https://pjreddie.com/darknet/imagenet/
ImageNet Classification
You can use Darknet to classify images for the 1000-class ImageNet challenge. If you haven't installed Darknet yet, you should do that first.
Classifying With Pre-Trained Models
Here are the commands to install Darknet, download a classification weights file, and run a classifier on an image:
git clone https://github.com/pjreddie/darknet.git
cd darknet
make
wget https://pjreddie.com/media/files/darknet19.weights
./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/dog.jpg
This example uses the Darknet19 model, you can read more about it below. After running this command you should see the following output:
layer filters size input output
0 conv 32 3 x 3 / 1 256 x 256 x 3 -> 256 x 256 x 32 0.113 BFLOPs
1 max 2 x 2 / 2 256 x 256 x 32 -> 128 x 128 x 32
2 conv 64 3 x 3 / 1 128 x 128 x 32 -> 128 x 128 x 64 0.604 BFLOPs
3 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64
4 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
5 conv 64 1 x 1 / 1 64 x 64 x 128 -> 64 x 64 x 64 0.067 BFLOPs
6 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
7 max 2 x 2 / 2 64 x 64 x 128 -> 32 x 32 x 128
8 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
9 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
10 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
11 max 2 x 2 / 2 32 x 32 x 256 -> 16 x 16 x 256
12 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
13 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
14 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
15 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
16 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
17 max 2 x 2 / 2 16 x 16 x 512 -> 8 x 8 x 512
18 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
19 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
20 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
21 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
22 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
23 conv 1000 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x1000 0.131 BFLOPs
24 avg 8 x 8 x1000 -> 1000
25 softmax 1000
Loading weights from darknet19.weights...Done!
data/dog.jpg: Predicted in 0.769246 seconds.
42.55%: malamute
22.93%: Eskimo dog
12.51%: Siberian husky
2.76%: bicycle-built-for-two
1.20%: mountain bike
Darknet displays information as it loads the config file and weights, then it classifies the image and prints the top-10 classes for the image. Kelp is a mixed breed dog but she has a lot of malamute in her so we'll consider this a success!
You can also try with other images, like the bald eagle image:
./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/eagle.jpg
Which produces:
...
data/eagle.jpg: Predicted in 0.707070 seconds.
84.68%: bald eagle
11.91%: kite
2.62%: vulture
0.08%: great grey owl
0.07%: hen
Pretty good!
If you don't specify an image file you will be prompted at run-time for an image. This way you can classify multiple in a row without reloading the whole model. Use the command:
./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights
Then you will get a prompt that looks like:
....
25: Softmax Layer: 1000 inputs
Loading weights from darknet19.weights...Done!
Enter Image Path:
Whenever you get bored of classifying images you can use Ctrl-C to exit the program.
Validating On ImageNet
You see these validation set numbers thrown around everywhere. Maybe you want to double check for yourself how well these models actually work. Let's do it!
First you need to download the validation images, and the cls-loc annotations. You can get them here but you'll have to make an account! Once you download everything you should have a directory with ILSVRC2012_bbox_val_v3.tgz, and ILSVRC2012_img_val.tar. First we unpack them:
tar -xzf ILSVRC2012_bbox_val_v3.tgz
mkdir -p imgs && tar xf ILSVRC2012_img_val.tar -C imgs
Now we have the images and the annotations but we need to label the images so Darknet can evaluate its predictions. We do that using this bash script. It's already in your scripts/ subdirectory. We can just get it again though and run it:
wget https://pjreddie.com/media/files/imagenet_label.sh
bash imagenet_label.sh
This will generate two things: a directory called labelled/ which contains renamed symbolic links to the images, and a file called inet.val.list which contains a list of the paths of the labelled images. We need to move this file to the data/ subdirectory in Darknet:
mv inet.val.list <path-to>/darknet/data
Now you are finally ready to validate your model! First re-make Darknet. Then run the validation routine like so:
./darknet classifier valid cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights
Note: if you don't compile Darknet with OpenCV then you won't be able to load all of the ImageNet images since some of them are weird formats not supported by stb_image.h.
If you don't compile with CUDA you can still validate on ImageNet but it will take like a reallllllly long time. Not recommended.
Pre-Trained Models
Here are a variety of pre-trained models for ImageNet classification. Accuracy is measured as single-crop validation accuracy on ImageNet. GPU timing is measured on a Titan X, CPU timing on an Intel i7-4790K (4 GHz) run on a single core. Using multi-threading with OPENMP should scale linearly with # of CPUs.
| Model | Top-1 | Top-5 | Ops | GPU | CPU | Cfg | Weights |
|---|---|---|---|---|---|---|---|
| AlexNet | 57.0 | 80.3 | 2.27 Bn | 3.1 ms | 0.29 s | cfg | 238 MB |
| Darknet Reference | 61.1 | 83.0 | 0.96 Bn | 2.9 ms | 0.14 s | cfg | 28 MB |
| VGG-16 | 70.5 | 90.0 | 30.94 Bn | 9.4 ms | 4.36 s | cfg | 528 MB |
| Extraction | 72.5 | 90.8 | 8.52 Bn | 4.8 ms | 0.97 s | cfg | 90 MB |
| Darknet19 | 72.9 | 91.2 | 7.29 Bn | 6.2 ms | 0.87 s | cfg | 80 MB |
| Darknet19 448x448 | 76.4 | 93.5 | 22.33 Bn | 11.0 ms | 2.96 s | cfg | 80 MB |
| Resnet 18 | 70.7 | 89.9 | 4.69 Bn | 4.6 ms | 0.57 s | cfg | 44 MB |
| Resnet 34 | 72.4 | 91.1 | 9.52 Bn | 7.1 ms | 1.11 s | cfg | 83 MB |
| Resnet 50 | 75.8 | 92.9 | 9.74 Bn | 11.4 ms | 1.13 s | cfg | 87 MB |
| Resnet 101 | 77.1 | 93.7 | 19.70 Bn | 20.0 ms | 2.23 s | cfg | 160 MB |
| Resnet 152 | 77.6 | 93.8 | 29.39 Bn | 28.6 ms | 3.31 s | cfg | 220 MB |
| ResNeXt 50 | 77.8 | 94.2 | 10.11 Bn | 24.2 ms | 1.20 s | cfg | 220 MB |
| ResNeXt 101 (32x4d) | 77.7 | 94.1 | 18.92 Bn | 58.7 ms | 2.24 s | cfg | 159 MB |
| ResNeXt 152 (32x4d) | 77.6 | 94.1 | 28.20 Bn | 73.8 ms | 3.31 s | cfg | 217 MB |
| Densenet 201 | 77.0 | 93.7 | 10.85 Bn | 32.6 ms | 1.38 s | cfg | 66 MB |
| Darknet53 | 77.2 | 93.8 | 18.57 Bn | 13.7 ms | 2.11 s | cfg | 159 MB |
| Darknet53 448x448 | 78.5 | 94.7 | 56.87 Bn | 26.3 ms | 7.21 s | cfg | 159 MB |
AlexNet
The model that started a revolution! The original model was crazy with the split GPU thing so this is the model from some follow-up work.
- Top-1 Accuracy: 57.0%
- Top-5 Accuracy: 80.3%
- Forward Timing: 3.1 ms/img
- CPU Forward Timing: 0.29 s/img
- cfg file
- weight file (238 MB)
Darknet Reference Model
This model is designed to be small but powerful. It attains the same top-1 and top-5 performance as AlexNet but with 1/10th the parameters. It uses mostly convolutional layers without the large fully connected layers at the end. It is about twice as fast as AlexNet on CPU making it more suitable for some vision applications.
- Top-1 Accuracy: 61.1%
- Top-5 Accuracy: 83.0%
- Forward Timing: 2.9 ms/img
- CPU Forward Timing: 0.14 s/img
- cfg file
- weight file (28 MB)
VGG-16
The Visual Geometry Group at Oxford developed the VGG-16 model for the ILSVRC-2014 competition. It is highly accurate and widely used for classification and detection. I adapted this version from the Caffe pre-trained model. It was trained for an additional 6 epochs to adjust to Darknet-specific image preprocessing (instead of mean subtraction Darknet adjusts images to fall between -1 and 1).
- Top-1 Accuracy: 70.5%
- Top-5 Accuracy: 90.0%
- Forward Timing: 9.4 ms/img
- CPU Forward Timing: 4.36 s/img
- cfg file
- weight file (528 MB)
Extraction
I developed this model as an offshoot of the GoogleNet model. It doesn't use the "inception" modules, only 1x1 and 3x3 convolutional layers.
- Top-1 Accuracy: 72.5%
- Top-5 Accuracy: 90.8%
- Forward Timing: 4.8 ms/img
- CPU Forward Timing: 0.97 s/img
- cfg file
- weight file (90 MB)
Darknet19
I modified the Extraction network to be faster and more accurate. This network was sort of a merging of ideas from the Darknet Reference network and Extraction as well as numerous publications like Network In Network, Inception, and Batch Normalization.
- Top-1 Accuracy: 72.9%
- Top-5 Accuracy: 91.2%
- Forward Timing: 6.2 ms/img
- CPU Forward Timing: 0.87 s/img
- cfg file
- weight file (80 MB)
Darknet19 448x448
I trained Darknet19 for 10 more epochs with a larger input image size, 448x448. This model performs significantly better but is slower since the whole image is larger.
- Top-1 Accuracy: 76.4%
- Top-5 Accuracy: 93.5%
- Forward Timing: 11.0 ms/img
- CPU Forward Timing: 2.96 s/img
- cfg file
- weight file (80 MB)
Resnet 50
For some reason people love these networks even though they are so sloooooow. Whatever. Paper
- Top-1 Accuracy: 75.8%
- Top-5 Accuracy: 92.9%
- Forward Timing: 11.4 ms/img
- CPU Forward Timing: 1.13 s/img
- cfg file
- weight file (87 MB)
Resnet 152
For some reason people love these networks even though they are so sloooooow. Whatever. Paper
- Top-1 Accuracy: 77.6%
- Top-5 Accuracy: 93.8%
- Forward Timing: 28.6 ms/img
- CPU Forward Timing: 3.31 s/img
- cfg file
- weight file (220 MB)
Densenet 201
I love DenseNets! They are just so deep and so crazy and work so well. Like Resnet, still slow since they are sooooo many layers but at least they work really well! Paper
- Top-1 Accuracy: 77.0%
- Top-5 Accuracy: 93.7%
- Forward Timing: 32.6 ms/img
- CPU Forward Timing: 1.38 s/img
- cfg file
- weight file (67 MB)
ImageNet Classification
You can use Darknet to classify images for the 1000-class ImageNet challenge. If you haven't installed Darknet yet, you should do that first.
Classifying With Pre-Trained Models
Here are the commands to install Darknet, download a classification weights file, and run a classifier on an image:
git clone https://github.com/pjreddie/darknet.git
cd darknet
make
wget https://pjreddie.com/media/files/darknet19.weights
./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/dog.jpg
This example uses the Darknet19 model, you can read more about it below. After running this command you should see the following output:
layer filters size input output
0 conv 32 3 x 3 / 1 256 x 256 x 3 -> 256 x 256 x 32 0.113 BFLOPs
1 max 2 x 2 / 2 256 x 256 x 32 -> 128 x 128 x 32
2 conv 64 3 x 3 / 1 128 x 128 x 32 -> 128 x 128 x 64 0.604 BFLOPs
3 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64
4 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
5 conv 64 1 x 1 / 1 64 x 64 x 128 -> 64 x 64 x 64 0.067 BFLOPs
6 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
7 max 2 x 2 / 2 64 x 64 x 128 -> 32 x 32 x 128
8 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
9 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
10 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
11 max 2 x 2 / 2 32 x 32 x 256 -> 16 x 16 x 256
12 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
13 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
14 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
15 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
16 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
17 max 2 x 2 / 2 16 x 16 x 512 -> 8 x 8 x 512
18 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
19 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
20 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
21 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
22 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
23 conv 1000 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x1000 0.131 BFLOPs
24 avg 8 x 8 x1000 -> 1000
25 softmax 1000
Loading weights from darknet19.weights...Done!
data/dog.jpg: Predicted in 0.769246 seconds.
42.55%: malamute
22.93%: Eskimo dog
12.51%: Siberian husky
2.76%: bicycle-built-for-two
1.20%: mountain bike
Darknet displays information as it loads the config file and weights, then it classifies the image and prints the top-10 classes for the image. Kelp is a mixed breed dog but she has a lot of malamute in her so we'll consider this a success!
You can also try with other images, like the bald eagle image:
./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/eagle.jpg
Which produces:
...
data/eagle.jpg: Predicted in 0.707070 seconds.
84.68%: bald eagle
11.91%: kite
2.62%: vulture
0.08%: great grey owl
0.07%: hen
Pretty good!
If you don't specify an image file you will be prompted at run-time for an image. This way you can classify multiple in a row without reloading the whole model. Use the command:
./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights
Then you will get a prompt that looks like:
....
25: Softmax Layer: 1000 inputs
Loading weights from darknet19.weights...Done!
Enter Image Path:
Whenever you get bored of classifying images you can use Ctrl-C to exit the program.
Validating On ImageNet
You see these validation set numbers thrown around everywhere. Maybe you want to double check for yourself how well these models actually work. Let's do it!
First you need to download the validation images, and the cls-loc annotations. You can get them here but you'll have to make an account! Once you download everything you should have a directory with ILSVRC2012_bbox_val_v3.tgz, and ILSVRC2012_img_val.tar. First we unpack them:
tar -xzf ILSVRC2012_bbox_val_v3.tgz
mkdir -p imgs && tar xf ILSVRC2012_img_val.tar -C imgs
Now we have the images and the annotations but we need to label the images so Darknet can evaluate its predictions. We do that using this bash script. It's already in your scripts/ subdirectory. We can just get it again though and run it:
wget https://pjreddie.com/media/files/imagenet_label.sh
bash imagenet_label.sh
This will generate two things: a directory called labelled/ which contains renamed symbolic links to the images, and a file called inet.val.list which contains a list of the paths of the labelled images. We need to move this file to the data/ subdirectory in Darknet:
mv inet.val.list <path-to>/darknet/data
Now you are finally ready to validate your model! First re-make Darknet. Then run the validation routine like so:
./darknet classifier valid cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights
Note: if you don't compile Darknet with OpenCV then you won't be able to load all of the ImageNet images since some of them are weird formats not supported by stb_image.h.
If you don't compile with CUDA you can still validate on ImageNet but it will take like a reallllllly long time. Not recommended.
Pre-Trained Models
Here are a variety of pre-trained models for ImageNet classification. Accuracy is measured as single-crop validation accuracy on ImageNet. GPU timing is measured on a Titan X, CPU timing on an Intel i7-4790K (4 GHz) run on a single core. Using multi-threading with OPENMP should scale linearly with # of CPUs.
| Model | Top-1 | Top-5 | Ops | GPU | CPU | Cfg | Weights |
|---|---|---|---|---|---|---|---|
| AlexNet | 57.0 | 80.3 | 2.27 Bn | 3.1 ms | 0.29 s | cfg | 238 MB |
| Darknet Reference | 61.1 | 83.0 | 0.96 Bn | 2.9 ms | 0.14 s | cfg | 28 MB |
| VGG-16 | 70.5 | 90.0 | 30.94 Bn | 9.4 ms | 4.36 s | cfg | 528 MB |
| Extraction | 72.5 | 90.8 | 8.52 Bn | 4.8 ms | 0.97 s | cfg | 90 MB |
| Darknet19 | 72.9 | 91.2 | 7.29 Bn | 6.2 ms | 0.87 s | cfg | 80 MB |
| Darknet19 448x448 | 76.4 | 93.5 | 22.33 Bn | 11.0 ms | 2.96 s | cfg | 80 MB |
| Resnet 18 | 70.7 | 89.9 | 4.69 Bn | 4.6 ms | 0.57 s | cfg | 44 MB |
| Resnet 34 | 72.4 | 91.1 | 9.52 Bn | 7.1 ms | 1.11 s | cfg | 83 MB |
| Resnet 50 | 75.8 | 92.9 | 9.74 Bn | 11.4 ms | 1.13 s | cfg | 87 MB |
| Resnet 101 | 77.1 | 93.7 | 19.70 Bn | 20.0 ms | 2.23 s | cfg | 160 MB |
| Resnet 152 | 77.6 | 93.8 | 29.39 Bn | 28.6 ms | 3.31 s | cfg | 220 MB |
| ResNeXt 50 | 77.8 | 94.2 | 10.11 Bn | 24.2 ms | 1.20 s | cfg | 220 MB |
| ResNeXt 101 (32x4d) | 77.7 | 94.1 | 18.92 Bn | 58.7 ms | 2.24 s | cfg | 159 MB |
| ResNeXt 152 (32x4d) | 77.6 | 94.1 | 28.20 Bn | 73.8 ms | 3.31 s | cfg | 217 MB |
| Densenet 201 | 77.0 | 93.7 | 10.85 Bn | 32.6 ms | 1.38 s | cfg | 66 MB |
| Darknet53 | 77.2 | 93.8 | 18.57 Bn | 13.7 ms | 2.11 s | cfg | 159 MB |
| Darknet53 448x448 | 78.5 | 94.7 | 56.87 Bn | 26.3 ms | 7.21 s | cfg | 159 MB |
AlexNet
The model that started a revolution! The original model was crazy with the split GPU thing so this is the model from some follow-up work.
- Top-1 Accuracy: 57.0%
- Top-5 Accuracy: 80.3%
- Forward Timing: 3.1 ms/img
- CPU Forward Timing: 0.29 s/img
- cfg file
- weight file (238 MB)
Darknet Reference Model
This model is designed to be small but powerful. It attains the same top-1 and top-5 performance as AlexNet but with 1/10th the parameters. It uses mostly convolutional layers without the large fully connected layers at the end. It is about twice as fast as AlexNet on CPU making it more suitable for some vision applications.
- Top-1 Accuracy: 61.1%
- Top-5 Accuracy: 83.0%
- Forward Timing: 2.9 ms/img
- CPU Forward Timing: 0.14 s/img
- cfg file
- weight file (28 MB)
VGG-16
The Visual Geometry Group at Oxford developed the VGG-16 model for the ILSVRC-2014 competition. It is highly accurate and widely used for classification and detection. I adapted this version from the Caffe pre-trained model. It was trained for an additional 6 epochs to adjust to Darknet-specific image preprocessing (instead of mean subtraction Darknet adjusts images to fall between -1 and 1).
- Top-1 Accuracy: 70.5%
- Top-5 Accuracy: 90.0%
- Forward Timing: 9.4 ms/img
- CPU Forward Timing: 4.36 s/img
- cfg file
- weight file (528 MB)
Extraction
I developed this model as an offshoot of the GoogleNet model. It doesn't use the "inception" modules, only 1x1 and 3x3 convolutional layers.
- Top-1 Accuracy: 72.5%
- Top-5 Accuracy: 90.8%
- Forward Timing: 4.8 ms/img
- CPU Forward Timing: 0.97 s/img
- cfg file
- weight file (90 MB)
Darknet19
I modified the Extraction network to be faster and more accurate. This network was sort of a merging of ideas from the Darknet Reference network and Extraction as well as numerous publications like Network In Network, Inception, and Batch Normalization.
- Top-1 Accuracy: 72.9%
- Top-5 Accuracy: 91.2%
- Forward Timing: 6.2 ms/img
- CPU Forward Timing: 0.87 s/img
- cfg file
- weight file (80 MB)
Darknet19 448x448
I trained Darknet19 for 10 more epochs with a larger input image size, 448x448. This model performs significantly better but is slower since the whole image is larger.
- Top-1 Accuracy: 76.4%
- Top-5 Accuracy: 93.5%
- Forward Timing: 11.0 ms/img
- CPU Forward Timing: 2.96 s/img
- cfg file
- weight file (80 MB)
Resnet 50
For some reason people love these networks even though they are so sloooooow. Whatever. Paper
- Top-1 Accuracy: 75.8%
- Top-5 Accuracy: 92.9%
- Forward Timing: 11.4 ms/img
- CPU Forward Timing: 1.13 s/img
- cfg file
- weight file (87 MB)
Resnet 152
For some reason people love these networks even though they are so sloooooow. Whatever. Paper
- Top-1 Accuracy: 77.6%
- Top-5 Accuracy: 93.8%
- Forward Timing: 28.6 ms/img
- CPU Forward Timing: 3.31 s/img
- cfg file
- weight file (220 MB)
Densenet 201
I love DenseNets! They are just so deep and so crazy and work so well. Like Resnet, still slow since they are sooooo many layers but at least they work really well! Paper
- Top-1 Accuracy: 77.0%
- Top-5 Accuracy: 93.7%
- Forward Timing: 32.6 ms/img
- CPU Forward Timing: 1.38 s/img
- cfg file
- weight file (67 MB)