Comments (3)
The conversion is actually using my keras_reload_from_torch_model. You can find some model conversion code here including beit 08-20_pytorch_to_keras.md#beit. Generally, it's define model architecture
-> covert and load model weights
. If you just wanna a fast try, may consider tools like onnx-tensorflow or onnx2tflite.
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Thanks!
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Uh, sorry, I didn't expect beitv2 is exactly same with beit, and got lazy when thinking may need to read their code... beit_v2_base_patch16_224.h5
is uploaded in beit_pretrained. Others are also in the way, including eva ones. May soon be available just depending on my network speed...
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Related Issues (20)
- assigning class weight to each class during training and access to the each class map and recall HOT 5
- EdgeNeXt-Base Model HOT 2
- EfficientVit-B0 ImageNet 224 Weights Released HOT 2
- Convnextv2 outputs nan when training in fp16 HOT 7
- Extraction of heatmaps from attention layers. HOT 1
- Super-resolution using transformes HOT 2
- RepViT Models HOT 2
- Efficientnet EdgeTpu Models HOT 1
- Training custom model HOT 10
- Converting EfficientFormer into tflite doesn't work HOT 11
- EfficientVit-L Models HOT 3
- Amazing HOT 3
- feature extractor HOT 2
- will you have models for Fastsam and Mobilesam in the future? HOT 3
- coco_eval_script script with yolov7_w6_coco.h5 leads to low AP HOT 3
- CSPNeXt HOT 5
- PyTorch backend doesn't work in Kaggle notebook HOT 4
- Request To Add FlashInternImage Models HOT 3
- CLIP Testing Script HOT 2
- TFLite conversion for llama2 tiny models HOT 2
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