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@mergennachin mergennachin commented Jul 11, 2025

Add next set of GA models

Summary:

On top of #12340

Add a few more tasks:

  1. Image-Text Understanding (OpenCLIP)
  2. Semantic Text Search (Sentence Transformers)
  3. Document Q&A (DistilBERT QA)
  4. Super resolution (Real-ESRGAN)
  5. Audio Classification (AST)
  6. Text Sentiment Analysis (RoBERTa)
  7. Depth estimation (Depth Anything 2)

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pytorch-bot bot commented Jul 11, 2025

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/12398

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❌ 1 New Failure, 1 Cancelled Job, 7 Unrelated Failures

As of commit 0ad47d3 with merge base 7c300e7 (image):

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👉 Rebase onto the `viable/strict` branch to avoid these failures

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@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Jul 11, 2025
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@mergennachin mergennachin changed the title Add new GA models (final) Add new GA models (next set) Jul 11, 2025
@mergennachin mergennachin force-pushed the add_new_models_2 branch 2 times, most recently from fbd8064 to 87a2817 Compare July 11, 2025 19:25
Added 8 new representative models to ExecutorTorch examples:

EfficientNet-B4: Image classification with CNN architecture
DETR-ResNet50: Object detection using transformer decoder
SegFormer-ADE: Semantic segmentation transformer
Swin2SR: Super-resolution with Swin transformer
ALBERT: Lightweight BERT for NLP tasks
TrOCR: Optical character recognition transformer
Wav2Vec2: Cross-lingual speech representation learning
All models include XNNPACK backend support with appropriate quantization configurations and full CI integration.

Test plan:

Validate model export and execution with portable backend
Test XNNPACK delegation and quantization (with appropriate exclusions)
Integrate into CI workflows for automated testing
Verify all models perform their intended tasks accurately
Summary:

Add a few more tasks:

1. Image-Text Understanding (OpenCLIP)
2. Semantic Text Search (Sentence Transformers)
3. Document Q&A (DistilBERT QA)
4. Practical Image Enhancement (Real-ESRGAN)
5. Audio Classification (AST)
6. Text Sentiment Analysis (RoBERTa)
7. Depth estimation (Depth Anything 2)
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@mergennachin - As I was going through this list, I realized most of them are from HF transformers. So,
(1) if we should continue adding more HF transformer based models on optimum-et, instead of here.
(2) As I looked on optimum-et[1], I saw many already there i.e. Albert, DistillBert, EfficientNet, Roberta, Swin. Even if we don't do (1) - we should deduplicate unless you have preference for keeping them in ET CI for some reason.

[1] https://github.com/huggingface/optimum-executorch/tree/main/tests/models

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@github-actions github-actions bot added the stale PRs inactive for over 60 days label Sep 17, 2025
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