Upload ViT-B/32 SEM classification model

README.md

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----
-license: mit
----
+---
+license: apache-2.0
+language: en
+tags:
+- image-classification
+- vision-transformer
+- pytorch
+- sem
+- materials-science
+- nffa-di
+base_model: google/vit-base-patch32-224-in21k
+pipeline_tag: image-classification
+---
+
+# Vision Transformer for SEM Image Classification
+
+This is a fine-tuned **Vision Transformer (ViT-B/32)** model for classifying Scanning Electron Microscopy (SEM) images into 10 distinct categories of nanostructures [1].
+
+This model was developed as part of the **NFFA-DI (Nano Foundries and Fine Analysis Digital Infrastructure)** project, funded by the European Union's NextGenerationEU program.
+
+
+## Model Description 
+
+The model is based on the `google/vit-base-patch32-224-in21k` checkpoint and has been fine-tuned for a 10-class image classification task on SEM images. The 10 categories cover a wide range of nanostructures:
+
+1.  Porous Sponge
+2.  Patterned Surface
+3.  Particles
+4.  Films and Coated Surface
+5.  Powder
+6.  Tips
+7.  Nanowires
+8.  Biological
+9.  MEMS devices and electrodes
+10. Fibres
+
+## How to Use 
+The following Python code shows how to load the model and its processor from the Hub and use it to classify a local SEM image.
+
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load the model and image processor from the Hub
+model_name = "t0m-R/vit-sem-classification" 
+image_processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Load and preprocess the image
+image_path = "path/to/your/sem_image.jpg" 
+try:
+    image = Image.open(image_path).convert("RGB")
+
+    # Prepare the image for the model
+    inputs = image_processor(images=image, return_tensors="pt")
+
+    # Run inference
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        predicted_label_id = logits.argmax(-1).item()
+        predicted_label = model.config.id2label[predicted_label_id]
+
+    print(f"Predicted Label: {predicted_label}")
+
+except FileNotFoundError:
+    print(f"Error: The file at {image_path} was not found.")
+```
+
+## Training Data 
+
+This model was fine-tuned on the SEM Majority dataset, the first annotated set of scanning electron microscopy images for nanoscience.
+
+The dataset consists of 25,537 SEM images manually classified into 10 categories. The classification labels were verified by a group of nanoscientists, and only images validated by the majority of the group were included in the dataset.
+
+The dataset is publicly available at: https://doi.org/10.23728/b2share.e344a8afef08463a855ada08aadbf352
+
+[1] Aversa, Rossella, et al. "The first annotated set of scanning electron microscopy images for nanoscience." Scientific data 5.1 (2018): 1-10.

config.json

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+{
+  "_name_or_path": "google/vit-base-patch32-224-in21k",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Porous_Sponge",
+    "1": "Patterned_surface",
+    "2": "Particles",
+    "3": "Films_Coated_Surface",
+    "4": "Powder",
+    "5": "Tips",
+    "6": "Nanowires",
+    "7": "Biological",
+    "8": "MEMS_devices_and_electrodes",
+    "9": "Fibres"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Porous_Sponge": "0",
+    "Patterned_surface": "1",
+    "Particles": "2",
+    "Films_Coated_Surface": "3",
+    "Powder": "4",
+    "Tips": "5",
+    "Nanowires": "6",
+    "Biological": "7",
+    "MEMS_devices_and_electrodes": "8",
+    "Fibres": "9"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 32,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:37b6f1dfa2a42d99e78e875ef14f3efd8fe8d6f9f7bb72b84c9866cb13cc8125
+size 349874904

preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}