwiG5y740HVPmb1P/Eyr86KK/TZZZgJRs6asfDLMManpNn6P6b/wdA/t96ZM08Hwj+D7MUKgyeH9U+Ue2NSH6+lbWt/8HXH/AAUF13TbDS7j4E/BCKPTs+S1t4b1dGZtxbc//E1+Y8457DnJ5r8xqKyjk2Vwi4qkrMHmGNlK7qO5+k//ABFJ/wDBQP7Yt4fhF8HiU6J/wj+q7en/AGE6b4j/AODo/wD4KA+J9Rn1O/8AhB8HUea5km2Q+HdUCx7nZgig6kcKucAegGcnmvzaoqXkeUtNOitRrMccndVGfpV4Y/4OmP8AgoF4W01tMt/g98HblWR18y70LVy43buQV1ReRu4+gqrN/wAHQX7e86SRyfBz4OkOP+hf1X5T6j/iZ8GvzfopLIsnSsqMbD/tLH3v7Rn6LN/wc3ft7uArfCv4SlQuCp0HU8H0P/IRq1N/wdB/t8zKob4PfB4Ff4hoGq5P/lSr836K3p5Xl9FWhTSFLMMbPebP0w8P/wDB1P8A8FCfDV5cX+mfB34NiW5tzDI7aBq/Kn1xqg3cZHzZGD06Yw7/AP4Oaf28dRupLy4+Evwj3yuWbboOqYyev/MRr866KU8qy6pHllTTQo4/GRlzKbufoWf+DlX9uh4ntpvhN8JJIZB88Evh/UmQ/gdR/GvD/wBtX/gqz+0H+3X8NrH4WfFbwN4J0nTbDX4tXhfwxp15DKZ0hnhAJnupl2FbhyQADkLyOQfmSilTyjLaNRVIUkpLZ9gnmGNqQcJTbT3R/9k=",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [47,54,58,76] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [60,44,72,80] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [45,33,59,62] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [38,48,66,85] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [51,50,68,68] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [39,44,74,55] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [51,29,78,71] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,42,72,70] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [40,44,65,62] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [29,31,60,62] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [42,23,90,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [52,52,70,68] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [53,33,66,62] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [53,41,74,79] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [45,36,62,76] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,38,57,63] |
[
{
"bytes": 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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [40,36,49,63] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [60,32,70,61] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [35,24,79,80] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [25,35,65,70] |
[
{
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [54,47,70,64] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,41,60,60] |
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,47,60,61] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [38,58,53,71] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [27,34,64,85] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [53,42,74,70] |
[
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",
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [51,38,65,53] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [45,41,53,70] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [19,44,90,82] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [43,58,63,69] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [33,53,64,74] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [35,39,71,62] |
[
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"path": "image_1.jpg"
},
{
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [50,55,70,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [42,43,63,57] |
[
{
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"path": "image_1.jpg"
},
{
"bytes": "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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [35,45,71,55] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [45,46,83,56] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [58,49,76,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [36,53,60,77] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,33,61,63] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [55,40,69,74] |
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ExozLu4OMMM5U+xxXJTzzGxoyptrXrbX7zsnkmF9opQuvR6fqftx8Lf8Ag53+GsVzaaXcX/i3RIYZ0EMl3pkdxDGAR98pIW2DjjaeM8Z4r7S/Zd/4LCeLvj54Em+J/hbUbHXdHkvri1svtNmsG9oyFL7VVXCE8gMAxUgnGRj+ZP4e/DDx34qs9S8V+F/Dz31n4ftRearKI1dbeDeiF3Q53IDIgYkFQGy2BX65f8G3s/wh8dfDD4ofAT4j3LWPirT5oNa8JeczxieORTDOpKqcKsgty24chwQQFYjbC5lQq14xxNGLUtLqKTOXH5VOlhZVKE2nHW17o/SyT/goT8UppHe58C+E5dwIIfTZDn6/vOa5Dxr+2lfXUV1qPjj4feGBpckEpvLXR7OeGdjtjwrFnZGhZoow8fIdDKpH7zcsc37P1ujOl94y0nTWJO1ZrqWTP08uJxj/AIF2/GvN/F3wd1+61K50PTxBqsYO0m1id0lHU4BXOMeor7CnluTVNIJL5tHx6x2Pozu3f5Gb+018SP2gP2p/hAdGtfGWtaP4GkuXtEuLZ4Lq9gUNu8iS7eES4bKjd8ofbjqKxP2efDPi79m/WLD4kj42DxNdJqAl0nw/Npi4sraHy1XzLqN98xkxJxncpUncd4C+s6F8KNK8R/AHQL/4f/DHWtA8XNcNFrGj634cls7i6UtJ+8HmRj5R5UZBZjvEmQQeDynif9nb4t+GfL1LxRdaTpUjxiRlvNetoJoxnHMbOGJwAflB4PHPFZ4SGX4vD2Vku2zfTXvc6cRiMTRmua7XfsfUGif8FDfh7/wjT6hHFcaJq9/iW/SHTjeRiTHJUtPGcAduuc8isvwnc/Fv9onSdasPgn+0XI1jcSvLe6ZceHo4pUuJRyY/PuJGRRnJ8tsdwpbr8u6No1heX81xq/xGhuJI22TXF1HI4kUjjy2bDPtXAyVUDGMk1reDfiJdfAvxgniH4V+IzfEowf8AtCzaOM5H3SNx3jkHnaTgjApSyOhyN4dWlvqrr8dRRzGomvaP3fncv/tVf8Eo/jD480G/h+N2paLrWipA/wBs1a68SvblIdzE7HfY4OCDtAHzNgA9/hmx/wCCM3jLStet/E/gi9mh8KXMN21pqeuajFaB7mBXIi8yVW80yjKqQpO7luAWP3tqX7SPxK+KXiiSbxf4f03xDe32IdHsTaNHHp8hbO6BI3Vdx4BLqxwOTya5L45+Gvj1A/8AZ3xdhu/K2pdR2jahEIFBGBg7vLDAevIx6VzV8nnjaCo4zkv3Ss16HVhM1eBqueFlJLzbaPFf2N/2Q/2kfBHilpfhN4C8IKdGuHGp6608l9f3RaO4TahZ+VVZGI+z26ZMURkB27q+U/8AgoJ4N+LHiL9ub43eKY7+VvEXgbwHpV1r3iLUdHKahrUlxY2Vs9vGlqHhWIwT/ZyGIaVI3LdZFj/STSvh34M8MeHrK68V61eWUd2m63mTxPZ3MJwASAkO18gHorZyByOtdP4Z/Z103XdJ1G+8B/DbxBqum3ixNca6mhxw2106gZDi/dWYLuIykjkj06D5+tlVHBR9lT+Home7SzatiZe1qO8j8zP2lf8AgjF4j8KfDxviX8DP2p/CV94f0zwuNV8R2F+G0+e1WO386VkNvEUkyFLIjFCAyxguwYn5Z/Zw/wCCZX7V37Tnhm1+I/w/8E2ll4bvFdrPXNc1SK2jlZCylRGSZhkrhW2bW55wAT+0H7ZX7Fvir42fBw/AG4ufDvh2HWdVt0WfTvEsLahNZxyK8kcFvJIzxM5RQcOeHPByNvr+hWf7PHws0TQ/h34eu7HStJ0zSYLCw06fRJgbeOKNY1jUyeYzbdu0ksckHDEc1588HG6fTyO6GZyjDWx+ZX/BJv4NWXwy8MeMLnxRZxReKdJ8Q33h28gFyziCBo7SScNEWMZEjpFu3Ic+QvzEAAfSPwa/Z5+DfwL+KT/Gr4ReELfRvEbW7wfa7GeZYRGzxuyrAH8lATEn3EXjIGAxB9m/Zo/Yx+Cfw5+LHxf+M7fGCG60r4m6vb6la6HDbC0udDvIY38+RGE7CfzTKpHyJjy1XDbdx0/HPwk+HcOmfbdC8Zale3ZnZneSzWNjGR8o2B3LHOSW3Z6cHrX0+TSw/wBXjRnT26uJ83mtWrVrurCe/S+ho237Xd5azwjxB8GfD2oRRkCc+dcQvKB1IZZPlJ69COelen+D/wBu74S2/h6Wx8N/BVtJ1l+LNb7XPNtY3OQW8xlyB7FefbrXy9qHhe7gspbvTrn7THBL++jKMJUHPzFcfd4xu6Z4rJWW7aT93iMg8YBDV77yrBYhJ2f3tL7tjxljK9B6WOxv/wBrL4patOskfjVbFI12w29jFFCkShiwVQOQAST17+5rzf4n/ErUNZkbULvxPcvqE0wkMwUz3FywwxX5shg23DEkAZHzLXORXNuZTvf6cit2zhikhKxyIjEgZzjPPFepKhQpRfKkjijWrVHeTuc98NP2sPgtqHxIHwe8RRT6hrvmQwwRwXXkq7sm5nkWNHKqp+Rvu7W4yOtey6h8QL210t/Dun+FNI0392Y5nhs1e49CDM+W69xjpXzT4K/Yy1a98Zan8SPhh4d8Jatquq6kzy/bNOYrbRqVQlP3+Hc+TkqoiBaVnLfNivsP4e/so6nFpgf4meKbe2eABgukWzeXNHknZH5jMVwu1QTkdDjnFeHhsxlGc44tppP3bfqelicBzRi6HbW55la6vqum3S6npt9Lb3CnKzwuVfP1606/tfG3je5k1e7TUdTuGXbJcSCSZiAMgFjk8enavonQ/CfwM8A30mq6fZWt1aMm1P8AhII45cPgZA3YUjvyO/JryL4l/tH/AAqs/EWt/D34UX8AS9gij1L7BZxhWZZJAfKLgEZHG4rxlgM4IPZLNFOX7qlr3ZjDBOEbTn9xwmjeHvGOo3cM3hjSLy72Sss7afZGZ49qljnCnaQFJ7dPTNe329lD4P8AAGn+IvidoU19qRvVDN4k15r61eCQAfLGoUK+QxIJOBx2NeY+Gfi9a+CbZLiy0uWW+S3lt7WVH8pYw8aqzsVPGQJBgDo5GTuNGr3XxL+LMUcNiX8kD7lxKzIgVgQu/aAMlT2B5IzXLVpVMRO04pI6adWGHj7jbPT/ABJ+0J4Zm8Mz6D4C+G+k6NDqEKpqT2FuLfzyrEg4jKtwSQMnkE54OD5zDrd9fyk2l+yIWyICQY156BTkAVNZ+DdVupo7GwspLmdAN8NshbrjJIx05zzXfeCf2W/GuswC9W0is8gFjc3aKcHvtz/IV106OX4KPRepwVK2OxE7xuzgZLXUp2MkN0kR6lYhwR6Y7VVf+17S6XTtajhcXSFrUSTbGZM7cgng4wevGetfQnhn9mGLw/HJrWvataXL2ymTyYpzjCjJByMDp16Vyv7QXwz0u48L+FvHA06WLWrFr2GLRo3kBu7XzQSQq/eKOHBOQdsg/ugDOOMwftlGmk7mnsMS4OVRnF2Pwy+Imo2UT3dpeJpcq+ct3LBJcogP8W6LOFAOM4rSsvgtp09k0Gl+KdMv7gfPLa+dFay8noHuFBA9wPzp3w68Ia1qGn2vjzwPrt7p0Fzlb2DTZH3W5BOVZsPgsQXUkAbT1yDXtPhW107XLS1m8a6T/bca/JKty0DTRKOjjMaMxPfl/oK5sXiqlJ3jL7kdWHoRmrSPk+L9n/wd4b1DyPiX8QLGxc4LRWVwty4XjkCNiOhz1rif2rfif+zF8A/A1lq+mfE29vJ7y5khaOOxEj71iaRU8tQjAsFbB+7xklepyJJr28OVOSfpXCt4e/Zz+K/xi8P/AAk+PN0Utbu+juFi+y5uGeLZIywpKu2UeTvZ2AZEVWMg2hsb5lUnQoSl7TXXp5MwwVONWrGPJpp19Duf2M/+Cpnin44+JV/4Zpj0bSbnwxpVnpevaT4gjS2lvtIaa4MNzB8zF7mB5HLLgIy3cI2h1kdvs+0/a8+Hfjfw2PD/AIzuo01KG6MMur6Jcrb3dvJk7mkCfu5CCfmR1zzwVODXl+v/APBE/wDY1+GXh+y+NHguPVdRh8R6khuobya0lFwgErJLFIlnCsQAXmMwjmThuDXe+E/2EPh9fRWdp8PdC1HSLODAWa7vY3jdCGUKqrGpGTg9hweOc18hglhpp1Jy69mfRYp1XaKXQ8l+L+m3+n+JpLS68ZzaxaOS9pf7ztniJzuCE/Jz/D2NchY+FvCDTiX+ymebdlXjULg9u2T1P519g3X7FfhXwT4cm8SeKUju7OygaWZ5ZZW+VFBYAKR27Z7nFeHaV4U+FHjdNWh8NHWDf293Etpp4ijbcrtICynenyjaBhuTu+uPsMNmOFnS0baWl7HzdXCV41Neovgv9nPxBrCre2+n6WoaON7eSbUBNHJvUNgmFmKkKwJB5HcCu28G6l8Z9I+zeE49G0aztWtZLizQGHDRr8zYYsfmywypIb5hxzXTfBv4Aa54diu1tdLukttQRRPNc26RTqyBtvlMFcpje24biGOw5+QA9Np3hM6fo+peDPCHj26kj0KVJrjS9UuXklG8AFoi4w6L8u5QdqlgepArzMRmMpTkpNOK8v66nbRwyVNNaM5q2sr7Wx/Z91YXtuqffHmlV5yR047frXeaf4TaLSDb+ZLIYUYqgn2Z68ZGap2XhGI+Fbm507x0IdXRl2QXFuZFKfNuOM8EDBA796d4v8eaXFo+h2+im7dI5WTWbeXyNzREpghmhPQbuByfUcVwVa7rTSgb0oKmryN/SPD4/sKO7sLe4hins5GuZ3uDMFk+YKgQoN4baMnPB3DacDPh/wC1brWq6d4m8D3mm3M2nS2vho3MKW5kj8qU3U6mRdxyC21Xz9PSvaPD/i3+3NLGneEdQ1TTV0+RTbSR2yynZgqp2gbCORlWB57HgiD4n6F4O8deLvAXir4mfCo3nktNpmqarZq3lLagJhJYUww+Z5PlHypuOCR0xoYiVDFxlJXSv+R01Ka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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [35,47,69,63] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [35,38,54,52] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [56,38,66,63] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [50,35,59,65] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [40,44,65,65] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [44,56,70,77] |
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"path": "image_1.jpg"
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [56,37,70,72] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [46,44,60,72] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [35,27,94,79] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [36,51,59,72] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [54,55,70,69] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [47,51,69,70] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [46,32,86,63] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [36,39,71,59] |
[
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"path": "image_1.jpg"
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [54,44,76,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,57,64,75] |
[
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BHCBVIO3oDwPTkw9VSWh6NlE7OGeK4hS4hkDJIoZGB4IPINI0yrnJHHvXOfDTXZvEHwy8Na+dwN9oNpcMCoBG+FG5HbrW2GEgO8/nWjbUxuoloPe+QjAAz/vVRvbwu5VVx05BqSdY4xuXGSccGqjgNIc+lS0+p5uIr1GrJn8r3jrxd/wlWpXPiTxPPd3lxNEftk0shZ5emOS3GAMYFU/AvjnwvonhNPEdto1zp2mx3tmyJdl/30sU6yF4lckcRCRO2RIfQVJbeALrwN8T/Bei6n4mimfWLqKWaSe2WNrErMgJkEbEbQGGeh/I47T9vn4b39x4J0LWfDfjPw9qr2OrP9rstFEMbmF4XG7bAcSYcLww4Bz6YwtGc/I9/DTThyPc+uv2gvi38D/2o/DDfGH4L63BqmmR6Ve2bTLuV7aXybVngYHoyhUz9RgnJrA8IaLp134fsJJ7RHM/gOWKZnbBb5ZeMk8DaQOor5s/ZK+GnxF+Evwvv/GfjEyWuneM7a6i0zTGkUsv2W2klad1BJXerYCsAcIGxhhXrOhfFXS20ez8O3GnPLJL4I1JLxVdwFRH1CAEYZcg/Z+eSeuBzxz14+xg2ctSn71kdhpf7LXwFub577XvBAuBqWgvqV0Tqk4VrgPISxAlCADa2MfLzXI6R+zn8ENd0JDpfhPSroTeD9QurvbcO8clxB9t2ScEjg27KMd0OR1rS8J+ONLuLfQ/CmleB7cPf+CNXMMryTDyRHNqEalMyYwQGBBDZB96j8HePrTSV0aGP4b6Tp73fgHWLWQRGconlSaqzqFEqjDZZuR1lYg9McsMzxkWrTdjH6nRim1FX7nJaz8Mfhfq3i/R4dP8DWUNrffD/WLuC3ZSQk8A1b5ycnHzwRsMcfL7mvRLTxZoR8ReAvD1rYTC/vvh7NILl0XbGkq6sB3yTuDEjGAMY9KwfCXi9PF3jPwz4X0HwLZz6tf+FdRhjsLCwmeTykGoLsRfMc7TvfdknlzyBwOs0+30nStX0LXorbRDe2tlPYm3/sY+ZCX87YF87LRcz7SG784AzW9XFVq9P35X9SYRhBnefCP9qnw3+zz4V0qY+FJNWfxb4V1Hw0htZxB5LzXPEhYc4UsDgA7gSDX6YftL/Fmz+HD/AAr+IXizWYdK0uabUIr2VlJiXzNDvJVQ8Egb4l56j6Zr8kfgL4Ij/aO8W+D/AITah8RfBvgGR4JrmO/1fQIjbzTL5UptYypRY5mCzkSEjLKOuQtfo5+2NoFl8UvDfwS8JeOPEmow6B4wnUajNpbj7Tp7xeHNRk2gsG+SdnWN8qSR0I61vgeZHRK6Ssexfsc/tHN8Z/COg6dHpBtAPCdveiEurOq+Y0S8g4aMoI3U9cHnOa9tmt5WYuGFfmL8Cv2i3/Z+8TaZ4s8GeItMi8N2Pgm4061uNcs7mbbZw6vcKhZbf5xJ5VsJCoUnbuGASK+xv2Of2ttC/bG8N+JrnRvGNjfyaJcW8bzaL4d1DThAJYy6jdfANIx2n7qrjHPUGuqrKzt1OOqozqNM9uaN8ckU23uLJ74aabyEXLRu6QNIA7KpTcwXqQPMTJ6DePWuc1TRptG0681a41m7fybZmRrjUDGC/wDCGdcHGeK/MHxf/wAFJf2gm+J58QP4h0yzudCe/s7GFLW5uYhFNPHvQE3AaTmzTDAgH5jt5G1Kaj8RxwhGUz4Z/bw+AOmfAX9oTwDpFt4jOow6wZ3ubz7HHbxxBZbYsFAduijJJ44NZf7cWi+D/hH8PtH07wdqSTat4omuhp6WcRJht44CJZnZsBQDIi553GRQBwxHSf8ABQn4iav8c/FXgXW9f+Gx8LIlrqtrGIbxJ/PObPcSdvy4WTGO4Y1y37W+kaDrPw/8M+KNP1e0fU9EiutOismJzcW80yh8kA7Ar26NjBzn8TyUbyVz2IfxLG58O/i38L/iZ+z1onwl8E/Dq98O+JPDUWp6n4ghN0Z4NSiutHkgF+kjYZS00QRkICqcYJyCeo+GHhG88ReINOu7jw5dzWd14P1RILhLRypDXl/s5AwNwlJ59vfPBeFPgb4m+FX7Omm/tBnx1HrOpeIdO0azsLCwZjHZ6VdZdlKMoYuS2XJAGIwOcbq1P2f/ABVrWt/Ei70Dx4hsvC2maTPJp2oXLtEinzIlVUkyAAwkdu4Oz3rCu243Rs4c80e3+DvDWlaF4m8D65N4E1q9toLG9t7mO1tWVTa/ablmLFk6lJGIIOPauk+D3wt/Zy8X/Cy08QeOPig+heJtI0G6XTvDSWoea7SaCXaMM6uDmdhlQ3QHB7+CaJ4g8JwfE5LH/hM7V7Ox1WSHTmjnBxCcMWVgTjLErx1OT3ry/wCJU37Vd/quq2ngbwprVzZIB/Zb2mlGaMEKAArYIIU5XjHTHFclKnKXxETelj23w/4A+Nvh34reEvGHgnTX0+W1j1VWu47mOKXy2YFFIXLkEnpjsCffs/gx4d06Pw54QufHlvbwa2VWbV5nvogGdWTHAY8fe468V8o6/wCG/wBr+4u5RBDdafBDoVl9jt5prOLZdNbW5nG2aRSD5nmkqepyfevUvAfwe+IeoeGtDv8AxhN4hku5dDs31RdGvoPIS68iMTBXQhSxkVm3K3fvjNbyjO1rGEkm1zHSfHdZ/Avhb4feIfh9rmkxano3ic36s+pWskR+zCJ1WTexRvvtlDyRg4xnP098b/2XPjavj3wD/wAFANJ+Jem3HgTxJN4CfTNI+3TRXDLJZWSmOQOAqRSbWRjklkmwRndXxZ4/+FGr23wT0TWvFQt7PxBMbuDURcahFv2tLOImLltpZY2iJ5zg9R1r9B/2iP20NB+N/wCxF4Y1r4XaBo0eieE7fwrNqmk6Rfgix1KC+WGfS2MG/CrEsLphC6xyRsqfMAejDT9mxylFJWOf/Z3/AOEA+Nv7R15+x/418Fy2um6n/a0Gpf2VqatIwe6nu4hbXAUeUU+1yRbxjIGRgHJ/RGz8K/s2fsO6f4s+KbNdaLa+MNdgvdfmlmnuhJeiBIEEcShipKRZKouCQ7HjOPyGtv2jR+z9+2/d/GXw3odvNBomtXGqT2trdGPfaNbxHyEYqSUBUgA9d3I71+oPxq8UeGP2lvgB4ZtdV01bLTvHfkXFlqN1Mpm08faIdzoWHyt8wi64w7ZDDIru51NXMk41JXR0+r/tufs5+KtRb4d+GfHz6jqN3py3dv8A2ZpksiOpjSdU3soXzBG4do+W2pIMBgQPyZ+Kmk6bao2uajaaxGmn6xYkKWW3muEntbqQPgjhSrAqDzgrmvsf486n4D/Zc8feH9b8EaboWr66uhL4dMMlpDHYmOKXyjdyCPB87C28ZII+TfngCvkH9rW78D+AbnxFolp4svdftrDWtM+1XFtqKSzXQkS4jVklckKEJC4wQqlQOlZTk6mhm4yi7HyV/wAFBdQ1fSv2z/EOmtqN0bNNVuDpdkszJbWUcsNm48iPO2NTtAO3AbZntXE+OrGwjtLWW3aWWU6lAZHafduUuFKt6g7u/pXov/BUSyh0z9sG4vVjKLNHA7MScY+zW/r6NuH1BHavP/iPNY6d4Ps9UtocOdWs4nmD5BzOrbueMYU/kaVKzjYalK9xnwv8SadZfCvSfA2zKappenPerDd7DAYmSZJGUDsDsOccFeRgCsmCxmudRnSK3a4jhkIaVI8qVDfe4zgcA/lWd8EdN8N6hd2s0P2ddelsLm1kaSdVJijscAgE527kBA6Z6cnnnvFnwh8ajQbnXvE99omiaRaWDXttd6vqsXm3MW4qoihQyTlyV6BBjgsRxUypRmrI7qdb2Tuz3nwvpyXXgjSfDtho4j1NPEkmpfbxJEhurVooEFqwI3EI0cjLkgZmJX7xNfQvg74+/CTRNBtNL1L4W2n2+LdHM8up2kTu4cg/KzE5z/s5z61+b3gTxTpPhLUNM8U6V4faa906eO5j1KGOU+WylWDsnCkArnDA5wQe9exw/C3wL8RIdH/bA8deNtJtfBHiD4iXOnTeDri9mTWnit3hubvKiPbHGLebKyl8Hp97ArCWDnPTYPbp62PrvV/jr+zvBcD4j6x4E0q4t7KRdMudFuJ7SW38198yTEBDmTCSLuIzgFc/Lis9P+ChX7N9nAILT4T6WkY6RWskYQf98Rivzk0v9omO507UdL8NeANC8MWGp6r9vksPsKXAGxplgQvcl2YpHMy7jy3JOOg1vgZ8R9AX4veF7f4zyRv4KuNbhXxNNaaXD59tbqwcyRbIwXbKjKc5XcBgkEOWAaj8T0MeduTdj9AfGX7U/hDx54Ss/HNt8MNOi0L7a8El0BJKY9pbdIUVenCj6tjPFeb/AAd8Z/254Y+JGn6MJoYtRW61L7HbTMFgmiubR12rjkDaMHAIAr3T/gqT8aZPBHwf8M/Dv9l29tfEWpa7LdD4gnS4zL9kji+y/ZltXtxFhHYORMgZZVVyHZSCfiH4RfGv9ofwN8S7bXvEPwruNV0m4meDXbDVLJ/KuIJSu8SyXLlwoKoxKMCAnynJ5dKjGn1uOzm7WPriy8Q6FL8Sz48+KWgR3q6xps9xc206+WLtDG6LFnHyAvGQHAz8ytycV7P4p/aw8XftCS+FPhnrfgzTLTUPBun366DqloxhVbKO3E8gmUAlkiht5X+QEncTjgVxn7Suj618Q/2aPD3iP4RWngbTfG2lzJ/wkWqeELaP7Hd2MscgiDw3UDvashERyHZmJk5BwT82fs/ePP2ovDXi/wAXahD4FufFIn8E6npaaheounw2r39tcWD3cbSpG1wI4JJ2Aj3D5lJxhc78yiHsKlGV2jpPiJ+2jNoFz/YPw88Fx66IDb3sOr6reSoiGaMSvEka7XHUA7jnjoMAV4/49/bp+Ourxy6Tqvwx8ET207o8kOp2V1Nv2klRlrgEhScgEkA84zVaysdatPB0msaPo8Z1m6mkNzBqVwsKLFhfLZA5H3gScc4GMcV5f4g8PfFHXbzbCnh1ZkAPlyasPlXHoBnrinGCb3Jm+V7H/9k=",
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [33,54,55,65] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [26,45,107,82] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [47,38,82,89] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,58,64,75] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [30,59,82,71] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [23,46,67,57] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [22,49,67,58] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [29,55,67,62] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [42,54,57,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [47,32,70,69] |
[
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"path": "image_1.jpg"
},
{
"bytes": "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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [36,39,92,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [38,39,52,53] |
[
{
"bytes": 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",
"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [18,27,89,68] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [30,37,79,66] |
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [52,38,75,59] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [29,35,73,88] |
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"path": "image_1.jpg"
},
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5+rRuUKK0TKQRkc9qfFMuNzPyfesPwxqS6l4a03UI5FcXGnwShlOQd0at6+9X1mU8ZI+tetzpo+Ln7smf/9k=",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,59,53,73] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [56,42,75,63] |
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,22,77,67] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [51,57,74,72] |
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [29,36,60,64] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [53,36,79,58] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [34,9,56,83] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [42,33,55,79] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [40,54,60,71] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,55,71,68] |
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [32,41,82,74] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [35,44,56,60] |
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [47,41,65,55] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [50,60,72,73] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [56,22,76,68] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [36,43,54,66] |
[
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,49,63,63] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [43,35,65,56] |
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"path": "image_1.jpg"
},
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [48,52,74,77] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [38,32,72,62] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [32,32,61,81] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [43,43,56,64] |
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [48,37,65,52] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [47,36,66,59] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [33,40,69,54] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [14,39,85,68] |
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",
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [45,54,65,78] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [48,30,76,64] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,46,58,82] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [52,47,73,84] |
[
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",
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [40,47,51,77] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [51,49,80,77] |
[
{
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"path": "image_1.jpg"
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [41,48,61,69] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [39,43,74,70] |
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245B+Iumf/H6b/wAN/fsNEbR+2h8KB9fiLpn/AMfpDR6PcS30Cgyjiq76ipGGyuByQf8A69ecTft8/sMOCr/tl/CnI6H/AIWJph/9r1Quf25P2F5UP/GZPwpyf+qiab/8foDqennUXjm4OVIz8vJ/+vUv9pmEGN3HDEHkH8vWvHrv9t39iBWZ4P2zPhYePl/4uFpmR/5HqjL+3L+xgpIP7YfwtYDoP+Fg6af/AGtS6Be5/OHRRRTICiiigAooooAKKKKACiiigAooooAKKKKAP//Z",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [44,53,73,71] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [20,23,79,70] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [57,33,71,73] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [47,27,67,87] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [23,32,70,77] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [40,37,59,58] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [45,57,85,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [23,42,104,66] |
[
{
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"path": "image_1.jpg"
},
{
"bytes": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUDAwUKBwYHCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgr/wAARCABwAHADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD+f+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAACTgCrR0LW1UO2jXQDY2k27c56dqf4aiE3iPT4Su4PexKVz1y4r3fxd4bh0KwRGtJLiJPvpGzEAc5YdM/jSd0iZSSdjwI6XqY4OnT/8Aflv8KQ6dqCjcbCYD1MRr0oSaRNIYobuKI4yDICu0fl/Kob3TraKDEcwkBPBB61g67T0Q7nmzI6HDoQfcUlaXiWLybpV8sr161m1tCXNFMYUUUVQBRRRQAUUUUAFFFFAGv4AJHjvRCDgjV7bBP/XVa+xvEmjy61p8g0+RiJDvlxIOWxhWwP8AJr4x8K6jbaR4n03Vr3d5NrfwzS7Bk7VcMce+BX01B+078ErQD7Pr1wSY8ENp8vB/75rWDXI0zgxsZuUXFX3/AEPPfHPwzurBJ7241FZZCdzN5RGcnrxnFcdp2g+IYYzPa3apHICME5/QjFep+KP2j/hPqcEljZT33lt1/wBD3Bxj3x6ntXD6p8VfBEqGKyN1t2n/AJY7cmuOVP3rm1KpVa96JxPjRJEuIPNOTtILAcE8ZxWJWz4w12z1y5jlsyxVN2d64POP8Kxq2irI6QooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/9k=",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,52,59,66] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [44,59,65,72] |
[
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [38,32,57,71] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [41,47,58,81] |
[
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"path": "image_1.jpg"
},
{
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"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [47,56,72,77] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [50,41,70,57] |
[
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",
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moiYBbWO/spLudwlvOQolEcO1y8kmVIBGFG44LYIYviT4paBJZXngya8WwsoJLS2JhEUwgWFUMYxgSIIhHuIyoVl3YDKD86XH7SXxuu9JttEufH1w9vZuGtw1vDvQggj59m44IGMnjAAxUd5+0J8XL9GW98TxSF4wjyPpVqXYAkjLeVuzyec56egq/YSlHllYHiI3ufVWp+P9A8b2VuvxM+Gl9b3Dnz7G/wBLaOMxBZC7GIsj4ACg52MPkOePmXnbfSPDHiAailt4i16a4WaSZ7K6Tzwts/llfNeFiTwyyuwVRmNgV3BBJ813Hxn+Jt1v87xS+JAA4W2iUEDoMBRwPSrFp8ffi5Yc2XjGSLMYRtlrCNy4xg/JzwB19KUMK6cHFC9tC7dj/9k=",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [36,29,65,70] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [43,42,61,65] |
[
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",
"path": "image_2.jpg"
}
] | <image> <image>Given two images, you need to:
1. Analyze and Identify the target object marked by bounding box [41,37,64,53] in <image_1>;
2. Re-locate this target in <image_2>;
3. Return [x_min, y_min, x_max, y_max] coordinates of the target in <image_2>. | [53,43,74,60] |
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