# Image Modeling on ImageNet 64x64 (train) Source: [Image Modeling on ImageNet 64x64 (train)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:53:17.209Z Evaluation of Image Modeling performance (density estimation or likelihood) on the training split of the ImageNet 64x64 dataset, typically measured using Negative Log-Likelihood (NLL). A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Image Modeling | | Dataset (including any stated split) | ImageNet 64x64 (train) | | Metric | NLL (bits/dim) | | Unit | SCALAR | | Direction | Lower is better | | Results loaded | 1 of 1 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [NLL (bits/dim)](): Lower is better; unit: SCALAR. ## Recorded results | Method | NLL (bits/dim) | Result date | Report paper | | --- | --- | --- | --- | | PixelRNN | 3.57 | Not recorded | 68fa62b81e209131ce9fad7e | ### PixelRNN NLL (bits/dim): 3.57. Result ID: 698be0b4ecace47ef5598b9a. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Resolution":"64x64","Data Augmentation":false}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.