b5de52c4cd
torch.cuda.amp bug fix ( #2750 )
PR https://github.com/ultralytics/yolov5/pull/2725 introduced a very specific bug that only affects multi-GPU trainings. Apparently the cause was using the torch.cuda.amp decorator in the autoShape forward method. I've implemented amp more traditionally in this PR, and the bug is resolved.
3 lat temu
fca5e2a48f
autocast enable=torch.cuda.is_available() ( #2748 )
3 lat temu
c03d590320
Add Hub results.pandas() method ( #2725 )
* Add Hub results.pandas() method
New method converts results from torch tensors to pandas DataFrames with column names.
This PR may partially resolve issue https://github.com/ultralytics/yolov5/issues/2703
```python
results = model(imgs)
print(results.pandas().xyxy[0])
xmin ymin xmax ymax confidence class name
0 57.068970 391.770599 241.383545 905.797852 0.868964 0 person
1 667.661255 399.303589 810.000000 881.396667 0.851888 0 person
2 222.878387 414.774231 343.804474 857.825073 0.838376 0 person
3 4.205386 234.447678 803.739136 750.023376 0.658006 5 bus
4 0.000000 550.596008 76.681190 878.669922 0.450596 0 person
```
* Update comments
torch example input now shown resized to size=640 and also now a multiple of P6 stride 64 (see https://github.com/ultralytics/yolov5/issues/2722#issuecomment-814785930 )
* apply decorators
* PEP8
* Update common.py
* pd.options.display.max_columns = 10
* Update common.py
3 lat temu
ec8979f1d2
Updated filename attributes for YOLOv5 Hub BytesIO ( #2718 )
Fix 2 for 'Model predict with forward will fail if PIL image does not have filename attribute' #2702
3 lat temu
74276d5189
Updated filename attributes for YOLOv5 Hub results ( #2708 )
Proposed fix for 'Model predict with forward will fail if PIL image does not have filename attribute' #2702
3 lat temu
17300a4c7b
autoShape forward im = np.asarray(im) # to numpy ( #2689 )
Slight speedup.
3 lat temu
2af059c0d8
PyTorch Hub model.save() increment as runs/hub/exp ( #2684 )
* PyTorch Hub model.save() increment as runs/hub/exp
This chane will align PyTorch Hub results saving with the existing unified results saving directory structure of
runs/
/train
/detect
/test
/hub
/exp
/exp2
...
* cleanup
3 lat temu
1148e2ea63
Add TransformerLayer, TransformerBlock, C3TR modules ( #2333 )
* yolotr
* transformer block
* Remove bias in Transformer
* Remove C3T
* Remove a deprecated class
* put the 2nd LayerNorm into the 2nd residual block
* move example model to models/hub, rename to -transformer
* Add module comments and TODOs
* Remove LN in Transformer
* Add comments for Transformer
* Solve the problem of MA with DDP
* cleanup
* cleanup find_unused_parameters
* PEP8 reformat
Co-authored-by: DingYiwei <846414640@qq.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
3 lat temu
2bf34f50fd
PyTorch Hub amp.autocast() inference ( #2641 )
I think this should help speed up CUDA inference, as currently models may be running in FP32 inference mode on CUDA devices unnecesarily.
3 lat temu
005d7a8c54
Update Detections() self.n comment ( #2620 )
```python
self.n = len(self.pred) # number of images (batch size)
```
3 lat temu
a57f23d18b
Update detections() self.t = tuple() ( #2617 )
* Update detections() self.t = tuple()
Fix multiple results.print() bug.
* Update experimental.py
* Update yolo.py
3 lat temu
1c132a1f94
Update Detections() times=None ( #2570 )
Fix for results.tolist() method breaking after YOLOv5 Hub profiling PRshttps://github.com/ultralytics/yolov5/pull/2460 https://github.com/ultralytics/yolov5/pull/2459 and
3 lat temu
e8fc97aa38
Improved W&B integration ( #2125 )
* Init Commit
* new wandb integration
* Update
* Use data_dict in test
* Updates
* Update: scope of log_img
* Update: scope of log_img
* Update
* Update: Fix logging conditions
* Add tqdm bar, support for .txt dataset format
* Improve Result table Logger
* Init Commit
* new wandb integration
* Update
* Use data_dict in test
* Updates
* Update: scope of log_img
* Update: scope of log_img
* Update
* Update: Fix logging conditions
* Add tqdm bar, support for .txt dataset format
* Improve Result table Logger
* Add dataset creation in training script
* Change scope: self.wandb_run
* Add wandb-artifact:// natively
you can now use --resume with wandb run links
* Add suuport for logging dataset while training
* Cleanup
* Fix: Merge conflict
* Fix: CI tests
* Automatically use wandb config
* Fix: Resume
* Fix: CI
* Enhance: Using val_table
* More resume enhancement
* FIX : CI
* Add alias
* Get useful opt config data
* train.py cleanup
* Cleanup train.py
* more cleanup
* Cleanup| CI fix
* Reformat using PEP8
* FIX:CI
* rebase
* remove uneccesary changes
* remove uneccesary changes
* remove uneccesary changes
* remove unecessary chage from test.py
* FIX: resume from local checkpoint
* FIX:resume
* FIX:resume
* Reformat
* Performance improvement
* Fix local resume
* Fix local resume
* FIX:CI
* Fix: CI
* Imporve image logging
* (:(:Redo CI tests:):)
* Remember epochs when resuming
* Remember epochs when resuming
* Update DDP location
Potential fix for #2405
* PEP8 reformat
* 0.25 confidence threshold
* reset train.py plots syntax to previous
* reset epochs completed syntax to previous
* reset space to previous
* remove brackets
* reset comment to previous
* Update: is_coco check, remove unused code
* Remove redundant print statement
* Remove wandb imports
* remove dsviz logger from test.py
* Remove redundant change from test.py
* remove redundant changes from train.py
* reformat and improvements
* Fix typo
* Add tqdm tqdm progress when scanning files, naming improvements
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
3 lat temu
f813f6dcc8
autoShape() speed profiling update ( #2460 )
3 lat temu
569757ecc0
Add autoShape() speed profiling ( #2459 )
* Add autoShape() speed profiling
* Update common.py
* Create README.md
* Update hubconf.py
* cleanuip
3 lat temu
c09964c27c
Update inference default to multi_label=False ( #2252 )
* Update inference default to multi_label=False
* bug fix
* Update plots.py
* Update plots.py
3 lat temu
5f42643a53
Unified hub and detect.py box and labels plotting ( #2243 )
3 lat temu
db28ce61ac
Improved hubconf.py CI tests ( #2251 )
3 lat temu
9d87307784
YOLOv5 Hub URL inference bug fix ( #2250 )
* Update common.py
* Update common.py
* Update common.py
3 lat temu
3e560e2fae
YOLOv5 PyTorch Hub results.save() method retains filenames ( #2194 )
* save results with name
* debug
* save original imgs names
* Update common.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
3 lat temu
404749a33c
PyTorch Hub results.save('path/to/dir') ( #2179 )
3 lat temu
2fc4760257
Update autoshape .print() and .save() ( #2022 )
3 lat temu
aac33f87ad
Update inference multiple-counting ( #2019 )
* Update inference multiple-counting
* update github check
3 lat temu
1d1c0567a4
PyTorch Hub results.render() ( #1897 )
3 lat temu
69be8e738f
YOLOv5 v4.0 Release ( #1837 )
* Update C3 module
* Update C3 module
* Update C3 module
* Update C3 module
* update
* update
* update
* update
* update
* update
* update
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* updates
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* update datasets
* update
* update
* update
* update attempt_downlaod()
* merge
* merge
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* parameterize eps
* comments
* gs-multiple
* update
* max_nms implemented
* Create one_cycle() function
* update
* update
* update
* update
* update
* update
* update
* update study.png
* update study.png
* Update datasets.py
3 lat temu
14b0abe2d0
autoShape() default for PyTorch Hub models ( #1692 )
* Add autoshape parameter
* Remove autoshape call in ReadMe
* Update hubconf.py
* file/URI inputs and autoshape check passthrough
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
3 lat temu
799724108f
Update C3 module ( #1705 )
3 lat temu
fa8f1fb0e9
Simplify autoshape() post-process ( #1653 )
* Simplify autoshape() post-process
* cleanup
* cleanup
3 lat temu
ba48f867ea
Add bias to Classify() ( #1601 )
3 lat temu
199c9c7874
Detection() device bug fix ( #1455 )
4 lat temu
225845e781
Update test.py for IoU in native image-space ( #1439 )
* Update test.py for IoU in native image-space
* remove redundant
* gn to device
* remove output scale_coords
* --img-size correction
* update
* native-space labels
* pred to predn
* remove clip_coords()
4 lat temu
f5429260ca
PyTorch Hub and autoShape update ( #1415 )
* PyTorch Hub and autoShape update
* comment x for imgs
* reduce comment
4 lat temu
15a10609fe
autoshape() update for PIL greyscale inputs ( #1279 )
* autoshape update for PIL greyscale inputs
* autoshape update for PIL greyscale inputs
4 lat temu
3b57cb5641
Simplified inference ( #1153 )
4 lat temu
c4cb78570c
add NMS to pretrained pytorch hub models
4 lat temu
916d4aad9a
v3.0 Release ( #725 )
* initial commit
* remove yolov3-spp from test.py study
* update study --img range
* update mAP
* cleanup and speed updates
* update README plot
4 lat temu
d5b6416c87
Explicit Imports ( #498 )
* expand imports
* optimize
* miss
* fix
4 lat temu
a97c3f94ec
update common.py Classify()
4 lat temu
5387d4747d
update common.py add Classify()
4 lat temu
1fca7a7f24
autopad() update in common.py
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b5659d1195
module updates
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e071b8dd3a
update models/common.py for Conv() flexible padding
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a814720403
PyTorch Hub updates
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ff02ae0869
module updates
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12b0c046d5
model fusion and onnx export
4 lat temu
1b97392801
updates
4 lat temu
1e84a23f38
initial commit
4 lat temu
Glenn Jocher
Ding Yiwei
Ayush Chaurasia
Yann Defretin
Daniel Khromov
NanoCode012
Jirka Borovec