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pretrained object detection model with more classes than COCO

For a project, I need to have a detector that can detect many different object. For that, the 90 classes of COCO are not enough because I would like to be able to see more.
I have seen that imagenet for instance has many more classes, however I couldn't find a model trained to detect imagenet classes.
I am programming on python and I want to avoid retraining a Network to detect more classes myself.
I have looked on pytorch vision and couple of other repositories but I didn't find anything.
Thanks in advance.
EDIT: I have found a good one now, LVIS dataset has 1200 classes for detections and is using the images from coco (they relabelled them). There is good model for it with detectron2 from facebookai. https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md
I think it's only avaliable for a cuda environnement though (I have no GPU :( )

Check https://keras.io/api/applications/ for more models/datasets
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
model = ResNet50(weights='imagenet')

ImageNet unfortunately is not labelled for that purpose.
Off the shelf, you can try and use the Tensorflow Object Detection API. There are models trained on the OpenImages dataset which has 600 classes.
You can use them straight away for inference or if you wish, retrain them.

Tensorflow Object Detection API Untrained Faster-RCNN Model

I am currently trying to build an Object Detector using the the Tensorflow Object Detection API with python. I have managed to retrain the faster-rcnn model by following the instructions posted here and here
However, training time is considerably long as I understand that I am. I understand that I am using transfer learning as opposed to training a faster-rcnn model from scratch. I am wondering if there is anyway to download an untrained faster-rcnn model and train it from scratch (end-to-end) instead of having to recourse to transfer-learning.
I am familiar with the advantages of transfer learning, however, my object detector is aimed at being quickly trainable, narrow in scope, and trained on letters as opposed to objects, so I do not think transfer learning is the best route.
I beleive solving this will have something to do with the pipeline.config file, particulary in this part:
fine_tune_checkpoint: "PATH/TO/PRETRAINED/model.ckpt"
from_detection_checkpoint: true
num_steps: 200000
But I am not sure how to specify that there is no fine_tune_checkpoint

To train your own model from scratch do the following:
Comment out the following lines
# fine_tune_checkpoint: <YOUR PATH>
# from_detection_checkpoint: true
Remove your downloaded pretrained model or rename its path in case you followed the tutorial.
You don't have to download an "empty" model. Instead you can specify your own weight initialization in the config file, e.g., as done here: How to initialize weight for convolution layers in Tensorflow Object Detection API?

Can we Train YOLOv3 on custom objects with no GPU?

My YOLO model works fine for detecting objects such as bottle, person, cellphone, backpack et cetera. But I want to make my model detect a ring or a bracelet or a helmet (objects which are not in the present in the present yolo model). Without GPU can I make a custom object detection yolo model? What are the risks involved? (if any).
My System is Windows 10 Home single language with 8GB RAM.

Re-compile darknet.exe to run on CPU is terribly slow. I've tried before. It's totally unpractical.
Recommend you study Intel OpenVINO toolkit.
https://software.intel.com/en-us/openvino-toolkit
and
https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow.html
OpenVINO toolkit can load and run any frameworks on their CPU/integrated GPU.
You can still use regular NVIDIA cards to train your custom objects by Darknet YOLO.
Then use 3rd-party converter tools (which can be easily found on the GitHub) to convert YOLO weight files you trained to the Tensorflow PB file.
Then use Intel's Model Optimizer to transform the PB file and label file into their so-called "Inference Representation" files (named in *.bin, *.xml, *.labels, and *.mapping files) which later can be loaded and run on Intel's CPU or integrated GPU.
Their Model Optimizer will automatically optimize and remove some unused nodes in YOLO convolutional network file and improve the overall inference speed, which is much faster than simply using re-compiled CPU version of darknet.exe to run YOLO weight on CPU.

Yes you can do that.
Just change the following lines in the Makefile of darknet folder-
GPU=1
CUDNN=1 (for GPU)
change it to -
GPU=0
CUDNN=0 (for CPU)
Yes you can train your YOLO model to detect custom objects too.. Just follow this blog - Link

How to get better results when using the TensorFlow object detection API for a custom trained model to recognize hand poses?

in my project I need to train a object detection model which is able to recognize hands in different poses in real-time from a rgb webcam.
Thus, I'm using the TensorFlow object detection API.
What I did so far is training the model based on the ssd_inception_v2 architecture with the ssd_inception_v2_coco model as finetune-checkpoint.
I want to detect 10 different classes of hand poses. Each class has 300 images which are augmented. In total there are 2400 images in for training and 600 for evaluation. The labeling was done with LabelImg.
The Problem is that the model isn't able to detect the different classes properly. Even if it still wasn't good I got much better results by training with the same images, but only with like 3 different classes. It seems like the problem is the SSD architecture. I've read several times that SSD networks are not good in detecting small objects.
Finally, my questions are the following:
Could I receive better results by using a faster_rcnn_inception architecture for this use case?
Has anyone some advice how to optimize the model?
Do I need more images?
Thanks for your answers!

Image Retrain Inception only check the own specific category not tensor dataset

I have retrained the inception model from my data set of traffic sign.Its working fine but when I am trying to check other image e.g panda it's resulting with the name of traffic sign with some probabilities.I don't understand why its doing it.I need both tensor-flow data-set and my own category too.
My steps:
I have installed the python 3.5.2 in windows 7
I installed tensor-flow with
pip --install tensorflow
I download these two files retrain.py to train my data and label_image.py to check image.
Files downloaded from:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/image_retraining

You have misunderstood the fundamentals of transfer learning wrt this image retraining program.
In the image retraining program you are referencing, you are taking the inception CNN model that is already pretraining on the imageNET dataset. You are then retraining the final classification layers on your NEW classes and data.
The transfer learning occurs because you are retaining all the learnt feature extraction filters etc. in the early layers and you are just reclassifying the activations of those layers to new classes based on your new dataset. This means you are replacing the classification part with a new one. AFAIK there is no way to simply add classes to a CNN model via transfer learning because you have already trained a softmax layer (for example) with the classification distribution for each class.
To achieve what you are suggesting will require you to retrain the final layers of inception with the original dataset PLUS your additional data. This will take a long time due to the size of imageNET.
I would re-evaluate whether you actually need to be able to utilise all these classes in your application or whether it is sufficient to just have your traffic signs etc.
You can learn more about the program at the tensorflow tutorial here.