glenn-jocher commented 4 years ago

📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub https://pytorch.org/hub/ultralytics_yolov5. See YOLOv5 Docs for additional details. UPDATED 26 March 2023.

Before You Start

Install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7. Models and datasets download automatically from the latest YOLOv5 release.

pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt

💡 ProTip: Cloning https://github.com/ultralytics/yolov5 is not required 😃

Load YOLOv5 with PyTorch Hub

Simple Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes an image for inference. 'yolov5s' is the lightest and fastest YOLOv5 model. For details on all available models please see the README.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Image
im = 'https://ultralytics.com/images/zidane.jpg'

# Inference
results = model(im)

results.pandas().xyxy[0]
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

Detailed Example

This example shows batched inference with PIL and OpenCV image sources. results can be printed to console, saved to runs/hub, showed to screen on supported environments, and returned as tensors or pandas dataframes.

import cv2
import torch
from PIL import Image

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Images
for f in 'zidane.jpg', 'bus.jpg':
    torch.hub.download_url_to_file('https://ultralytics.com/images/' + f, f)  # download 2 images
im1 = Image.open('zidane.jpg')  # PIL image
im2 = cv2.imread('bus.jpg')[..., ::-1]  # OpenCV image (BGR to RGB)

# Inference
results = model([im1, im2], size=640) # batch of images

# Results
results.print()  
results.save()  # or .show()

results.xyxy[0]  # im1 predictions (tensor)
results.pandas().xyxy[0]  # im1 predictions (pandas)
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

For all inference options see YOLOv5 AutoShape() forward method: https://github.com/ultralytics/yolov5/blob/30e4c4f09297b67afedf8b2bcd851833ddc9dead/models/common.py#L243-L252

Inference Settings

YOLOv5 models contain various inference attributes such as confidence threshold, IoU threshold, etc. which can be set by:

model.conf = 0.25  # NMS confidence threshold
      iou = 0.45  # NMS IoU threshold
      agnostic = False  # NMS class-agnostic
      multi_label = False  # NMS multiple labels per box
      classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
      max_det = 1000  # maximum number of detections per image
      amp = False  # Automatic Mixed Precision (AMP) inference

results = model(im, size=320)  # custom inference size

Device

Models can be transferred to any device after creation:

model.cpu()  # CPU
model.cuda()  # GPU
model.to(device)  # i.e. device=torch.device(0)

Models can also be created directly on any device:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', device='cpu')  # load on CPU

💡 ProTip: Input images are automatically transferred to the correct model device before inference.

Silence Outputs

Models can be loaded silently with _verbose=False:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', _verbose=False)  # load silently

Input Channels

To load a pretrained YOLOv5s model with 4 input channels rather than the default 3:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', channels=4)

In this case the model will be composed of pretrained weights except for the very first input layer, which is no longer the same shape as the pretrained input layer. The input layer will remain initialized by random weights.

Number of Classes

To load a pretrained YOLOv5s model with 10 output classes rather than the default 80:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', classes=10)

In this case the model will be composed of pretrained weights except for the output layers, which are no longer the same shape as the pretrained output layers. The output layers will remain initialized by random weights.

Force Reload

If you run into problems with the above steps, setting force_reload=True may help by discarding the existing cache and force a fresh download of the latest YOLOv5 version from PyTorch Hub.

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)  # force reload

Screenshot Inference

To run inference on your desktop screen:

import torch
from PIL import ImageGrab

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Image
im = ImageGrab.grab()  # take a screenshot

# Inference
results = model(im)

Multi-GPU Inference

YOLOv5 models can be be loaded to multiple GPUs in parallel with threaded inference:

import torch
import threading

def run(model, im):
  results = model(im)
  results.save()

# Models
model0 = torch.hub.load('ultralytics/yolov5', 'yolov5s', device=0)
model1 = torch.hub.load('ultralytics/yolov5', 'yolov5s', device=1)

# Inference
threading.Thread(target=run, args=[model0, 'https://ultralytics.com/images/zidane.jpg'], daemon=True).start()
threading.Thread(target=run, args=[model1, 'https://ultralytics.com/images/bus.jpg'], daemon=True).start()

Training

To load a YOLOv5 model for training rather than inference, set autoshape=False. To load a model with randomly initialized weights (to train from scratch) use pretrained=False. You must provide your own training script in this case. Alternatively see our YOLOv5 Train Custom Data Tutorial for model training.

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)  # load pretrained
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False, pretrained=False)  # load scratch

Base64 Results

For use with API services. See https://github.com/ultralytics/yolov5/pull/2291 and Flask REST API example for details.

results = model(im)  # inference

results.ims # array of original images (as np array) passed to model for inference
results.render()  # updates results.ims with boxes and labels
for im in results.ims:
    buffered = BytesIO()
    im_base64 = Image.fromarray(im)
    im_base64.save(buffered, format="JPEG")
    print(base64.b64encode(buffered.getvalue()).decode('utf-8'))  # base64 encoded image with results

Cropped Results

Results can be returned and saved as detection crops:

results = model(im)  # inference
crops = results.crop(save=True)  # cropped detections dictionary

Pandas Results

Results can be returned as Pandas DataFrames:

results = model(im)  # inference
results.pandas().xyxy[0]  # Pandas DataFrame

Pandas Output (click to expand)

```python print(results.pandas().xyxy[0]) # xmin ymin xmax ymax confidence class name # 0 749.50 43.50 1148.0 704.5 0.874023 0 person # 1 433.50 433.50 517.5 714.5 0.687988 27 tie # 2 114.75 195.75 1095.0 708.0 0.624512 0 person # 3 986.00 304.00 1028.0 420.0 0.286865 27 tie ```

Sorted Results

Results can be sorted by column, i.e. to sort license plate digit detection left-to-right (x-axis):

results = model(im)  # inference
results.pandas().xyxy[0].sort_values('xmin')  # sorted left-right

Box-Cropped Results

Results can be returned and saved as detection crops:

results = model(im)  # inference
crops = results.crop(save=True)  # cropped detections dictionary

JSON Results

Results can be returned in JSON format once converted to .pandas() dataframes using the .to_json() method. The JSON format can be modified using the orient argument. See pandas .to_json() documentation for details.

results = model(ims)  # inference
results.pandas().xyxy[0].to_json(orient="records")  # JSON img1 predictions

JSON Output (click to expand)

```json [ {"xmin":749.5,"ymin":43.5,"xmax":1148.0,"ymax":704.5,"confidence":0.8740234375,"class":0,"name":"person"}, {"xmin":433.5,"ymin":433.5,"xmax":517.5,"ymax":714.5,"confidence":0.6879882812,"class":27,"name":"tie"}, {"xmin":115.25,"ymin":195.75,"xmax":1096.0,"ymax":708.0,"confidence":0.6254882812,"class":0,"name":"person"}, {"xmin":986.0,"ymin":304.0,"xmax":1028.0,"ymax":420.0,"confidence":0.2873535156,"class":27,"name":"tie"} ] ```

Custom Models

This example loads a custom 20-class VOC-trained YOLOv5s model 'best.pt' with PyTorch Hub.

model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/best.pt')  # local model
model = torch.hub.load('path/to/yolov5', 'custom', path='path/to/best.pt', source='local')  # local repo

TensorRT, ONNX and OpenVINO Models

PyTorch Hub supports inference on most YOLOv5 export formats, including custom trained models. See TFLite, ONNX, CoreML, TensorRT Export tutorial for details on exporting models.

💡 ProTip: TensorRT may be up to 2-5X faster than PyTorch on GPU benchmarks 💡 ProTip: ONNX and OpenVINO may be up to 2-3X faster than PyTorch on CPU benchmarks

model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.pt')  # PyTorch
                                                            'yolov5s.torchscript')  # TorchScript
                                                            'yolov5s.onnx')  # ONNX
                                                            'yolov5s_openvino_model/')  # OpenVINO
                                                            'yolov5s.engine')  # TensorRT
                                                            'yolov5s.mlmodel')  # CoreML (macOS-only)
                                                            'yolov5s.tflite')  # TFLite
                                                            'yolov5s_paddle_model/')  # PaddlePaddle

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Notebooks with free GPU:
Google Cloud Deep Learning VM. See GCP Quickstart Guide
Amazon Deep Learning AMI. See AWS Quickstart Guide
Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

synked16 commented 4 years ago

@glenn-jocher so can i fit a model with it?

MohamedAliRashad commented 4 years ago

Can someone use the training script with this configuration ?

rlalpha commented 3 years ago

Can I ask about the meaning of the output? How can I reconstruct as box prediction results via the output? Thanks

glenn-jocher commented 3 years ago

@rlalpha if want to run inference, put the model in .eval() mode, and select the first output. These are the predictions, which may then be filtered via NMS:

glenn-jocher commented 3 years ago

@rlalpha I've updated pytorch hub functionality now in c4cb78570cfca23d7e0a5aa7b4ee7e8c6b8d998c to automatically append an NMS module to the model when pretrained=True is requested. Anyone using YOLOv5 pretrained pytorch hub models must remove this last layer prior to training now: model.model = model.model[:-1]

Anyone using YOLOv5 pretrained pytorch hub models directly for inference can now replicate the following code to use YOLOv5 without cloning the ultralytics/yolov5 repository. In this example you see the pytorch hub model detect 2 people (class 0) and 1 tie (class 27) in zidane.jpg. Note there is no repo cloned in the workspace. Also note that ideally all inputs to the model should be letterboxed to the nearest 32 multiple. The second best option is to stretch the image up to the next largest 32-multiple as I've done here with PIL resize.

rlalpha commented 3 years ago

@rlalpha I've updated pytorch hub functionality now in c4cb785 to automatically append an NMS module to the model when pretrained=True is requested. Anyone using YOLOv5 pretrained pytorch hub models must remove this last layer prior to training now: model.model = model.model[:-1]

Anyone using YOLOv5 pretrained pytorch hub models directly for inference can now replicate the following code to use YOLOv5 without cloning the ultralytics/yolov5 repository. In this example you see the pytorch hub model detect 2 people (class 0) and 1 tie (class 27) in zidane.jpg. Note there is no repo cloned in the workspace. Also note that ideally all inputs to the model should be letterboxed to the nearest 32 multiple. The second best option is to stretch the image up to the next largest 32-multiple as I've done here with PIL resize.

I got how to do it now. Thank you for rapid reply.

glenn-jocher commented 3 years ago

@rlalpha @justAyaan @MohamedAliRashad this PyTorch Hub tutorial is now updated to reflect the simplified inference improvements in PR #1153. It's very simple now to load any YOLOv5 model from PyTorch Hub and use it directly for inference on PIL, OpenCV, Numpy or PyTorch inputs, including for batched inference. Reshaping and NMS are handled automatically. Example script is shown in above tutorial.

pfeatherstone commented 3 years ago

@glenn-jocher calling model = torch.hub.load('ultralytics/yolov5', 'yolov5l', pretrained=True) throws error:

Using cache found in /home/pf/.cache/torch/hub/ultralytics_yolov5_master
Traceback (most recent call last):
  File "<frozen importlib._bootstrap>", line 971, in _find_and_load
  File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 678, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/home/pf/.cache/torch/hub/ultralytics_yolov5_master/models/yolo.py", line 15, in <module>
    from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
  File "/home/pf/.cache/torch/hub/ultralytics_yolov5_master/models/common.py", line 8, in <module>
    from utils.datasets import letterbox
ModuleNotFoundError: No module named 'utils.datasets'; 'utils' is not a package

Process finished with exit code 1

glenn-jocher commented 3 years ago

@pfeatherstone thanks for the feedback! Can you try with force_reload=True? Without it the cached repo is used, which may be out of date.

import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, force_reload=True)

pfeatherstone commented 3 years ago

Still doesn't work. I get the following errors:

Downloading: "https://github.com/ultralytics/yolov5/archive/master.zip" to /home/pf/.cache/torch/hub/master.zip
Traceback (most recent call last):
  File "<frozen importlib._bootstrap>", line 971, in _find_and_load
  File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 678, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/home/pf/.cache/torch/hub/ultralytics_yolov5_master/models/yolo.py", line 15, in <module>
    from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
  File "/home/pf/.cache/torch/hub/ultralytics_yolov5_master/models/common.py", line 8, in <module>
    from utils.datasets import letterbox
ModuleNotFoundError: No module named 'utils.datasets'; 'utils' is not a package
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
  File "/usr/local/pycharm-2020.2/plugins/python/helpers/pydev/pydevd.py", line 1785, in stoptrace
    debugger.exiting()
  File "/usr/local/pycharm-2020.2/plugins/python/helpers/pydev/pydevd.py", line 1471, in exiting
    sys.stdout.flush()
ValueError: I/O operation on closed file.

Process finished with exit code 1

glenn-jocher commented 3 years ago

@pfeatherstone I've raised a new bug report in https://github.com/ultralytics/yolov5/issues/1181 for your observation. This typically indicates a pip package called utils is installed in your environment, you should pip uninstall utils.

Semihal commented 3 years ago

Hi!

I try load model and apply .to(device), but i receive exception: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

glenn-jocher commented 3 years ago

@Semihal please raise a bug report with reproducible example code. Thank you.

dagap commented 3 years ago

Is there a way to specify the NMS parameters on the pytorch hub model?

glenn-jocher commented 3 years ago

NMS parameters are model.autoshape() attributes. You can modify them to whatever you want. i.e. model.conf = 0.5 before running inference. https://github.com/ultralytics/yolov5/blob/784feae30a1a792cd4e28c5dcb51618e6e5d20b5/models/common.py#L121-L127

p9anand commented 3 years ago

can we pass augment argument in at the time of inference?

glenn-jocher commented 3 years ago

@p9anand see the autoshape forward method for available arguments: https://github.com/ultralytics/yolov5/blob/94a7f55c4e5cca3dfe4de0bd0793173d5b152ec5/models/common.py#L131-L138

glenn-jocher commented 3 years ago

Custom model loading has been simplified now with PyTorch Hub in PR https://github.com/ultralytics/yolov5/pull/1677 🚀

Custom Models

This example loads a custom 20-class VOC-trained YOLOv5s model 'yolov5s_voc_best.pt' with PyTorch Hub.

model = torch.hub.load('ultralytics/yolov5', 'custom', path_or_model='yolov5s_voc_best.pt')
model = model.autoshape()  # for PIL/cv2/np inputs and NMS

EconML commented 3 years ago

Where can I see the code for the results methods offered through pytorch hub? i.e results.print(), results.save(), etc

glenn-jocher commented 3 years ago

@EconML results is a Detections() instance, defined in models/common.py: https://github.com/ultralytics/yolov5/blob/c0ffcdf998aa0e00f1f39e79ee8124b74b53d23f/models/common.py#L190-L191

EconML commented 3 years ago

@glenn-jocher Thank you for your prompt reply, and your tireless efforts!

Lifeng1129 commented 3 years ago

I want to know how show the result in OpenCV cv.imshow

Now these results work well, as follows results.print() # print results to screen results.show() # display results results.save() # save as results1.jpg, results2.jpg... etc.

But I want to know how to cv2.imshow("Results", ????)

glenn-jocher commented 3 years ago

@Lifeng1129 I've heard this request before, so I've created and merged a new PR #1897 to add this capability. To receive this update you'll need to force_reload your pytorch hub cache:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, force_reload=True)

Then you can use the new results.render() method to return you a list of np arrays representing the original images annotated with the predicted bounding boxes. Note that cv2 usage of the images will require a RGB to BGR conversion, i.e.:

results = model(imgs)
im_list = results.render()
cv2.imshow(im_list[0][..., ::-1])  # show image 0 with RGB to BGR conversion

jmanuelnavarro commented 3 years ago

@glenn-jocher , loading the model using torch.hub is a great functionality. Nevertheless, I am trying to deploy the custom trained model in an isolated environment and it's being weird... I sucessfully generate the model using torch.hub, save it and load it again in the same py script (test.py): imagen

However, when I try to load the model from the previously saved file in a second py script (test2.py), it fails: imagen

Both scripts are in the same location: imagen

What's the best way to do this? Thanks in advance

glenn-jocher commented 3 years ago

@jmanuelnavarro hub does not need network connectivity. Load the model with your first method as that's working.

dan0nchik commented 3 years ago

Hello! How can I save results to a folder?

glenn-jocher commented 3 years ago

@dan0nchik that's a good question. There's no capability for this currently. It would be nice to have something like results.save('path/to/dir') right?

glenn-jocher commented 3 years ago

@dan0nchik I've implemented your feature idea in PR #2179. You can now pass a directory to save results to:

results.save()  # save to current directory
results.save('path/to/dir')  # save to specific directory

dan0nchik commented 3 years ago

Great! Thank you very much!

dan0nchik commented 3 years ago

Hello again Can you please add a flag or something to the display function, so original picture names would be saved? For example:

results.save(save_orig_names=True) # save as results_zidane.jpg, results_bus.jpg... etc.

I've tried to implement that, but I couldn't test :(

glenn-jocher commented 3 years ago

@dan0nchik yes that's a good point. I've thought of implementing this by default for use cases that allow it, i.e when a file or url, or PIL object is passed directly to model. For other cases this is not possible, such as when cv2 or torch images are passed in.

If you have some work started down this path perhaps you could submit a PR and I could review there?

glenn-jocher commented 3 years ago

@dan0nchik BTW, the main pytorch hub functionality is done with the autoShape() module that is in common.py, which generates Detections() class results, also in common.py: https://github.com/ultralytics/yolov5/blob/404749a33cc29d119f54b2ce35bf3b33a847a487/models/common.py#L168

You would want to modify this line in particular, i.e. fname = .../self.fnames[i] if self.fnames else .../results{i}.jpg https://github.com/ultralytics/yolov5/blob/404749a33cc29d119f54b2ce35bf3b33a847a487/models/common.py#L263

dan0nchik commented 3 years ago

If you have some work started down this path perhaps you could submit a PR and I could review there?

@glenn-jocher Yes, I've created PR #2194 and implemented that, but didn't test.

kinoute commented 3 years ago

Should the display/save/show function also displays the class names? Right now it only displays the bounding boxes. I can see there was something for that that was coded or started here: https://github.com/ultralytics/yolov5/blob/c0ffcdf998aa0e00f1f39e79ee8124b74b53d23f/models/common.py#L214-L217

glenn-jocher commented 3 years ago

@kinoute yes it should, this is on a long TODO list of ours. If you'd like to contribute this feature feel free to submit a PR! A good example starting point is here: https://github.com/WelkinU/yolov5-fastapi-demo/blob/270a9f6114ce1e54bd047221544178a419eef365/server.py#L71-L89

jalotra commented 3 years ago

Hey @glenn-jocher Thanks for putting this awesome work together. How to do this :

I want to just detect a particular class, for example 0 or human beings. How to do this is there some interface provided ? I see that the autoShape class makes classes = None.
```
class autoShape(nn.Module):
classes = None  # (optional list) filter by class

# Then somewhere in NMS we use 
 nms(self.classes) 
```
Thanks

glenn-jocher commented 3 years ago

@jalotra filter inference by class using the classes attribute. To detect only class 0, persons:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model.classes = [0]  # list of classes to detect

morestart commented 3 years ago

can you give a demo that is use torch hub to load model inference rtsp video? I use LoadStrames class to load a rtsp video

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
dataset = LoadStreams(self.source)

for path, img, im0s, vid_cap in dataset:

    results = model(img, size=640)
    results.show()

but i get this error ValueError: axes don't match array

i think the error due to the LoadStreams's return, the first channel is batch,i see the hub source code is three channel

glenn-jocher commented 3 years ago

@morestart for a fully managed rtsp streaming solution I would use python detect.py --source rtsp://yourstreamhere.

The PyTorch Hub model is a single-batch solution, so you'd have to pair it with a custom streamloader as in your example, except that LoadStreams() builds padded pytorch tensor batches rather than the original image inputs that the hub autoshape models typically handle. The hub model can run inference on torch tensors, however these are assumed to have unknown padding and thus pass through a different inference channel here that skips all postprocessing (i.e. does not return a results object) https://github.com/ultralytics/yolov5/blob/95aefea49374a1fe867794971c76337526a4d6cb/models/common.py#L194-L196

morestart commented 3 years ago

thanks for your reply! so i need change the LoadStreams channel to 3?

glenn-jocher commented 3 years ago

@morestart as I said for a fully managed solution simply use detect.py.

Torch Hub models are intended for integration into your own python projects, they are not intended for use with the detect.py dataloaders.

morestart commented 3 years ago

@glenn-jocher My project is use multi video to detect object, the detect.py is a easy way to use, but the detect.py is too dependent on local files to load model and It's hard to scale. I think use hub to load model is a simple plan and it made my project look better. I will try change the LoadStreams class can use Hub model to inference.

debparth commented 3 years ago

@glenn-jocher How can I pass a confidence threshold when I'm loading the model from PyTorch Hub?

glenn-jocher commented 3 years ago

@debparth see PyTorch Hub Tutorial, it's explained there.

Tutorials

Train Custom Data 🚀 RECOMMENDED
Weights & Biases Logging 🌟 NEW
Multi-GPU Training
PyTorch Hub ⭐ NEW
ONNX and TorchScript Export
Test-Time Augmentation (TTA)
Model Ensembling
Model Pruning/Sparsity
Hyperparameter Evolution
Transfer Learning with Frozen Layers ⭐ NEW
TensorRT Deployment

valerietram88 commented 3 years ago

thanks for your reply! so i need change the LoadStreams channel to 3?

@glenn-jocher My project is use multi video to detect object, the detect.py is a easy way to use, but the detect.py is too dependent on local files to load model and It's hard to scale. I think use hub to load model is a simple plan and it made my project look better. I will try change the LoadStreams class can use Hub model to inference.

Have you changed the LoadStreams class?

morestart commented 3 years ago

@valerietram88 you can use detect.py, but change the load model part to torch hub

bipinkc19 commented 3 years ago

@glenn-jocher How do I know the inferences happening in the torch hub model are happening in GPU.

We sending a list of np arrays, so how do we use GPU for inferences with pytorch hub?

glenn-jocher commented 3 years ago

@bipinkc19 you can send the model to a cuda device using normal pytorch methods: model.to(device).

YOLOv5 PyTorch Hub models automatically move images to the correct device if needed before inference.

ghost commented 3 years ago

Dear @glenn-jocher , How can use yolov3-tiny weights in hub. I'm working on raspberry pi and want a good prediction speed and I think using tiny version I can achieve the optimal FPS. Thank you. Regards, Asim

glenn-jocher commented 3 years ago

@asim266 for YOLOv3 models you can use the ultralytics/yolov3 repo. See https://github.com/ultralytics/yolov3#pytorch-hub

ultralytics / yolov5

Load YOLOv5 from PyTorch Hub ⭐ #36

Before You Start

Load YOLOv5 with PyTorch Hub

Simple Example

Detailed Example

Inference Settings

Device

Silence Outputs

Input Channels

Number of Classes

Force Reload

Screenshot Inference

Multi-GPU Inference

Training

Base64 Results

Cropped Results

Pandas Results

Sorted Results

Box-Cropped Results

JSON Results

Custom Models

TensorRT, ONNX and OpenVINO Models

Environments

Status

Custom Models

I want to know how show the result in OpenCV cv.imshow

Tutorials