image
imagewidth (px)
84
1.2k
label
class label
2 classes
id
stringlengths
32
32
0charts
9c4053acc0c82e9f225e3b634bd9c220
0charts
e936400f34a573023f4bc96c3af2a864
0charts
cd44051322a294eed6c49138f084fba7
0charts
4143935566f9f52dd4643d2611061cff
0charts
81c5d1ecf6360e56d5b7f6f7ad266894
0charts
ba75d2d9409f2173013dc3304169ffe2
0charts
0e723533a333297db0e6bd9f13d6efb4
0charts
19667a217c2df5d9a3696c8c2b4d7537
0charts
945e401567975085e69ba3845f8bb876
0charts
4e93e79d426000cdf4e2ced41d5aae8e
0charts
00be9f63e07b0acdad092a02966085c7
0charts
322d25a2f505fc13d9846b2c892812c1
0charts
949cba0b03f860c52296ef8b786ce6c5
0charts
5c02bba89700bf5dcfc256d13f16c676
0charts
188ad2fccc1dccaedd43fb1a1c7f390f
0charts
79caf729e1f1c22dc2940ce0436a01e4
0charts
6b6d8176870d77537d011d065aa6ce87
0charts
f8ea695db8d5ab3569ced5f8fde92d44
0charts
0955678f022cb5c54a3aea372ab8e5e1
0charts
304ba1e77c6423bb2c5bf207bf4b578e
0charts
ebd95f45310976a36bf666f9ef477983
0charts
894222d7a85b72d32d6c9f7f98aec030
0charts
a4764c431d3c4f1c8a656d049414db65
0charts
71024a981fed777c5c852d317a770d82
0charts
b6ed562cc2340432c073e276adedd528
0charts
43f00527d11c83d71519c7eb680ce1a2
0charts
dcd394f4f82b06f281f84dab3230f57a
0charts
8f4f7526d12a3c16859278e69609c2a3
0charts
e234bab9dedf50b7fb3303876cd1d7bc
0charts
9e802523bb3b2d1b0e92b86314e11b2b
0charts
5379581f1afebb369624a142a304d3e2
0charts
9300ff167ae5821c4995c6cb357f03b5
0charts
3b431cc70710eb19f6fe0a119a26bca8
0charts
2bb9d7d167cccc3d8f4d42d9da315419
0charts
3a0291bd046f47ae011f780b87f7cb85
0charts
030b969d2cda7d24cfc01738cbd7dedb
0charts
45fb784c8eb9a2295157e3cb5f8f8a4e
0charts
627dbb852899cebf00ac11406d0058c3
0charts
ddf76c85d5171fb35b72d01ed44b2a93
0charts
e2b87f7c50e89ec85f9783eb82afdd8c
0charts
b2ff6c934ad7517f5208da9bba8f3324
0charts
0bf34b1c865502984637f4a91639d1e2
0charts
3f2067df4f8dcedd18eeb11586d842f5
0charts
23e577638d7598e5f2856a93d5b15873
0charts
2f0751c23c03144fb84ff49574a7e2dd
0charts
caffb34ce45e188c0cd78875a55b91b9
0charts
8309c13b69135cf9a8d4418fe436f517
0charts
a7db75b3c8808415ea69497893aece92
0charts
2657a1d9a76bd2492e9777c6e4fdd398
0charts
55fa617badc4ea4a0245120fd8aeda37
0charts
b095d290821d60bb0e1b8fca67156bc3
0charts
366a164dd4b3302de378fa73e020abea
0charts
402ae553e6addb40bee7dd225b7b7825
0charts
470e82d5b78b489971ce2de2230fc64f
0charts
d521bafe54b9341d1ea12e005936ae2e
0charts
03e91dc9f97062e9cbafa8fe61e38830
0charts
2a42ac2a255e665991100fa97d7c7f27
0charts
6e0f6f0434d7e859a81c28f19c77fc56
0charts
dcc5651319707847536974552ef60b6d
0charts
7a867e04603a705e53a3e256cf67aaf6
0charts
07b466d0b7aac6c6d53c1f47d3bd2a1e
0charts
383f8a3fa6fbd662fc3d88bbe36aaea4
0charts
b1c98ef708be17e55175c5d14d3b30f7
0charts
4d3c1f2b8235bb51f6f22fd912e68b8c
0charts
37c64bdf40486de45f12cc145a205660
0charts
3d774c5a731632e5b4d57a5636575727
0charts
b79fddaf4e1ccb98c0b4238fb8921a60
0charts
0988417bbc8a4872d487ff431e779462
0charts
3e3d8cd7f5860007287b046e376d743f
0charts
3ace80b6a7a393b16b0a546019e817df
0charts
417571373494bdc771ac43c83cd09a28
0charts
4ac65082262a60752552b2596be45058
0charts
3a0e626ef154b9cc59ec7ae8402ccdc1
0charts
8b453b29e752aff1ac9abd2675b542ba
0charts
42526c7e78d36090a696e1df08a61d6e
0charts
17c2cc99dd4113960214b700daf01859
0charts
bdde6245f4f54203c6151dc470632cab
0charts
5b6b4c0f1c093fd2c693c7c09dcc7c59
0charts
1794e7fe25f49c090e4c0d1aace95fe9
0charts
c44b9b531aa50ae0882add442b840c2d
0charts
a7eb2a32cbd4d57c48769bdf2559006b
0charts
7e2b7356202a4d8f8fd4c4aefd82c0a2
0charts
855f08f25fba7a0577de8bd353f54b0c
0charts
54ab4d2ddc9464194c4e015d4354c5a6
0charts
6d9115a56e4f5e6b07917dfd61792eff
0charts
63fd5fc1db9c82d2553a7f173a551060
0charts
9c1e52743a7559f188ddd0a2837a8f86
0charts
00e60a51c7ae6ce2bd7e98b5e8ba5bce
0charts
060d6e120dfcd8ed0d1a3d42b78d576e
0charts
77cb6c243d64f2b1c096e45d252d2394
0charts
52fa2b3d9ea0f88d88a887d52d7ee6c9
0charts
8c7c67dda6a8e8e2b853baa53bb5a960
0charts
05c0f5ba186bb5639fb1b2a71c92f5c8
0charts
bda026668d3425731d89ed995251946b
0charts
0e86a573c94d96dca718a63f047afe81
0charts
fe27770e07519f1c068c8f955a13c85a
0charts
f4f06c79d18af60beff10ec47898d671
0charts
6d2c2248a0891965aaad66ebe8cfc499
0charts
986872006c2ba4c193f4543e93583173
0charts
46388fb7d6c84bea65af94859fe8823b

FinTwit Images

This dataset is a collection of a sample of images from tweets that I scraped using my Discord bot that keeps track of financial influencers on Twitter. The data consists of images that were part of tweets that did not mention a ticker. This dataset can be used for a wide variety of tasks, such as image classification or feature extraction.

FinTwit Charts Collection

This dataset is part of a larger collection of datasets, scraped from Twitter and labeled by a human (me). Below is the list of related datasets.

  • Crypto Charts: Images of financial charts of cryptocurrencies
  • Stock Charts: Images of financial charts of stocks
  • FinTwit Images: Images that had no clear description, this contains a lot of non-chart images

Dataset Structure

Each images in the dataset is structured as follows:

  • Image: The image of the tweet, this can be of varying dimensions.
  • Label: A numerical label indicating the category of the image, with '1' for charts, and '0' for non-charts.

Dataset Size

The dataset comprises 4,579 images in total, categorized into:

  • 1,083 chart images
  • 3,496 non-chart images

Usage

I used this dataset for training my chart-recognizer model for classifying if an image is a chart or not.

Acknowledgments

We extend our heartfelt gratitude to all the authors of the original tweets.

License

This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets.

Downloads last month
202

Models trained or fine-tuned on StephanAkkerman/fintwit-images