1. Did you load the pretrained CNN into cnn_model and print its summary?

✅ Yes
❌ No

Explanation:
Printing the backbone summary ensures the pretrained CNN is correctly loaded and layers are available for feature extraction.

---

2. Did you correctly set feature_layer_name to the layer used for extraction?

✅ Yes
❌ No

Explanation:
Selecting the correct feature layer ensures the hybrid model receives the right feature tensor for transformer processing.

---

3. Did you implement the custom positional-embedding layer inheriting from layers.Layer?

✅ Yes
❌ No

Explanation:
Custom positional embeddings encode spatial ordering so the transformer can understand patch positions.

---

4. Did you define a function to combine the CNN backbone and transformer layers?

✅ Yes
❌ No

Explanation:
A builder function (e.g., build_cnn_vit_hybrid) makes the hybrid-model assembly clean, reproducible, and modular.

---

5. Did you configure ImageDataGenerator / tf.data for training and validation?

❌ No
✅ Yes

Explanation:
Proper data loaders with image size and batch size ensure consistent preprocessing for model training.

---

6. Did you instantiate the hybrid ViT model using the builder function?

❌ No
✅ Yes

Explanation:
Instantiating the hybrid model ensures the CNN + ViT architecture is fully constructed and ready for training.

---

7. Did you compile hybrid_model with optimizer, loss, and metrics?

❌ No
✅ Yes

Explanation:
Compilation is required before training so TensorFlow can compute gradients, losses, and accuracy.

---

8. Did you pass a dummy tensor through the model to verify output shape?

❌ No
✅ Yes

Explanation:
A dummy forward pass catches shape mismatches early, preventing runtime training errors.

---

9. Did you specify hyperparameters and launch fit()?

✅ Yes
❌ No

Explanation:
Training requires epochs, steps per epoch, and callbacks to enable full learning and tracking.

---

10. Did you visualize training vs. validation curves?

✅ Yes
❌ No

Explanation:
Loss and accuracy curves reveal overfitting, convergence behavior, and generalization of the hybrid model.

---

11. Did you complete and download the notebook?

✅ Yes
❌ No

Explanation:
Downloading the completed notebook is required for submission and ensures your progress is saved.

---

🧾 Summary Table

---