Scheduled Commit

data/retrieval_battle-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -160,3 +160,4 @@
 {"tstamp": 1728055790.4076, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "67848ffd4c32469a92af622b5a51ebac", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "best ways to search relevant article ", "0_output": [["best ways to search relevant article ", "Title: Finding and Recommending Scholarly Articles\n\nAbstract: The rate at which scholarly literature is being produced has been increasing at approximately 3.5 percent per year for decades. This means that during a typical 40 year career the amount of new literature produced each year increases by a factor of four. The methods scholars use to discover relevant literature must change. Just like everybody else involved in information discovery, scholars are confronted with information overload. Two decades ago, this discovery process essentially consisted of paging through abstract books, talking to colleagues and librarians, and browsing journals. A time-consuming process, which could even be longer if material had to be shipped from elsewhere. Now much of this discovery process is mediated by online scholarly information systems. All these systems are relatively new, and all are still changing. They all share a common goal: to provide their users with access to the literature relevant to their specific needs. To achieve this each system responds to actions by the user by displaying articles which the system judges relevant to the user's current needs. Recently search systems which use particularly sophisticated methodologies to recommend a few specific papers to the user have been called \"recommender systems\". These methods are in line with the current use of the term \"recommender system\" in computer science. We do not adopt this definition, rather we view systems like these as components in a larger whole, which is presented by the scholarly information systems themselves. In what follows we view the recommender system as an aspect of the entire information system; one which combines the massive memory capacities of the machine with the cognitive abilities of the human user to achieve a human-machine synergy."]], "0_corpus": "arxiv", "1_conv_id": "2d28bd14957d40a582c0c76cb91c65c5", "1_model_name": "voyage-multilingual-2", "1_prompt": "best ways to search relevant article ", "1_output": [["best ways to search relevant article ", "Title: Towards a Semantic Search Engine for Scientific Articles\n\nAbstract: Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a great challenge. As a starting point, semantic relations between keywords from scientific articles could be extracted in order to classify articles. This might help later in the process of browsing and searching for content in a meaningful scientific way. Indeed, by connecting keywords, the context of the article can be extracted. This paper aims to provide ideas to build such a smart search engine and describes the initial contributions towards achieving such an ambitious goal."]], "1_corpus": "arxiv"}
 {"tstamp": 1728055888.5455, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "aeaf9946eb9b40cb8df74422d910e9fe", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Семантический поиск похожих научных статей на русском языке", "0_output": [["Семантический поиск похожих научных статей на русском языке", "Title: Evaluation experiments on related terms search in Wikipedia: Information Content and Adapted HITS (In Russian)\n\nAbstract: The classification of metrics and algorithms search for related terms via WordNet, Roget's Thesaurus, and Wikipedia was extended to include adapted HITS algorithm. Evaluation experiments on Information Content and adapted HITS algorithm are described. The test collection of Russian word pairs with human-assigned similarity judgments is proposed. ----- Klassifikacija metrik i algoritmov poiska semanticheski blizkih slov v tezaurusah WordNet, Rozhe i jenciklopedii Vikipedija rasshirena adaptirovannym HITS algoritmom. S pomow'ju jeksperimentov v Vikipedii oceneny metrika Information Content i adaptirovannyj algoritm HITS. Predlozhen resurs dlja ocenki semanticheskoj blizosti russkih slov."]], "0_corpus": "arxiv", "1_conv_id": "bd5ae926e2b747b58befb8cf04831a18", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Семантический поиск похожих научных статей на русском языке", "1_output": [["Семантический поиск похожих научных статей на русском языке", "Title: Texts in, meaning out: neural language models in semantic similarity task for Russian\n\nAbstract: Distributed vector representations for natural language vocabulary get a lot of attention in contemporary computational linguistics. This paper summarizes the experience of applying neural network language models to the task of calculating semantic similarity for Russian. The experiments were performed in the course of Russian Semantic Similarity Evaluation track, where our models took from the 2nd to the 5th position, depending on the task. We introduce the tools and corpora used, comment on the nature of the shared task and describe the achieved results. It was found out that Continuous Skip-gram and Continuous Bag-of-words models, previously successfully applied to English material, can be used for semantic modeling of Russian as well. Moreover, we show that texts in Russian National Corpus (RNC) provide an excellent training material for such models, outperforming other, much larger corpora. It is especially true for semantic relatedness tasks (although stacking models trained on larger corpora on top of RNC models improves performance even more). High-quality semantic vectors learned in such a way can be used in a variety of linguistic tasks and promise an exciting field for further study."]], "1_corpus": "arxiv"}
 {"tstamp": 1728056690.1517, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a18383d181684287829141784aefd5a6", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "What is the population of BERLIN ?", "0_output": [["What is the population of BERLIN ?", "Berlin\n\nBerlin is the capital and largest city of Germany, both by area and by population. Its more than 3.85 million inhabitants make it the European Union's most populous city, as measured by population within city limits having gained this status after the United Kingdom's, and thus London's, departure from the European Union. Simultaneously, the city is one of the states of Germany, and is the third smallest state in the country in terms of area. Berlin is surrounded by the state of Brandenburg, and Brandenburg's capital Potsdam is nearby. The urban area of Berlin has a population of over 4.5 million and is therefore the most populous urban area in Germany. The Berlin-Brandenburg capital region has around 6.2 million inhabitants and is Germany's second-largest metropolitan region after the Rhine-Ruhr region, and the sixth-biggest metropolitan region by GDP in the European Union.\nBerlin was built along the banks of the Spree river, which flows into the Havel in the western borough of Spandau. The city incorporates lakes in the western and southeastern boroughs, the largest of which is Müggelsee. About one-third of the city's area is composed of forests, parks and gardens, rivers, canals, and lakes."]], "0_corpus": "wikipedia", "1_conv_id": "3d6513ac1b5d46bdbab2e79d2a15b3c4", "1_model_name": "text-embedding-3-large", "1_prompt": "What is the population of BERLIN ?", "1_output": [["What is the population of BERLIN ?", "Demographics of Berlin\n\nThe industrialisation had brought about a rapid expansion of the suburbs, many of them developed explicitly for workers of specific factories, e.g. Siemensstadt and Borsigwalde. The Greater Berlin Act of 1920 (Groß-Berlin-Gesetz) boosted the population by incorporating many hitherto autonomous towns and cities, e.g. Spandau and Köpenick at the margins of the modern metropolis, but also Charlottenburg, nowadays almost in the heart of the city. The city approximately reached its modern extent, growing from  to . This expansion made Berlin the most populous city proper of Continental Europe in the interwar period (though not the largest agglomeration) and the third-largest in the world behind London and New York.\nThe four-million mark was surpassed in the 1920s, and in 1942, the officially registered population reached its maximum of 4.48 million, although because of the war conditions, this was an overestimation. More likely estimates based on food rationing data show lower numbers of 3.95 million people in February 1942 and only 3.11 million people in February 1944 (incl. 177,000 foreigners) when aerial attacks approached its most intense phase.\nIn the context of the more general huge population movements in immediately post-war Germany, a significant part of Berlin's pre-war population permanently resettled to other parts of Germany or abroad. A 1946 census counted 436,600 Berliners in the western occupation zones and 306,823 in the Soviet zone. In 1950, this number had risen to 518,218 in what had now become the Federal Republic. Since the end of World War II, the city population has been fluctuating between 3 and 3.5 million, with a low of less than 3.1 million from the mid-1970s to mid-1980s. Between 1950 and 1961, so between the establishment of the Soviet-backed German Democratic Republic and the construction of the Berlin Wall, most of the losses were incurred by East Berlin, while West Berlin showed modest growth of 2.3%."]], "1_corpus": "wikipedia"}
+{"tstamp": 1728063013.1007, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "67176d488f2749c899e84f99b8f923f9", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "4f22b73f78a9421ea2de9704de319aba", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "1_corpus": "wikipedia"}

data/retrieval_individual-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -729,3 +729,9 @@
 {"tstamp": 1728056675.98, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1728056675.0264, "finish": 1728056675.98, "ip": "", "conv_id": "3d6513ac1b5d46bdbab2e79d2a15b3c4", "model_name": "text-embedding-3-large", "prompt": "What is the population of BERLIN ?", "output": [["What is the population of BERLIN ?", "Demographics of Berlin\n\nThe industrialisation had brought about a rapid expansion of the suburbs, many of them developed explicitly for workers of specific factories, e.g. Siemensstadt and Borsigwalde. The Greater Berlin Act of 1920 (Groß-Berlin-Gesetz) boosted the population by incorporating many hitherto autonomous towns and cities, e.g. Spandau and Köpenick at the margins of the modern metropolis, but also Charlottenburg, nowadays almost in the heart of the city. The city approximately reached its modern extent, growing from  to . This expansion made Berlin the most populous city proper of Continental Europe in the interwar period (though not the largest agglomeration) and the third-largest in the world behind London and New York.\nThe four-million mark was surpassed in the 1920s, and in 1942, the officially registered population reached its maximum of 4.48 million, although because of the war conditions, this was an overestimation. More likely estimates based on food rationing data show lower numbers of 3.95 million people in February 1942 and only 3.11 million people in February 1944 (incl. 177,000 foreigners) when aerial attacks approached its most intense phase.\nIn the context of the more general huge population movements in immediately post-war Germany, a significant part of Berlin's pre-war population permanently resettled to other parts of Germany or abroad. A 1946 census counted 436,600 Berliners in the western occupation zones and 306,823 in the Soviet zone. In 1950, this number had risen to 518,218 in what had now become the Federal Republic. Since the end of World War II, the city population has been fluctuating between 3 and 3.5 million, with a low of less than 3.1 million from the mid-1970s to mid-1980s. Between 1950 and 1961, so between the establishment of the Soviet-backed German Democratic Republic and the construction of the Berlin Wall, most of the losses were incurred by East Berlin, while West Berlin showed modest growth of 2.3%."]], "corpus": "wikipedia"}
 {"tstamp": 1728056731.3956, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1728056729.1211, "finish": 1728056731.3956, "ip": "", "conv_id": "6183cbd3b6ea4368aab02dd40b380207", "model_name": "text-embedding-004", "prompt": "What is the population of Berlin ?", "output": [["What is the population of Berlin ?", "Demographics of Berlin\n\nIn December 2019, the city-state of Berlin had a population of 3,769,495 registered inhabitants in an area of . The city's population density was 4,227 inhabitants per km2. Berlin is Germany's largest city and the most populous city proper in the European Union.\nIn December 2015, there were 621,075 registered residents of foreign nationality, originating from approximately 190 different countries.\nHistory\nThe city responded to the 1685 revocation of the Edict of Nantes in France with the Edict of Potsdam, which guaranteed religious freedom and tax-free status to French Huguenot refugees for ten years. Thanks to its role as the capital of rising Prussia, the population grew steadily: it surpassed the 100,000 mark in 1747 and the one-million mark in 1877."]], "corpus": "wikipedia"}
 {"tstamp": 1728056731.3956, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1728056729.1211, "finish": 1728056731.3956, "ip": "", "conv_id": "c44b630bba0b4511aff479ced395b5d3", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "What is the population of Berlin ?", "output": [["What is the population of Berlin ?", "Berlin population statistics\n\nBerlin is the most populous city in the European Union, as calculated by city-proper population (not metropolitan area).\nDemographics\nPopulation by borough\nHistorical development of Berlin's population\nThe spike in population in 1920 is a result of the Greater Berlin Act.\nPopulation by nationality\nOn 31 December 2010 the largest groups by foreign nationality were citizens from Turkey (104,556), Poland (40,988), Serbia (19,230), Italy (15,842), Russia (15,332), United States (12,733), France (13,262), Vietnam (13,199), Croatia (10,104), Bosnia and Herzegovina (10,198), UK (10,191), Greece (9,301), Austria (9,246), Ukraine (8,324), Lebanon (7,078), Spain (7,670), Bulgaria (9,988), the People's Republic of China (5,632), Thailand (5,037). There is also a large Arabic community, mostly from Lebanon, Palestine and Iraq. Additionally, Berlin has one of the largest Vietnamese communities outside Vietnam, with about 83,000 people of Vietnamese origin."]], "corpus": "wikipedia"}
+{"tstamp": 1728062957.5592, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1728062955.2355, "finish": 1728062957.5592, "ip": "", "conv_id": "9f6ef0e3ff6b4815b86f4340aebd9f70", "model_name": "text-embedding-3-large", "prompt": "How do I look yellow man?", "output": [["How do I look yellow man?", "Yellowman\n\nWinston Foster , (1956 or 15 January 1959) better known by the stage name Yellowman and also known as King Yellowman, is a Jamaican reggae and dancehall deejay. He first became popular in Jamaica in the 1980s, rising to prominence with a series of singles that established his reputation.\nYellowman is considered to be one of the genre's pioneers and has been credited with \"almost single-handedly [creating] the coarse, crude and fearlessly direct sound of today's dancehall.\" Spearheading the first generation of dancehall deejays, he brought the genre to an international audience.\nCareer\nWinston Foster was abandoned by his parents and grew up in the Maxfield Children's Home and the Catholic orphanage Alpha Boys School in Kingston, the latter known for its musical alumni. He was shunned due to having albinism, which was not typically socially accepted in Jamaica. In the late 1970s Yellowman first gained wide attention when he finished second to Nadine Sutherland in the 1978 Tastee Talent Contest, a competition he would go on to win in the following year's edition, launching his professional career. Like many Jamaican deejays, he honed his talents by frequently performing at outdoor sound-system dances, prominently with Aces International. He had success as a recording artist, working with producer Henry \"Junjo\" Lawes during the early 1980s. In 1981, after becoming popular throughout Jamaica, Yellowman became the first dancehall artist to be signed to a major American label (Columbia Records)."]], "corpus": "wikipedia"}
+{"tstamp": 1728062957.5592, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1728062955.2355, "finish": 1728062957.5592, "ip": "", "conv_id": "d9a909852a3f4b5796070bce872f539c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "How do I look yellow man?", "output": [["How do I look yellow man?", "Yellow\n\nBecause it was widely available, yellow ochre pigment was one of the first colors used in art; the Lascaux cave in France has a painting of a yellow horse 17,000 years old.  Ochre and orpiment pigments were used to represent gold and skin color in Egyptian tombs, then in the murals in Roman villas. In the early Christian church, yellow was the color associated with the Pope and the golden keys of the Kingdom, but it was also associated with Judas Iscariot and used to mark heretics. In the 20th century, Jews in Nazi-occupied Europe were forced to wear a yellow star. In China, bright yellow was the color of the Middle Kingdom, and could be worn only by the emperor and his household; special guests were welcomed on a yellow carpet.\nAccording to surveys in Europe, Canada, the United States and elsewhere, yellow is the color people most often associate with amusement, gentleness, humor, happiness, and spontaneity; however it can also be associated with duplicity, envy, jealousy, greed, justice, and, in the U.S., cowardice. In Iran it has connotations of pallor/sickness, but also wisdom and connection. In China and many Asian countries, it is seen as the color of happiness, glory, harmony and wisdom.\nEtymology\nThe word yellow is from the Old English  (oblique case), meaning \"yellow, and yellowish\", derived from the Proto-Germanic word gelwaz \"yellow\". It has the same Indo-European base, , as the words gold and yell; gʰel- means both bright and gleaming, and to cry out.\nThe English term is related to other Germanic words for yellow, namely Scots , East Frisian jeel, West Frisian , Dutch , German , and Swedish and Norwegian . According to the Oxford English Dictionary, the oldest known use of this word in English is from The Epinal Glossary in 700.\nScience and nature\nOptics, color printing, and computer screens"]], "corpus": "wikipedia"}
+{"tstamp": 1728062985.0282, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1728062982.8897, "finish": 1728062985.0282, "ip": "", "conv_id": "67176d488f2749c899e84f99b8f923f9", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1728062985.0282, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728062982.8897, "finish": 1728062985.0282, "ip": "", "conv_id": "4f22b73f78a9421ea2de9704de319aba", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "corpus": "wikipedia"}
+{"tstamp": 1728063044.184, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728063042.0057, "finish": 1728063044.184, "ip": "", "conv_id": "ec5172c8c51f47fb87871f7bf3838a45", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1728063044.184, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1728063042.0057, "finish": 1728063044.184, "ip": "", "conv_id": "edcb56fde75b4f3999ad824083003998", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone. \nThe video stream is written as \"socket\" to a file as H264 stream.\n$ ffmpeg -i [STREAM]\nInput #0, h264, from 'stream_h264':\n  Duration: N/A, bitrate: N/A\n  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc\n\nReading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:\nmplayer -fs -demuxer h264es -benchmark stream_h264\n\nThis plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python? \nAll recommendations are more than welcome!\n"]], "corpus": "stackexchange"}