To address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this st...

New model additions Gemma-2 The Gemma2 model was proposed in Gemma2: Open Models Based on Gemini Technology and Research by Gemma2 Team, Google. Gemma2 models are trained on 6T tokens, and released...

I did some quick research at the time, but couldn’t find any useful information. It seems that those parameters already existed in RT-DETR (not v2), but I couldn’t figure out why they were implemented. github.com/huggingface/transformers #### New model support RTDETR `main` ← `SangbumChoi:rtdetr` opened 08:34AM - 17 Feb 24 UTC SangbumChoi +6892 -9 # What does this PR do? This is the new model for RTDETR that is complete ver…sion from https://github.com/huggingface/transformers/pull/27247 There are several TO DOs - X] reslove conflicts - X] weight files for other 7 RTDETR - X] Edit testing script - X] (optional) enable training ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [X] Did you read the [contributor guideline, Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum? Please add a link to it if that's the case. - [X] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines, and [here are tips on formatting docstrings. - [X] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @amyeroberts @NielsRogge github.com ### GitHub - lyuwenyu/RT-DETR: [CVPR 2024] Official RT-DETR (RTDETR paddle... [CVPR 2024] Official RT-DETR (RTDETR paddle pytorch), Real-Time DEtection TRansformer, DETRs Beat YOLOs on Real-time Object Detection. 🔥 🔥 🔥

There may be some problems, but it looks like it will work. stackoverflow.com #### How can I convert a YOLOv8s model to coreML model using a custom dataset? **python, swift, object-detection, coreml** asked by Dhiman Das on 08:06PM - 15 Mar 23 UTC github.com/ultralytics/ultralytics #### Converting RTDETR to coreml opened 01:51PM - 13 Mar 24 UTC closed 12:16AM - 24 Apr 24 UTC LetifT bug Stale ### Search before asking - X] I have searched the YOLOv8 [issuesub.com/ultralytics/ultralytics/issues) and found no similar bug report. ### YOLOv8 Component Export ### Bug from ultralytics import RTDETR model = RTDETR('RTDETR.pt') model.export(format="coreml") when exporting a custom trained RT-DETR model i get the following error: Ultralytics YOLOv8.1.19 🚀 Python-3.10.13 torch-1.12.0+cu102 CPU (AMD EPYC 7282 16-Core Processor) rt-detr-l summary: 498 layers, 31985795 parameters, 0 gradients, 103.4 GFLOPs PyTorch: starting from 'RTDETR.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 300, 5) (63.1 MB) CoreML: starting export with coremltools 6.2... Converting PyTorch Frontend ==> MIL Ops: 18%|▏| 417/2352 [00:00<00:00, 4165.75 Saving value type of int64 into a builtin type of int32, might lose precision! Saving value type of int64 into a builtin type of int32, might lose precision! Saving value type of float64 into a builtin type of fp32, might lose precision! Converting PyTorch Frontend ==> MIL Ops: 25%|▏| 586/2352 [00:00<00:00, 3093.35 CoreML: export failure ❌ 21.4s: In op, of type linear, named out_w, the named input `weight` must have the same data type as the named input `x`. However, weight has dtype fp32 whereas x has dtype int32. Traceback (most recent call last): File "/opt/conda/bin/yolo", line 8, in <module> sys.exit(entrypoint()) File "/usr/src/ultralytics/ultralytics/cfg/__init__.py", line 568, in entrypoint getattr(model, mode)(**overrides) # default args from model File "/usr/src/ultralytics/ultralytics/engine/model.py", line 577, in export return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model) File "/opt/conda/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/usr/src/ultralytics/ultralytics/engine/exporter.py", line 281, in __call__ f[4], _ = self.export_coreml() File "/usr/src/ultralytics/ultralytics/engine/exporter.py", line 136, in outer_func raise e File "/usr/src/ultralytics/ultralytics/engine/exporter.py", line 131, in outer_func f, model = inner_func(*args, **kwargs) File "/usr/src/ultralytics/ultralytics/engine/exporter.py", line 594, in export_coreml ct_model = ct.convert( File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/_converters_entry.py", line 444, in convert mlmodel = mil_convert( File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 187, in mil_convert return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 211, in _mil_convert proto, mil_program = mil_convert_to_proto( File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 281, in mil_convert_to_proto prog = frontend_converter(model, **kwargs) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/converter.py", line 109, in __call__ return load(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 57, in load return _perform_torch_convert(converter, debug) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 96, in _perform_torch_convert prog = converter.convert() File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 281, in convert convert_nodes(self.context, self.graph) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 89, in convert_nodes add_op(context, node) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 749, in matmul res = mb.linear(x=inputs[0], weight=_np.transpose(inputs[1].val), name=node.name) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/mil/ops/registry.py", line 176, in add_op return cls._add_op(op_cls_to_add, **kwargs) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/mil/builder.py", line 166, in _add_op new_op = op_cls(**kwargs) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/mil/operation.py", line 187, in __init__ self._validate_and_set_inputs(input_kv) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/mil/operation.py", line 503, in _validate_and_set_inputs self.input_spec.validate_inputs(self.name, self.op_type, input_kvs) File "/opt/conda/lib/python3.10/site-packages/coremltools/converters/mil/mil/input_type.py", line 137, in validate_inputs raise ValueError(msg) ValueError: In op, of type linear, named out_w, the named input `weight` must have the same data type as the named input `x`. However, weight has dtype fp32 whereas x has dtype int32. This error is consistent when using either the coreml or mlmodel format and across different pytorch versions. ### Environment Ultralytics YOLOv8.1.19 🚀 Python-3.10.13 torch-1.12.0+cu102 CPU (AMD EPYC 7282 16-Core Processor) Setup complete ✅ (32 CPUs, 251.6 GB RAM, 304.2/313.0 GB disk) OS Linux-5.4.0-170-generic-x86_64-with-glibc2.35 Environment Docker Python 3.10.13 Install git RAM 251.57 GB CPU AMD EPYC 7282 16-Core Processor CUDA None matplotlib ✅ 3.8.3>=3.3.0 opencv-python ✅ 4.9.0.80>=4.6.0 pillow ✅ 10.0.1>=7.1.2 pyyaml ✅ 6.0.1>=5.3.1 requests ✅ 2.31.0>=2.23.0 scipy ✅ 1.12.0>=1.4.1 torch ✅ 1.12.0>=1.8.0 torchvision ✅ 0.13.0>=0.9.0 tqdm ✅ 4.65.0>=4.64.0 psutil ✅ 5.9.0 py-cpuinfo ✅ 9.0.0 thop ✅ 0.1.1-2209072238>=0.1.1 pandas ✅ 2.2.1>=1.1.4 seaborn ✅ 0.13.2>=0.11.0 error was also present when using a fresh ultralytics install in Google Colab ### Minimal Reproducible Example from ultralytics import RTDETR model = RTDETR('RTDETR.pt') model.export(format="coreml") ### Additional _No response_ ### Are you willing to submit a PR? - [x] Yes I'd like to help by submitting a PR!