pipeline_tag: image-classification
tags:
- arxiv:2010.07611
- arxiv:2104.00298
license: cc-by-nc-4.0
To be clear, this model is tailored to my image and video classification tasks, not to imagenet. I built EfficientNetV2.5 s to outperform the existing EfficientNet b0 to b4, EfficientNet b1 to b4 pruned (I pruned b4), and EfficientNetV2 t to l models, whether trained using TensorFlow or PyTorch, in terms of top-1 accuracy, efficiency, and robustness on my dataset and CMAD benchmark.
Model Details
- Model tasks: Image classification / video classification / feature backbone
- Model stats:
- Params: 16.64 M
- Multiply-Add Operations: 5.32 G
- Image size: train = 299x299 / 304x304, test = 304x304
- Classification layer: defaults to 1,000 classes
- Papers:
- EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298
- Layer-adaptive sparsity for the Magnitude-based Pruning: https://arxiv.org/abs/2010.07611
- Dataset: ImageNet-1k
- Pretrained: Yes, but requires more pretraining
- Original: This model architecture is original
Load PyTorch Jit Model with 1000 Classes
from transformers import AutoModel
model = AutoModel.from_pretrained("FredZhang7/efficientnetv2.5_rw_s", trust_remote_code=True)
Load Model with Custom Classes
To change the number of classes, replace the linear classification layer. Here's an example of how to convert the architecture into a trainable model.
pip install ptflops timm
from ptflops import get_model_complexity_info
import torch
import urllib.request
nclass = 3 # number of classes in your dataset
input_size = (3, 304, 304) # recommended image input size
print_layer_stats = True # prints the statistics for each layer of the model
verbose = True # prints additional info about the MAC calculation
# Download the model. Skip this step if already downloaded
base_model = "efficientnetv2.5_base_in1k"
url = f"https://huggingface.co/FredZhang7/efficientnetv2.5_rw_s/resolve/main/{base_model}.pth"
file_name = f"./{base_model}.pth"
urllib.request.urlretrieve(url, file_name)
shape = (2,) + input_size
example_inputs = torch.randn(shape)
example_inputs = (example_inputs - example_inputs.min()) / (example_inputs.max() - example_inputs.min())
model = torch.load(file_name)
model.classifier = torch.nn.Linear(in_features=1984, out_features=nclass, bias=True)
macs, nparams = get_model_complexity_info(model, input_size, as_strings=False, print_per_layer_stat=print_layer_stats, verbose=verbose)
traced_model = torch.jit.trace(model, example_inputs)
model_name = f'{base_model}_{"{:.2f}".format(nparams / 1e6)}M_{"{:.2f}".format(macs / 1e9)}G.pth'
traced_model.save(model_name)
# Load the trainable model
model = torch.load(model_name)
Top-1 Accuracy Comparisons
I finetuned the existing models on either 299x299, 304x304, 320x320, or 384x384 resolution, depending on the input size used during pretraining and the VRAM usage.
efficientnet_b3_pruned achieved the second highest top-1 accuracy as well as the highest epoch-1 training accuracy on my task, out of EfficientNetV2.5 small and all existing EfficientNet models my 24 GB VRAM RTX 3090 could handle.
I will publish the detailed report in this model repository. This repository is only for the base model, pretrained a bit on ImageNet, not my task.
Carbon Emissions
Comparing all models and testing my new architectures costed roughly 648 GPU hours, over a span of 35 days.