image
imagewidth (px)
190
1.2k
label
class label
2 classes
id
stringlengths
32
32
0charts
419d6afa183cd92326a8756fbfd3d86f
0charts
6eea6614acf920737107a8f7787ec303
0charts
79cda514a40e893c97bdffa4b1b2bfd2
0charts
89295c0032c29d4bce6d3c7c75762355
0charts
1fe98a17ccc25c1eefc01b61c38e0d28
0charts
baee408553a8c4a279397582a90eb462
0charts
0a260c571b800b0f41b6af38af638240
0charts
bfef93a88b5472aeab2dd6138fc3e356
0charts
cd84d7e65c6782c81f1b98fda2afc4b5
0charts
87fb5dd7574b7db04924c5a4e9c0b436
0charts
eaa1f3fdfa9944719b13d2669bf565ab
0charts
5901684dbabe9a1e3db766a0805e74e9
0charts
72664505f06b34a5a3f718350ce28f30
0charts
93ef98feab171599b7ca5c95afb21938
0charts
c96b9ae7c22753ec64bd1dd4bea0194a
0charts
99feb2cbaddd89673536f3280b6626f2
0charts
6dd25ce095b0bc72366920543af48bcd
0charts
80501f7d9754585e891d11cf15155731
0charts
e127d7f4f775a1ead975deb44ed37978
0charts
62bb8be66473bf3084f38f3e240f0e76
0charts
5ab6e9fe8b48509d8675baf6632e3e45
0charts
766078edeed4d602cfccad87dd583a9f
0charts
b41313fdccb0f4b12f5e31821b2af376
0charts
2ae85d212dec87b22419876b26528571
0charts
a464522e1c79474933eff613b60a6667
0charts
6f4f3a9e382226ad2db0ff67c7d844b4
0charts
49cc9efe24644a10521fa2227ff1c30b
0charts
2f808526322f7dd88297021d69f50ac2
0charts
338742cf2337b21433216a97fcabaa60
0charts
deed02134a6a93bab6c889df5646f998
0charts
b348dd746ba3bb2f73d9ecb6030122e8
0charts
fa7e00145848118639203037f9de3235
0charts
be5db4d7aa9c1f433344cc5478a98961
0charts
0df66154a7e7ccc3ebeee1ffc5d2e10f
0charts
7a406ffebd604a796feef3614f2c43d2
0charts
cad7d17b6ad1f3832fbe11ed9718ce4a
0charts
69555e34e29e6fb94eb4e3278b524f86
0charts
eba5483ba55ea1bb51b11341b8143a18
0charts
a7710affc1750ec5150985f316f1951a
0charts
27c41a612feffeb7dffb2d718f3f224b
0charts
7e5cba84221a607c7d4d223207ed0404
0charts
3dce9c327dea9545efca2ed63339092f
0charts
4f3b5018982c5ed7430bc47ed0c8e623
0charts
408458cc67f9234b038f3999f7b60425
0charts
5c1dce943608d27593d3cef9526afcbf
0charts
1756979bb30c4f2584e3c1fbca14cbe1
0charts
eec4f04c9bbdbdc4d05f1d1f90c94970
0charts
9a3f55f1d92700487f3c5c70ad9911de
0charts
bced5b662ac8b76af773087e1a88f31f
0charts
dceeaa77b22d7e79ba6dad2ad540c7ef
0charts
8d74524affb4f2d79192e4d172c9e576
0charts
462ffce00cd5842fd7e857a5b5b6ac50
0charts
6b6691b5c1fa16113610c308247eb4bd
0charts
db002b418f38f3c97cc7798fa6072fa3
0charts
dac41ea8f4369697be3047fa29ccd3f9
0charts
b61cfc8c631d112138ed82c378bc4e30
0charts
42acd2567f339a9f2c4cd409c371cf76
0charts
0098513880220f5cccc5b3d00d33fe64
0charts
a77b604335dd9a1da0ed5dbc7cba3d84
0charts
3504d487828cb080c46a865ecb1b0b33
0charts
43c338328b864911e4947d44b0000341
0charts
f8d404ebd8ad636b3152d57d3cdf8f73
0charts
ea4944c5aae8279006363b67b046fa55
0charts
984fd029931abfbcd69a962eee48437a
0charts
88c1d005d815a8085bf0ae5a94e91ca5
0charts
2a14b545278181e4b905e55b3d611a6c
0charts
44564caff25729c5b5ebe92ce9155350
0charts
b031bc6ac5a536855ee7cc2db39107a8
0charts
e12b97bb1957d6dd038b66d40a900a23
0charts
a5c34594d5620bd47f150c2c097f77dd
0charts
13af8036610de80c6b2cc1dfbf2a8f11
0charts
70b29c687d2ea180264fdd850f180507
0charts
3f7459e54daefb9166b07dc79ade332f
0charts
dc99ae15232f594bad4978b03d503921
0charts
eb101522adbf5876ee8727d31dbc3ca0
0charts
2a70aac2e4260fb43d0c5208f137f557
0charts
7a3295430d54e8c9a3470912c58bafd5
0charts
0b32ea2d3756bd168202f072a97f118a
0charts
9ffadb0ff058c8280653b2c081d2f332
0charts
2f6bed72addbf10532a8ccfdc5a1cbfe
0charts
4bb9bd57feaf42dd923b0582ea700e29
0charts
234a8196308d36ac5279dfb169abbacd
0charts
038dc43ad386001736eeb8588c19092f
0charts
2b9f1425c9eb50c8c3a2d1e168130bc7
0charts
c4f7667e0f5583825f596d132cc686b8
0charts
08d6fe425f3b5f4ec6541b928acc17bf
0charts
29d49cc2929bd1b58a0ae14e4732e9c1
0charts
b7cdb66bbc7a4030499de5b6917d31e2
0charts
05d472732763a7a779429453f55794aa
0charts
3de153475e716ffac173c3a7299a89fc
0charts
696f573daa9b1010be9df6e5f24b5197
0charts
a110ecc0ea4963e6759c794941d4d015
0charts
9b1893eea035ef5af9bbee0ae90464d8
0charts
6b43a1d1efd63d51839dde62c5e36e54
0charts
892a64c6dd3040a36e7f34f2173d22a1
0charts
a4e78396e9d5811b006b4ea0cd64f47b
0charts
e292f6694de57db1c03111d6b527648e
0charts
9559fc953fbb1e5f6748440ca0412e33
0charts
bda02b23f6103385a59b4d56b6210413
0charts
0d2e07a3d4ff88e35ff4d57083a2695f

Crypto Charts

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 mainly of images that are cryptocurrency charts. 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 4,880 images in total, categorized into:

  • 4,051 chart images
  • 829 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
221

Models trained or fine-tuned on StephanAkkerman/crypto-charts