Push FastAI model using huggingface_hub.

README.md

@@ -1,66 +1,32 @@
 ---
 tags:
 - fastai
-license: afl-3.0
-datasets:
-- smaciu/bee-wings-large
-- smaciu/bee-wings-small
-metrics:
-- accuracy
-library_name: fastai
-pipeline_tag: image-classification
 ---
 
-Model description in progress...
+# Amazing!
 
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
 
----
-
- ResNet-50 v1.5
-
-ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. 
-
-Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-## Model description
-
-ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
-
-This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
 
-![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
 
-## Intended uses & limitations
-
-You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
-fine-tuned versions on a task that interests you.
-
-### How to use
-
-Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
 
-```python
-from transformers import AutoImageProcessor, ResNetForImageClassification
-import torch
-from datasets import load_dataset
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
 
-dataset = load_dataset("huggingface/cats-image")
-image = dataset["test"]["image"][0]
 
-processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
-model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
-
-inputs = processor(image, return_tensors="pt")
+---
 
-with torch.no_grad():
-    logits = model(**inputs).logits
 
-# model predicts one of the 1000 ImageNet classes
-predicted_label = logits.argmax(-1).item()
-print(model.config.id2label[predicted_label])
-```
+# Model card
 
-For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
+## Model description
+More information needed
 
+## Intended uses & limitations
+More information needed
 
-```
+## Training and evaluation data
+More information needed

model.pkl

@@ -1,3 +1,3 @@
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-oid sha256:3b0b49d47e913a7869ed25d351228888cd49a3285ea2235951c6da949f822fe0
-size 47289049
+oid sha256:c6c5e69b0d07b42ade288a3811a230f1c563e139aa7547f57d7241bed68a34b0
+size 47334545

pyproject.toml

@@ -1,3 +1,3 @@
 [build-system]
-requires = ["setuptools>=40.8.0", "wheel", "python=3.11.0", "fastai=2.7.12", "fastcore=1.5.29"]
+requires = ["setuptools>=40.8.0", "wheel", "python=3.10.9", "fastai=2.7.12", "fastcore=1.5.29"]
 build-backend = "setuptools.build_meta:__legacy__"