Update README.md

README.md

@@ -1,3 +1,43 @@
 ---
+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 to outperform the existing EfficientNet b0 to b7 and EfficientNetV2 t to xl models, whether in TensorFlow or PyTorch, in terms of top-1 accuracy, efficiency, and robustness on my datasets and benchmarks.
+
+
+To change the number of classes, replace the linear classification layer. 
+Here's an example to convert the architecture into a training-ready model.
+```bash
+pip install ptflops
+```
+```python
+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)
+
+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 training-ready model
+model = torch.load(model_name)
+```