COYO-Labeled-300M is a dataset of machine-labeled 300M image-multi-label pairs. We labeled subset of COYO-700M with a large model (efficientnetv2-xl) trained on imagenet-21k. We followed the same evaluation pipeline as in efficientnet-v2. The labels are top 50 most likely labels out of 21,841 classes from imagenet-21k. The label probabilies are also provided with labels so that the user can select threshold of their choice for multi-label classification use or can take top-1 class for single class classification use.
In other words, COYO-Labeled-300M is a ImageNet-like dataset. Instead of human labeled 1.25 million samples, it's machine-labeled 300 million samples. This dataset is similar to JFT-300M which is not released to the public.
We found that our ViT implementation trained on COYO-Labeled-300M performs similar to the performance numbers in the ViT paper trained on JFT-300M.
We also provide weights for the pretrained ViT model on COYO-Labeled-300M as well as its training & fine-tuning code.
The basic instruction, licenses and contributors are the same as for the coyo-700m.
| id | url | imagehash | labels | label_probs | width | height |
|---|---|---|---|---|---|---|
| 315 |  |
daf5a50aae4aa54a | [8087, 11054, 808... | [0.4453125, 0.304... | 240 | 240 |
| 463 | 8f3fd06161c41e3e | [12213, 12215, 12... | [0.71728516, 0.38... | 219 | 230 | |
| 601 |  |
8e70855e6ba3f825 | [4945, 4953, 4942... | [0.8496094, 0.069... | 320 | 240 |
| 1105 |  |
ea90d5978594cbc6 | [12352, 12658, 12... | [0.2763672, 0.267... | 310 | 310 |
| 2308 |  |
c0cf983c7c7074f4 | [6728, 14101, 158... | [0.32128906, 0.02... | 702 | 336 |
| 3710 | 8f9694594b26371b | [15721, 15722, 49... | [0.4152832, 0.175... | 600 | 467 |
| name | type | description |
|---|---|---|
| id | long | Unique 64-bit integer ID generated by monotonically_increasing_id() which is the same value that is mapped with the existing COYO-700M. |
| url | string | The image URL extracted from the src attribute of the <img> |
| imagehash | string | The perceptual hash(pHash) of the image |
| labels | sequence[integer] | Inference results of EfficientNetV2-XL model trained on ImageNet-21K dataset (Top 50 indices among 21,841 classes) |
| label_probs | sequence[float] | Inference results of EfficientNetV2-XL model trained on ImageNet-21K dataset (Top 50 indices among 21,841 probabilites) |
| width | integer | The width of the image |
| height | integer | The height of the image |
- Statistics for threshold-based label distribution
| Threshold | Labels per Image | Unique Labels | Sampling Ratio |
|---|---|---|---|
| 0.00 | 50.00 | 21,841 | 100.00% |
| 0.05 | 3.16 | 18,922 | 96.69% |
| 0.10 | 1.98 | 18,471 | 85.83% |
| 0.15 | 1.58 | 18,122 | 74.49% |
| 0.20 | 1.37 | 17,781 | 64.30% |
| 0.25 | 1.26 | 17,479 | 55.60% |
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Image Size
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Top1 Label Distribution
- You can download the dataset from Huggingface Dataset
- For more information on downloading the image dataset, please refer to download/README.md.