ONNX Model Zoo

deepghs/eattach_monochrome_experiments

By deepghs · ↓ 0 · ♥ 0 · Updated 2025-03-20

What's in this repo

This page lists the files in deepghs/eattach_monochrome_experiments, including model.onnx and more.

  • 4 ONNX models (.onnx files)
  • 46 files, including 24 .png, 8 .json, 4 .0, 4 .onnx, 4 .safetensors, 1 .md
  • Last updated 2025-03-20

ONNX files

File Size Download
mlp_layer1_seed1/model.onnx — Download
mlp_layer1_seed114514_dist8/model.onnx — Download
mlp_layer1_seed3_dist8/model.onnx — Download
mlp_layer1_seedx_dist8/model.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

This is an experiment on DeepGHS's new tagger attachment training framework deepghs/emb_attachments.

Just some technical validation, not recommended for use in production.

(2025.3.7: This feature is still WIP, you can take a look at this PR and this documentation, you will like it.)

Name Encoder Problem FLOPS Params AUC accuracy loss mAP Labels
mlp_layer1_seed114514_dist8 wdtagger:SmilingWolf/wd-swinv2-tagger-v3 classification 525.3K 525.8K 0.9937 0.9743 0.04483 0.9937 monochrome, normal
mlp_layer1_seedx_dist8 wdtagger:SmilingWolf/wd-swinv2-tagger-v3 classification 525.3K 525.8K 0.9946 0.9732 0.04058 0.9947 monochrome, normal
mlp_layer1_seed3_dist8 wdtagger:SmilingWolf/wd-swinv2-tagger-v3 classification 525.3K 525.8K 0.9956 0.9699 0.05301 0.9957 monochrome, normal
mlp_layer1_seed1 wdtagger:SmilingWolf/wd-swinv2-tagger-v3 classification 525.3K 525.8K 0.9948 0.9652 0.06904 0.995 monochrome, normal

Model info

image-classification dghs-imgutils
dghs-imgutils tensorboard onnx safetensors art emb_attachments wdtaggers image-classification dataset:deepghs/monochrome_danbooru license:mit region:us
↓ 0 ♥ 0

Created: 2025-03-07

Updated: 2025-03-20

View on Hugging Face

Files (46)

.gitattributes
README.md
mlp_layer1_seed1/events.out.tfevents.1741272460.77cc9058c7a7b07cddf3cf65aa1202cb5e26bda5001d182067e0d90f.661707.0
mlp_layer1_seed1/meta.json
mlp_layer1_seed1/metrics.json
mlp_layer1_seed1/model.onnx ONNX
mlp_layer1_seed1/model.safetensors
mlp_layer1_seed1/plot_confusion.png
mlp_layer1_seed1/plot_f1_curve.png
mlp_layer1_seed1/plot_p_curve.png
mlp_layer1_seed1/plot_pr_curve.png
mlp_layer1_seed1/plot_r_curve.png
mlp_layer1_seed1/plot_roc_curve.png
mlp_layer1_seed114514_dist8/events.out.tfevents.1742473121.197caa7ba88141226195c99eeafb8490b47d74629daea96443dcc28f.263082.0
mlp_layer1_seed114514_dist8/meta.json
mlp_layer1_seed114514_dist8/metrics.json
mlp_layer1_seed114514_dist8/model.onnx ONNX
mlp_layer1_seed114514_dist8/model.safetensors
mlp_layer1_seed114514_dist8/plot_confusion.png
mlp_layer1_seed114514_dist8/plot_f1_curve.png
mlp_layer1_seed114514_dist8/plot_p_curve.png
mlp_layer1_seed114514_dist8/plot_pr_curve.png
mlp_layer1_seed114514_dist8/plot_r_curve.png
mlp_layer1_seed114514_dist8/plot_roc_curve.png
mlp_layer1_seed3_dist8/events.out.tfevents.1742459370.197caa7ba88141226195c99eeafb8490b47d74629daea96443dcc28f.35842.0
mlp_layer1_seed3_dist8/meta.json
mlp_layer1_seed3_dist8/metrics.json
mlp_layer1_seed3_dist8/model.onnx ONNX
mlp_layer1_seed3_dist8/model.safetensors
mlp_layer1_seed3_dist8/plot_confusion.png
mlp_layer1_seed3_dist8/plot_f1_curve.png
mlp_layer1_seed3_dist8/plot_p_curve.png
mlp_layer1_seed3_dist8/plot_pr_curve.png
mlp_layer1_seed3_dist8/plot_r_curve.png
mlp_layer1_seed3_dist8/plot_roc_curve.png
mlp_layer1_seedx_dist8/events.out.tfevents.1742461859.197caa7ba88141226195c99eeafb8490b47d74629daea96443dcc28f.120820.0
mlp_layer1_seedx_dist8/meta.json
mlp_layer1_seedx_dist8/metrics.json
mlp_layer1_seedx_dist8/model.onnx ONNX
mlp_layer1_seedx_dist8/model.safetensors
mlp_layer1_seedx_dist8/plot_confusion.png
mlp_layer1_seedx_dist8/plot_f1_curve.png
mlp_layer1_seedx_dist8/plot_p_curve.png
mlp_layer1_seedx_dist8/plot_pr_curve.png
mlp_layer1_seedx_dist8/plot_r_curve.png
mlp_layer1_seedx_dist8/plot_roc_curve.png

Related models