Spaces:
Running
Running
major update
#47
by
MINGYISU
- opened
- Scores/LamRA-Ret-Qwen2.5VL-7b.json +2282 -0
- Scores/LamRA-Ret.json +2282 -0
- Scores/VLM2Vec-V1-Qwen2VL-2B.json +2282 -0
- Scores/VLM2Vec-V1-Qwen2VL-7B.json +2282 -0
- Scores/VLM2Vec-V2.0-Qwen2VL-2B.json +2282 -0
- Scores/colpali-v1.3.json +2282 -0
- Scores/gme-Qwen2-VL-2B-Instruct.json +2282 -0
- Scores/gme-Qwen2-VL-7B-Instruct.json +2282 -0
- app.py +43 -7
- utils.py +2 -3
- utils_v2.py +60 -39
Scores/LamRA-Ret-Qwen2.5VL-7b.json
ADDED
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@@ -0,0 +1,2282 @@
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|
| 1 |
+
{
|
| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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|
Scores/LamRA-Ret.json
ADDED
|
@@ -0,0 +1,2282 @@
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| 1 |
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|
Scores/VLM2Vec-V1-Qwen2VL-2B.json
ADDED
|
@@ -0,0 +1,2282 @@
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| 1 |
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|
Scores/VLM2Vec-V1-Qwen2VL-7B.json
ADDED
|
@@ -0,0 +1,2282 @@
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|
| 2281 |
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|
| 2282 |
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}
|
Scores/VLM2Vec-V2.0-Qwen2VL-2B.json
ADDED
|
@@ -0,0 +1,2282 @@
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| 2251 |
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| 2279 |
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| 2280 |
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|
| 2281 |
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}
|
| 2282 |
+
}
|
Scores/colpali-v1.3.json
ADDED
|
@@ -0,0 +1,2282 @@
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|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"model_name": "colpali-v1.3",
|
| 4 |
+
"report_generated_date": "2025-06-09T07:08:13.841120"
|
| 5 |
+
},
|
| 6 |
+
"metrics": {
|
| 7 |
+
"image": {
|
| 8 |
+
"ImageNet-1K": {
|
| 9 |
+
"hit@1": 0.424,
|
| 10 |
+
"hit@5": 0.63,
|
| 11 |
+
"hit@10": 0.695,
|
| 12 |
+
"ndcg_linear@1": 0.424,
|
| 13 |
+
"ndcg_linear@5": 0.5362278752072428,
|
| 14 |
+
"ndcg_linear@10": 0.5572292479510181,
|
| 15 |
+
"ndcg_exponential@1": 0.424,
|
| 16 |
+
"ndcg_exponential@5": 0.5362278752072428,
|
| 17 |
+
"ndcg_exponential@10": 0.5572292479510181,
|
| 18 |
+
"precision@1": 0.424,
|
| 19 |
+
"precision@5": 0.12599999999999997,
|
| 20 |
+
"precision@10": 0.0695,
|
| 21 |
+
"recall@1": 0.424,
|
| 22 |
+
"recall@5": 0.63,
|
| 23 |
+
"recall@10": 0.695,
|
| 24 |
+
"f1@1": 0.424,
|
| 25 |
+
"f1@5": 0.21000000000000005,
|
| 26 |
+
"f1@10": 0.1263636363636364,
|
| 27 |
+
"map@1": 0.424,
|
| 28 |
+
"map@5": 0.5047499999999999,
|
| 29 |
+
"map@10": 0.5134055555555556,
|
| 30 |
+
"mrr@1": 0.424,
|
| 31 |
+
"mrr@5": 0.5047499999999999,
|
| 32 |
+
"mrr@10": 0.5134055555555556,
|
| 33 |
+
"num_pred": 1000,
|
| 34 |
+
"num_data": 1000
|
| 35 |
+
},
|
| 36 |
+
"N24News": {
|
| 37 |
+
"hit@1": 0.255,
|
| 38 |
+
"hit@5": 0.522,
|
| 39 |
+
"hit@10": 0.698,
|
| 40 |
+
"ndcg_linear@1": 0.255,
|
| 41 |
+
"ndcg_linear@5": 0.39237570348741835,
|
| 42 |
+
"ndcg_linear@10": 0.4489355518555218,
|
| 43 |
+
"ndcg_exponential@1": 0.255,
|
| 44 |
+
"ndcg_exponential@5": 0.39237570348741835,
|
| 45 |
+
"ndcg_exponential@10": 0.4489355518555218,
|
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| 2218 |
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| 2219 |
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| 2220 |
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| 2221 |
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"num_pred": 8564,
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| 2222 |
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"num_data": 8564
|
| 2223 |
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},
|
| 2224 |
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"EgoSchema": {
|
| 2225 |
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| 2226 |
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| 2227 |
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| 2228 |
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"ndcg_linear@1": 0.384,
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| 2229 |
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"ndcg_linear@5": 0.6958150458511005,
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| 2230 |
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| 2231 |
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| 2232 |
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| 2233 |
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| 2234 |
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| 2235 |
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|
| 2236 |
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|
| 2237 |
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|
| 2238 |
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| 2239 |
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| 2240 |
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| 2241 |
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"f1@5": 0.3333333333333333,
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| 2242 |
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"f1@10": 0.18181818181818182,
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| 2243 |
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| 2244 |
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| 2245 |
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| 2246 |
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| 2247 |
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| 2248 |
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"mrr@10": 0.5960666666666666,
|
| 2249 |
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"num_pred": 500,
|
| 2250 |
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"num_data": 500
|
| 2251 |
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},
|
| 2252 |
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"ActivityNetQA": {
|
| 2253 |
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"hit@1": 0.513,
|
| 2254 |
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"hit@5": 1.0,
|
| 2255 |
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"hit@10": 1.0,
|
| 2256 |
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"ndcg_linear@1": 0.513,
|
| 2257 |
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"ndcg_linear@5": 0.8202627899893,
|
| 2258 |
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"ndcg_linear@10": 0.8202627899893,
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| 2259 |
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"ndcg_exponential@1": 0.513,
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| 2260 |
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| 2261 |
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| 2262 |
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| 2263 |
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"precision@5": 0.20000000000000004,
|
| 2264 |
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"precision@10": 0.10000000000000002,
|
| 2265 |
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|
| 2266 |
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|
| 2267 |
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| 2268 |
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| 2269 |
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| 2270 |
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| 2271 |
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| 2272 |
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| 2273 |
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| 2274 |
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| 2275 |
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| 2276 |
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| 2277 |
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|
| 2278 |
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"num_data": 1000
|
| 2279 |
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}
|
| 2280 |
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}
|
| 2281 |
+
}
|
| 2282 |
+
}
|
Scores/gme-Qwen2-VL-2B-Instruct.json
ADDED
|
@@ -0,0 +1,2282 @@
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|
| 1 |
+
{
|
| 2 |
+
"metadata": {
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| 3 |
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"model_name": "gme-Qwen2-VL-2B-Instruct",
|
| 4 |
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|
| 5 |
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},
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 15 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 24 |
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| 35 |
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| 36 |
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| 37 |
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| 65 |
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| 2262 |
+
"precision@1": 0.58,
|
| 2263 |
+
"precision@5": 0.20000000000000004,
|
| 2264 |
+
"precision@10": 0.10000000000000002,
|
| 2265 |
+
"recall@1": 0.58,
|
| 2266 |
+
"recall@5": 1.0,
|
| 2267 |
+
"recall@10": 1.0,
|
| 2268 |
+
"f1@1": 0.58,
|
| 2269 |
+
"f1@5": 0.3333333333333333,
|
| 2270 |
+
"f1@10": 0.18181818181818182,
|
| 2271 |
+
"map@1": 0.58,
|
| 2272 |
+
"map@5": 0.79,
|
| 2273 |
+
"map@10": 0.79,
|
| 2274 |
+
"mrr@1": 0.58,
|
| 2275 |
+
"mrr@5": 0.79,
|
| 2276 |
+
"mrr@10": 0.79,
|
| 2277 |
+
"num_pred": 1000,
|
| 2278 |
+
"num_data": 1000
|
| 2279 |
+
}
|
| 2280 |
+
}
|
| 2281 |
+
}
|
| 2282 |
+
}
|
Scores/gme-Qwen2-VL-7B-Instruct.json
ADDED
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@@ -0,0 +1,2282 @@
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|
| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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"mrr@5": 0.5684166666666667,
|
| 1996 |
+
"mrr@10": 0.5904809523809524,
|
| 1997 |
+
"num_pred": 1000,
|
| 1998 |
+
"num_data": 1000
|
| 1999 |
+
},
|
| 2000 |
+
"K700": {
|
| 2001 |
+
"hit@1": 0.397,
|
| 2002 |
+
"hit@5": 0.629,
|
| 2003 |
+
"hit@10": 0.715,
|
| 2004 |
+
"ndcg_linear@1": 0.397,
|
| 2005 |
+
"ndcg_linear@5": 0.5194137751146535,
|
| 2006 |
+
"ndcg_linear@10": 0.547354054247126,
|
| 2007 |
+
"ndcg_exponential@1": 0.397,
|
| 2008 |
+
"ndcg_exponential@5": 0.5194137751146535,
|
| 2009 |
+
"ndcg_exponential@10": 0.547354054247126,
|
| 2010 |
+
"precision@1": 0.397,
|
| 2011 |
+
"precision@5": 0.1258,
|
| 2012 |
+
"precision@10": 0.0715,
|
| 2013 |
+
"recall@1": 0.397,
|
| 2014 |
+
"recall@5": 0.629,
|
| 2015 |
+
"recall@10": 0.715,
|
| 2016 |
+
"f1@1": 0.397,
|
| 2017 |
+
"f1@5": 0.20966666666666675,
|
| 2018 |
+
"f1@10": 0.13,
|
| 2019 |
+
"map@1": 0.397,
|
| 2020 |
+
"map@5": 0.4830833333333333,
|
| 2021 |
+
"map@10": 0.4946920634920635,
|
| 2022 |
+
"mrr@1": 0.397,
|
| 2023 |
+
"mrr@5": 0.4830833333333333,
|
| 2024 |
+
"mrr@10": 0.4946920634920635,
|
| 2025 |
+
"num_pred": 1000,
|
| 2026 |
+
"num_data": 1000
|
| 2027 |
+
},
|
| 2028 |
+
"SmthSmthV2": {
|
| 2029 |
+
"hit@1": 0.306,
|
| 2030 |
+
"hit@5": 0.539,
|
| 2031 |
+
"hit@10": 0.621,
|
| 2032 |
+
"ndcg_linear@1": 0.306,
|
| 2033 |
+
"ndcg_linear@5": 0.4300547709738873,
|
| 2034 |
+
"ndcg_linear@10": 0.45655616986633807,
|
| 2035 |
+
"ndcg_exponential@1": 0.306,
|
| 2036 |
+
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|
| 2037 |
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|
| 2038 |
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"precision@1": 0.306,
|
| 2039 |
+
"precision@5": 0.10779999999999998,
|
| 2040 |
+
"precision@10": 0.062099999999999995,
|
| 2041 |
+
"recall@1": 0.306,
|
| 2042 |
+
"recall@5": 0.539,
|
| 2043 |
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|
| 2044 |
+
"f1@1": 0.306,
|
| 2045 |
+
"f1@5": 0.1796666666666667,
|
| 2046 |
+
"f1@10": 0.11290909090909092,
|
| 2047 |
+
"map@1": 0.306,
|
| 2048 |
+
"map@5": 0.39380000000000004,
|
| 2049 |
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"map@10": 0.40473095238095236,
|
| 2050 |
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"mrr@1": 0.306,
|
| 2051 |
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"mrr@5": 0.39380000000000004,
|
| 2052 |
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"mrr@10": 0.40473095238095236,
|
| 2053 |
+
"num_pred": 1000,
|
| 2054 |
+
"num_data": 1000
|
| 2055 |
+
},
|
| 2056 |
+
"HMDB51": {
|
| 2057 |
+
"hit@1": 0.479,
|
| 2058 |
+
"hit@5": 0.715,
|
| 2059 |
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|
| 2060 |
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"ndcg_linear@1": 0.479,
|
| 2061 |
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|
| 2062 |
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|
| 2063 |
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|
| 2064 |
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|
| 2065 |
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|
| 2066 |
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|
| 2067 |
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|
| 2068 |
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|
| 2069 |
+
"recall@1": 0.479,
|
| 2070 |
+
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|
| 2071 |
+
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|
| 2072 |
+
"f1@1": 0.479,
|
| 2073 |
+
"f1@5": 0.23833333333333337,
|
| 2074 |
+
"f1@10": 0.14636363636363636,
|
| 2075 |
+
"map@1": 0.479,
|
| 2076 |
+
"map@5": 0.5679833333333334,
|
| 2077 |
+
"map@10": 0.5802178571428571,
|
| 2078 |
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"mrr@1": 0.479,
|
| 2079 |
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"mrr@5": 0.5679833333333334,
|
| 2080 |
+
"mrr@10": 0.5802178571428571,
|
| 2081 |
+
"num_pred": 1000,
|
| 2082 |
+
"num_data": 1000
|
| 2083 |
+
},
|
| 2084 |
+
"UCF101": {
|
| 2085 |
+
"hit@1": 0.547,
|
| 2086 |
+
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|
| 2087 |
+
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|
| 2088 |
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|
| 2089 |
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"ndcg_linear@5": 0.671929263649622,
|
| 2090 |
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|
| 2091 |
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"ndcg_exponential@1": 0.547,
|
| 2092 |
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|
| 2093 |
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"ndcg_exponential@10": 0.6952345321688619,
|
| 2094 |
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"precision@1": 0.547,
|
| 2095 |
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"precision@5": 0.15519999999999998,
|
| 2096 |
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"precision@10": 0.08480000000000001,
|
| 2097 |
+
"recall@1": 0.547,
|
| 2098 |
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"recall@5": 0.776,
|
| 2099 |
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"recall@10": 0.848,
|
| 2100 |
+
"f1@1": 0.547,
|
| 2101 |
+
"f1@5": 0.25866666666666677,
|
| 2102 |
+
"f1@10": 0.1541818181818182,
|
| 2103 |
+
"map@1": 0.547,
|
| 2104 |
+
"map@5": 0.637,
|
| 2105 |
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"map@10": 0.6466361111111112,
|
| 2106 |
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"mrr@1": 0.547,
|
| 2107 |
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"mrr@5": 0.637,
|
| 2108 |
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"mrr@10": 0.6466361111111112,
|
| 2109 |
+
"num_pred": 1000,
|
| 2110 |
+
"num_data": 1000
|
| 2111 |
+
},
|
| 2112 |
+
"Breakfast": {
|
| 2113 |
+
"hit@1": 0.14318706697459585,
|
| 2114 |
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"hit@5": 0.5635103926096998,
|
| 2115 |
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"hit@10": 1.0,
|
| 2116 |
+
"ndcg_linear@1": 0.14318706697459585,
|
| 2117 |
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"ndcg_linear@5": 0.35021514635379236,
|
| 2118 |
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"ndcg_linear@10": 0.4904891071186407,
|
| 2119 |
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"ndcg_exponential@1": 0.14318706697459585,
|
| 2120 |
+
"ndcg_exponential@5": 0.35021514635379236,
|
| 2121 |
+
"ndcg_exponential@10": 0.4904891071186407,
|
| 2122 |
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"precision@1": 0.14318706697459585,
|
| 2123 |
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"precision@5": 0.11270207852193995,
|
| 2124 |
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"precision@10": 0.1,
|
| 2125 |
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"recall@1": 0.14318706697459585,
|
| 2126 |
+
"recall@5": 0.5635103926096998,
|
| 2127 |
+
"recall@10": 1.0,
|
| 2128 |
+
"f1@1": 0.14318706697459585,
|
| 2129 |
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"f1@5": 0.18783679753656665,
|
| 2130 |
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"f1@10": 0.1818181818181818,
|
| 2131 |
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"map@1": 0.14318706697459585,
|
| 2132 |
+
"map@5": 0.2807544264819091,
|
| 2133 |
+
"map@10": 0.33812731405110164,
|
| 2134 |
+
"mrr@1": 0.14318706697459585,
|
| 2135 |
+
"mrr@5": 0.2807544264819091,
|
| 2136 |
+
"mrr@10": 0.33812731405110164,
|
| 2137 |
+
"num_pred": 433,
|
| 2138 |
+
"num_data": 433
|
| 2139 |
+
},
|
| 2140 |
+
"MVBench": {
|
| 2141 |
+
"hit@1": 0.46625,
|
| 2142 |
+
"hit@5": 1.0,
|
| 2143 |
+
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|
| 2144 |
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|
| 2145 |
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"ndcg_linear@5": 0.7596556549662636,
|
| 2146 |
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"ndcg_linear@10": 0.7596556549662636,
|
| 2147 |
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"ndcg_exponential@1": 0.46625,
|
| 2148 |
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"ndcg_exponential@5": 0.7596556549662636,
|
| 2149 |
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|
| 2150 |
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|
| 2151 |
+
"precision@5": 0.20000000000000004,
|
| 2152 |
+
"precision@10": 0.10000000000000002,
|
| 2153 |
+
"recall@1": 0.46625,
|
| 2154 |
+
"recall@5": 1.0,
|
| 2155 |
+
"recall@10": 1.0,
|
| 2156 |
+
"f1@1": 0.46625,
|
| 2157 |
+
"f1@5": 0.3333333333333333,
|
| 2158 |
+
"f1@10": 0.18181818181818182,
|
| 2159 |
+
"map@1": 0.46625,
|
| 2160 |
+
"map@5": 0.678425,
|
| 2161 |
+
"map@10": 0.678425,
|
| 2162 |
+
"mrr@1": 0.46625,
|
| 2163 |
+
"mrr@5": 0.678425,
|
| 2164 |
+
"mrr@10": 0.678425,
|
| 2165 |
+
"num_pred": 4000,
|
| 2166 |
+
"num_data": 4000
|
| 2167 |
+
},
|
| 2168 |
+
"Video-MME": {
|
| 2169 |
+
"hit@1": 0.39185185185185184,
|
| 2170 |
+
"hit@5": 1.0,
|
| 2171 |
+
"hit@10": 1.0,
|
| 2172 |
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"ndcg_linear@1": 0.39185185185185184,
|
| 2173 |
+
"ndcg_linear@5": 0.7181257548926895,
|
| 2174 |
+
"ndcg_linear@10": 0.7181257548926895,
|
| 2175 |
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"ndcg_exponential@1": 0.39185185185185184,
|
| 2176 |
+
"ndcg_exponential@5": 0.7181257548926895,
|
| 2177 |
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"ndcg_exponential@10": 0.7181257548926895,
|
| 2178 |
+
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|
| 2179 |
+
"precision@5": 0.19999999999999996,
|
| 2180 |
+
"precision@10": 0.09999999999999998,
|
| 2181 |
+
"recall@1": 0.39185185185185184,
|
| 2182 |
+
"recall@5": 1.0,
|
| 2183 |
+
"recall@10": 1.0,
|
| 2184 |
+
"f1@1": 0.39185185185185184,
|
| 2185 |
+
"f1@5": 0.3333333333333334,
|
| 2186 |
+
"f1@10": 0.18181818181818188,
|
| 2187 |
+
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|
| 2188 |
+
"map@5": 0.6235802469135802,
|
| 2189 |
+
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|
| 2190 |
+
"mrr@1": 0.39185185185185184,
|
| 2191 |
+
"mrr@5": 0.6235802469135802,
|
| 2192 |
+
"mrr@10": 0.6235802469135802,
|
| 2193 |
+
"num_pred": 2700,
|
| 2194 |
+
"num_data": 2700
|
| 2195 |
+
},
|
| 2196 |
+
"NExTQA": {
|
| 2197 |
+
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|
| 2198 |
+
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|
| 2199 |
+
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|
| 2200 |
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|
| 2201 |
+
"ndcg_linear@5": 0.77906035779538,
|
| 2202 |
+
"ndcg_linear@10": 0.77906035779538,
|
| 2203 |
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|
| 2204 |
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"ndcg_exponential@5": 0.77906035779538,
|
| 2205 |
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|
| 2206 |
+
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|
| 2207 |
+
"precision@5": 0.20000000000000004,
|
| 2208 |
+
"precision@10": 0.10000000000000002,
|
| 2209 |
+
"recall@1": 0.5354974311069594,
|
| 2210 |
+
"recall@5": 1.0,
|
| 2211 |
+
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|
| 2212 |
+
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|
| 2213 |
+
"f1@5": 0.3333333333333333,
|
| 2214 |
+
"f1@10": 0.1818181818181818,
|
| 2215 |
+
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|
| 2216 |
+
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|
| 2217 |
+
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|
| 2218 |
+
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|
| 2219 |
+
"mrr@5": 0.7056632414759458,
|
| 2220 |
+
"mrr@10": 0.7056632414759458,
|
| 2221 |
+
"num_pred": 8564,
|
| 2222 |
+
"num_data": 8564
|
| 2223 |
+
},
|
| 2224 |
+
"EgoSchema": {
|
| 2225 |
+
"hit@1": 0.468,
|
| 2226 |
+
"hit@5": 1.0,
|
| 2227 |
+
"hit@10": 1.0,
|
| 2228 |
+
"ndcg_linear@1": 0.468,
|
| 2229 |
+
"ndcg_linear@5": 0.7452026017383512,
|
| 2230 |
+
"ndcg_linear@10": 0.7452026017383512,
|
| 2231 |
+
"ndcg_exponential@1": 0.468,
|
| 2232 |
+
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|
| 2233 |
+
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|
| 2234 |
+
"precision@1": 0.468,
|
| 2235 |
+
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|
| 2236 |
+
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|
| 2237 |
+
"recall@1": 0.468,
|
| 2238 |
+
"recall@5": 1.0,
|
| 2239 |
+
"recall@10": 1.0,
|
| 2240 |
+
"f1@1": 0.468,
|
| 2241 |
+
"f1@5": 0.3333333333333333,
|
| 2242 |
+
"f1@10": 0.18181818181818182,
|
| 2243 |
+
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|
| 2244 |
+
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|
| 2245 |
+
"map@10": 0.6607999999999999,
|
| 2246 |
+
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|
| 2247 |
+
"mrr@5": 0.6607999999999999,
|
| 2248 |
+
"mrr@10": 0.6607999999999999,
|
| 2249 |
+
"num_pred": 500,
|
| 2250 |
+
"num_data": 500
|
| 2251 |
+
},
|
| 2252 |
+
"ActivityNetQA": {
|
| 2253 |
+
"hit@1": 0.656,
|
| 2254 |
+
"hit@5": 1.0,
|
| 2255 |
+
"hit@10": 1.0,
|
| 2256 |
+
"ndcg_linear@1": 0.656,
|
| 2257 |
+
"ndcg_linear@5": 0.8730398352285815,
|
| 2258 |
+
"ndcg_linear@10": 0.8730398352285815,
|
| 2259 |
+
"ndcg_exponential@1": 0.656,
|
| 2260 |
+
"ndcg_exponential@5": 0.8730398352285815,
|
| 2261 |
+
"ndcg_exponential@10": 0.8730398352285815,
|
| 2262 |
+
"precision@1": 0.656,
|
| 2263 |
+
"precision@5": 0.20000000000000004,
|
| 2264 |
+
"precision@10": 0.10000000000000002,
|
| 2265 |
+
"recall@1": 0.656,
|
| 2266 |
+
"recall@5": 1.0,
|
| 2267 |
+
"recall@10": 1.0,
|
| 2268 |
+
"f1@1": 0.656,
|
| 2269 |
+
"f1@5": 0.3333333333333333,
|
| 2270 |
+
"f1@10": 0.18181818181818182,
|
| 2271 |
+
"map@1": 0.656,
|
| 2272 |
+
"map@5": 0.828,
|
| 2273 |
+
"map@10": 0.828,
|
| 2274 |
+
"mrr@1": 0.656,
|
| 2275 |
+
"mrr@5": 0.828,
|
| 2276 |
+
"mrr@10": 0.828,
|
| 2277 |
+
"num_pred": 1000,
|
| 2278 |
+
"num_data": 1000
|
| 2279 |
+
}
|
| 2280 |
+
}
|
| 2281 |
+
}
|
| 2282 |
+
}
|
app.py
CHANGED
|
@@ -24,7 +24,7 @@ with gr.Blocks() as block:
|
|
| 24 |
|
| 25 |
with gr.Tabs(elem_classes="tab-buttons") as tabs:
|
| 26 |
# Table 1
|
| 27 |
-
with gr.TabItem("📊 MMEB
|
| 28 |
with gr.Row():
|
| 29 |
with gr.Accordion("Citation", open=False):
|
| 30 |
citation_button = gr.Textbox(
|
|
@@ -106,7 +106,7 @@ with gr.Blocks() as block:
|
|
| 106 |
)
|
| 107 |
refresh_button.click(fn=refresh_data, outputs=data_component)
|
| 108 |
|
| 109 |
-
with gr.TabItem("📊 MMEB
|
| 110 |
with gr.Row():
|
| 111 |
with gr.Accordion("Citation", open=False):
|
| 112 |
citation_button2 = gr.Textbox(
|
|
@@ -189,13 +189,49 @@ with gr.Blocks() as block:
|
|
| 189 |
refresh_button.click(fn=v2.refresh_data, outputs=data_component)
|
| 190 |
|
| 191 |
# table 3
|
| 192 |
-
with gr.TabItem("
|
| 193 |
-
gr.
|
| 194 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 195 |
|
| 196 |
# table 4
|
| 197 |
-
with gr.TabItem("
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 198 |
with gr.Row():
|
| 199 |
gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
|
| 200 |
|
| 201 |
-
block.launch()
|
|
|
|
| 24 |
|
| 25 |
with gr.Tabs(elem_classes="tab-buttons") as tabs:
|
| 26 |
# Table 1
|
| 27 |
+
with gr.TabItem("📊 MMEB (Archived)", elem_id="qa-tab-table1", id=1):
|
| 28 |
with gr.Row():
|
| 29 |
with gr.Accordion("Citation", open=False):
|
| 30 |
citation_button = gr.Textbox(
|
|
|
|
| 106 |
)
|
| 107 |
refresh_button.click(fn=refresh_data, outputs=data_component)
|
| 108 |
|
| 109 |
+
with gr.TabItem("📊 **MMEB (V2)**", elem_id="qa-tab-table1", id=2):
|
| 110 |
with gr.Row():
|
| 111 |
with gr.Accordion("Citation", open=False):
|
| 112 |
citation_button2 = gr.Textbox(
|
|
|
|
| 189 |
refresh_button.click(fn=v2.refresh_data, outputs=data_component)
|
| 190 |
|
| 191 |
# table 3
|
| 192 |
+
with gr.TabItem("🖼️ Image", elem_id="qa-tab-table1", id=3):
|
| 193 |
+
data_component3 = gr.components.Dataframe(
|
| 194 |
+
value=df2[v2.COLUMN_NAMES_I],
|
| 195 |
+
headers=v2.COLUMN_NAMES_I,
|
| 196 |
+
type="pandas",
|
| 197 |
+
datatype=v2.DATA_TITLE_TYPE_I,
|
| 198 |
+
interactive=False,
|
| 199 |
+
visible=True,
|
| 200 |
+
max_height=2400,
|
| 201 |
+
)
|
| 202 |
|
| 203 |
# table 4
|
| 204 |
+
with gr.TabItem("💽 Video", elem_id="qa-tab-table1", id=4):
|
| 205 |
+
data_component4 = gr.components.Dataframe(
|
| 206 |
+
value=df2[v2.COLUMN_NAMES_V],
|
| 207 |
+
headers=v2.COLUMN_NAMES_V,
|
| 208 |
+
type="pandas",
|
| 209 |
+
datatype=v2.DATA_TITLE_TYPE_V,
|
| 210 |
+
interactive=False,
|
| 211 |
+
visible=True,
|
| 212 |
+
max_height=2400,
|
| 213 |
+
)
|
| 214 |
+
|
| 215 |
+
# table 5
|
| 216 |
+
with gr.TabItem("📑 Visual Doc", elem_id="qa-tab-table1", id=5):
|
| 217 |
+
data_component5 = gr.components.Dataframe(
|
| 218 |
+
value=df2[v2.COLUMN_NAMES_D],
|
| 219 |
+
headers=v2.COLUMN_NAMES_D,
|
| 220 |
+
type="pandas",
|
| 221 |
+
datatype=v2.DATA_TITLE_TYPE_D,
|
| 222 |
+
interactive=False,
|
| 223 |
+
visible=True,
|
| 224 |
+
max_height=2400,
|
| 225 |
+
)
|
| 226 |
+
|
| 227 |
+
# table 6
|
| 228 |
+
with gr.TabItem("📝 About", elem_id="qa-tab-table2", id=6):
|
| 229 |
+
gr.Markdown(LEADERBOARD_INFO, elem_classes="markdown-text")
|
| 230 |
+
gr.Image("overview.png", width=900, label="Dataset Overview")
|
| 231 |
+
|
| 232 |
+
# table 7
|
| 233 |
+
with gr.TabItem("🚀 Submit here! ", elem_id="submit-tab", id=7):
|
| 234 |
with gr.Row():
|
| 235 |
gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
|
| 236 |
|
| 237 |
+
block.launch(share=True)
|
utils.py
CHANGED
|
@@ -38,9 +38,8 @@ This comprehensive suite enables robust evaluation of multimodal embedding model
|
|
| 38 |
| [**🤗Hugging Face**](https://huggingface.co/datasets/TIGER-Lab/MMEB-V2) |
|
| 39 |
"""
|
| 40 |
|
| 41 |
-
TABLE_INTRODUCTION = """
|
| 42 |
-
**
|
| 43 |
-
Models are ranked based on **V1-Overall**."""
|
| 44 |
|
| 45 |
LEADERBOARD_INFO = """
|
| 46 |
## Dataset Summary
|
|
|
|
| 38 |
| [**🤗Hugging Face**](https://huggingface.co/datasets/TIGER-Lab/MMEB-V2) |
|
| 39 |
"""
|
| 40 |
|
| 41 |
+
TABLE_INTRODUCTION = """***Important Notes:*** \n
|
| 42 |
+
**We will be depreciating the MMEB-V1 leaderboard soon, and we will be releasing MMEB-V2 with more detailed scores and automatic evaluation.** \n"""
|
|
|
|
| 43 |
|
| 44 |
LEADERBOARD_INFO = """
|
| 45 |
## Dataset Summary
|
utils_v2.py
CHANGED
|
@@ -38,19 +38,37 @@ SPECIAL_METRICS = {
|
|
| 38 |
}
|
| 39 |
|
| 40 |
BASE_COLS = ['Rank', 'Models', 'Model Size(B)']
|
| 41 |
-
TASKS = ["Overall", "
|
| 42 |
-
|
| 43 |
|
| 44 |
-
|
|
|
|
| 45 |
['number'] * len(TASKS)
|
| 46 |
|
| 47 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 48 |
|
| 49 |
LEADERBOARD_INFO = """
|
| 50 |
## Dataset Summary
|
| 51 |
"""
|
| 52 |
|
| 53 |
-
CITATION_BUTTON_TEXT = r""""""
|
| 54 |
|
| 55 |
def load_single_json(file_path):
|
| 56 |
with open(file_path, 'r') as file:
|
|
@@ -60,53 +78,57 @@ def load_single_json(file_path):
|
|
| 60 |
def load_data(base_dir=SCORE_BASE_DIR):
|
| 61 |
all_data = []
|
| 62 |
for file_name in os.listdir(base_dir):
|
| 63 |
-
if file_name.endswith('
|
| 64 |
file_path = os.path.join(base_dir, file_name)
|
| 65 |
data = load_single_json(file_path)
|
| 66 |
all_data.append(data)
|
| 67 |
return all_data
|
| 68 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 69 |
def calculate_score(raw_scores=None):
|
| 70 |
"""This function calculates the overall average scores for all datasets as well as avg scores for each modality and sub-task based on the raw scores.
|
| 71 |
-
Algorithm summary:
|
| 72 |
"""
|
| 73 |
def get_avg(sum_score, leng):
|
| 74 |
avg = sum_score / leng if leng > 0 else 0.0
|
| 75 |
avg = round(avg, 2) # Round to 2 decimal places
|
| 76 |
return avg
|
| 77 |
|
|
|
|
| 78 |
avg_scores = {}
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
overall_scores_summary[modality] = (modality_sum_score, modality_datasets_len)
|
| 100 |
-
|
| 101 |
-
all_datasets_sum_score, all_datasets_len = 0.0, 0
|
| 102 |
-
for modality, (modality_sum_score, modality_datasets_len) in overall_scores_summary.items():
|
| 103 |
-
name = f"{modality.capitalize()}-Overall"
|
| 104 |
-
avg_scores[name] = get_avg(modality_sum_score, modality_datasets_len)
|
| 105 |
-
# Accumulate the scores sum and length for all datasets
|
| 106 |
-
all_datasets_sum_score += modality_sum_score
|
| 107 |
-
all_datasets_len += modality_datasets_len
|
| 108 |
-
avg_scores['Overall'] = get_avg(all_datasets_sum_score, all_datasets_len)
|
| 109 |
-
return avg_scores
|
| 110 |
|
| 111 |
def generate_model_row(data):
|
| 112 |
metadata = data['metadata']
|
|
@@ -127,7 +149,6 @@ def get_df():
|
|
| 127 |
df = df.sort_values(by='Overall', ascending=False).reset_index(drop=True)
|
| 128 |
df['Rank'] = range(1, len(df) + 1)
|
| 129 |
df = create_hyperlinked_names(df)
|
| 130 |
-
|
| 131 |
return df
|
| 132 |
|
| 133 |
def refresh_data():
|
|
|
|
| 38 |
}
|
| 39 |
|
| 40 |
BASE_COLS = ['Rank', 'Models', 'Model Size(B)']
|
| 41 |
+
TASKS = ["Overall", "I-CLS", "I-QA", "I-RET", "I-VG", "VisDoc", "V-CLS", "V-QA", "V-RET", "V-MRET"]
|
| 42 |
+
BASE_DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown']
|
| 43 |
|
| 44 |
+
COLUMN_NAMES = BASE_COLS + TASKS
|
| 45 |
+
DATA_TITLE_TYPE = BASE_DATA_TITLE_TYPE + \
|
| 46 |
['number'] * len(TASKS)
|
| 47 |
|
| 48 |
+
TASKS_I = ['Image-Overall'] + ALL_DATASETS_SPLITS['image']
|
| 49 |
+
COLUMN_NAMES_I = BASE_COLS + TASKS_I
|
| 50 |
+
DATA_TITLE_TYPE_I = BASE_DATA_TITLE_TYPE + \
|
| 51 |
+
['number'] * len(TASKS_I)
|
| 52 |
+
|
| 53 |
+
TASKS_V = ['Video-Overall'] + ALL_DATASETS_SPLITS['video']
|
| 54 |
+
COLUMN_NAMES_V = BASE_COLS + TASKS_V
|
| 55 |
+
DATA_TITLE_TYPE_V = BASE_DATA_TITLE_TYPE + \
|
| 56 |
+
['number'] * len(TASKS_V)
|
| 57 |
+
|
| 58 |
+
TASKS_D = ['VisDoc'] + ALL_DATASETS_SPLITS['visdoc']
|
| 59 |
+
COLUMN_NAMES_D = BASE_COLS + TASKS_D
|
| 60 |
+
DATA_TITLE_TYPE_D = BASE_DATA_TITLE_TYPE + \
|
| 61 |
+
['number'] * len(TASKS_D)
|
| 62 |
+
|
| 63 |
+
TABLE_INTRODUCTION = """**I-CLS**: Image Classification, **I-QA**: (Image) Visual Question Answering, **I-RET**: Image Retrieval, **I-VG**: (Image) Visual Grounding \n
|
| 64 |
+
**V-CLS**: Video Classification, **V-QA**: (Video) Visual Question Answering, **V-RET**: Video Retrieval, **V-MRET**: Video Moment Retrieval \n
|
| 65 |
+
**VisDoc**: Visual Document Understanding \n"""
|
| 66 |
|
| 67 |
LEADERBOARD_INFO = """
|
| 68 |
## Dataset Summary
|
| 69 |
"""
|
| 70 |
|
| 71 |
+
CITATION_BUTTON_TEXT = r"""TBA"""
|
| 72 |
|
| 73 |
def load_single_json(file_path):
|
| 74 |
with open(file_path, 'r') as file:
|
|
|
|
| 78 |
def load_data(base_dir=SCORE_BASE_DIR):
|
| 79 |
all_data = []
|
| 80 |
for file_name in os.listdir(base_dir):
|
| 81 |
+
if file_name.endswith('.json'):
|
| 82 |
file_path = os.path.join(base_dir, file_name)
|
| 83 |
data = load_single_json(file_path)
|
| 84 |
all_data.append(data)
|
| 85 |
return all_data
|
| 86 |
|
| 87 |
+
def load_scores(raw_scores=None):
|
| 88 |
+
"""This function loads the raw scores from the user provided scores summary and flattens them into a single dictionary."""
|
| 89 |
+
all_scores = {}
|
| 90 |
+
for modality, datasets_list in DATASETS.items(): # Ex.: ('image', {'I-CLS': [...], 'I-QA': [...]})
|
| 91 |
+
for sub_task, datasets in datasets_list.items(): # Ex.: ('I-CLS', ['VOC2007', 'N24News', ...])
|
| 92 |
+
for dataset in datasets: # Ex.: 'VOC2007'
|
| 93 |
+
score = raw_scores.get(modality, {}).get(dataset, 0.0)
|
| 94 |
+
score = 0.0 if score == "FILE_N/A" else score
|
| 95 |
+
metric = SPECIAL_METRICS.get(dataset, 'hit@1')
|
| 96 |
+
metric = "ndcg@5" if modality == 'visdoc' else metric
|
| 97 |
+
if isinstance(score, dict):
|
| 98 |
+
score = score.get(metric, 0.0)
|
| 99 |
+
all_scores[dataset] = round(score, 2)
|
| 100 |
+
return all_scores
|
| 101 |
+
|
| 102 |
def calculate_score(raw_scores=None):
|
| 103 |
"""This function calculates the overall average scores for all datasets as well as avg scores for each modality and sub-task based on the raw scores.
|
|
|
|
| 104 |
"""
|
| 105 |
def get_avg(sum_score, leng):
|
| 106 |
avg = sum_score / leng if leng > 0 else 0.0
|
| 107 |
avg = round(avg, 2) # Round to 2 decimal places
|
| 108 |
return avg
|
| 109 |
|
| 110 |
+
all_scores = load_scores(raw_scores)
|
| 111 |
avg_scores = {}
|
| 112 |
+
|
| 113 |
+
# Calculate overall score for all datasets
|
| 114 |
+
avg_scores['Overall'] = get_avg(sum(all_scores.values()), len(ALL_DATASETS))
|
| 115 |
+
|
| 116 |
+
# Calculate scores for each modality
|
| 117 |
+
for modality in MODALITIES:
|
| 118 |
+
datasets_for_each_modality = ALL_DATASETS_SPLITS.get(modality, [])
|
| 119 |
+
avg_scores[f"{modality.capitalize()}-Overall"] = get_avg(
|
| 120 |
+
sum(all_scores.get(dataset, 0.0) for dataset in datasets_for_each_modality),
|
| 121 |
+
len(datasets_for_each_modality)
|
| 122 |
+
)
|
| 123 |
+
|
| 124 |
+
# Calculate scores for each sub-task
|
| 125 |
+
for modality, datasets_list in DATASETS.items():
|
| 126 |
+
for sub_task, datasets in datasets_list.items():
|
| 127 |
+
sub_task_score = sum(all_scores.get(dataset, 0.0) for dataset in datasets)
|
| 128 |
+
avg_scores[sub_task] = get_avg(sub_task_score, len(datasets))
|
| 129 |
+
|
| 130 |
+
all_scores.update(avg_scores)
|
| 131 |
+
return all_scores
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 132 |
|
| 133 |
def generate_model_row(data):
|
| 134 |
metadata = data['metadata']
|
|
|
|
| 149 |
df = df.sort_values(by='Overall', ascending=False).reset_index(drop=True)
|
| 150 |
df['Rank'] = range(1, len(df) + 1)
|
| 151 |
df = create_hyperlinked_names(df)
|
|
|
|
| 152 |
return df
|
| 153 |
|
| 154 |
def refresh_data():
|